Agent Studio

Portable multi-agent ecosystem for Claude Code.

Agent Studio packages agents, skills, rules, hooks, schemas, and validation tooling into a single repo that can run directly or be dropped into another project.

If you want a local-first, reproducible agent stack with strict validation and hybrid code search, this is it.

Quick Links

Getting Started · .claude/docs/GETTING_STARTED.md
Architecture · .claude/docs/ARCHITECTURE.md
Developer Workflow · .claude/docs/DEVELOPER_WORKFLOW.md
Hooks Reference · .claude/docs/HOOKS_REFERENCE.md
Memory System · .claude/docs/MEMORY_SYSTEM.md
Code Indexing · .claude/docs/CODE_INDEXING_DESIGN.md
Telegram Integration · .claude/docs/TELEGRAM_ARCHITECTURE.md

Telegram Integration

Agent Studio includes a background channel daemon that monitors Telegram and responds to messages using Claude. Inspired by clawhip and Claude Code's KAIROS assistant mode.

Quick Start

# 1. Configure (one-time)
# Add to your .env:
TELEGRAM_BOT_TOKEN=<token from @BotFather>
TELEGRAM_OWNER_ID=<your user ID from @userinfobot>
TELEGRAM_ALLOWED_USERS=<your user ID>
CHANNEL_AUTO_START=true

# 2. Verify config
/setup-telegram

# 3. Start monitoring
/enable-telegram

# 4. Stop monitoring
/disable-telegram

# 5. Restart daemon (without killing Claude session)
/restart-telegram

Features

• Zero-cost idle — long-polls Telegram, only calls Claude when a message arrives
• 3-tier memory — chat history, session summaries, persistent user profiles
• Dream consolidation — KAIROS-style 4-phase memory synthesis (Orient → Gather → Consolidate → Prune)
• Context rot protection — auto-detects and rotates sessions transparently
• Task execution — ask the bot to run code, check git, run tests via headless Claude
• 25 bot commands — /help, /status, /memory, /dream, /tasks, /code, /usage, /insights, /personality, /schedule, /export, /pair, and more
• /code mission-aware coding — routes coding tasks through skill classification (16 agent types), builds feature specs, injects TDD workflow, grades results 0-100 against alignment rules
• Skill extraction — learns from completed tasks, auto-injects matching patterns into future prompts
• Ralph loops — iterative verify/fix execution ([RALPH] tag), max 5 iterations
• Ultrawork parallel — splits tasks into concurrent subtasks ([ULTRAWORK] tag)
• Multi-model routing — haiku/sonnet/opus selected automatically by message complexity
• "While you were away" recap — summarizes what happened when you return after being idle
• HTTP API — http://127.0.0.1:3101/status, /send, /history, /memory, /dream
• A2A ready — router can send messages and delegate tasks via the daemon's HTTP API
• Webhook source — POST /webhook endpoint for GitHub, CI, and external event ingestion
• Proactive mode — KAIROS tick engine with 15s heartbeat for scheduled messages and task progress streaming
• Multi-platform ready — Discord, Slack, and Web widget sources planned (platform-agnostic core)

Voice Pipeline (Optional)

# Add TTS keys to .env:
ELEVENLABS_API_KEY=<key>      # or OPENAI_API_KEY for fallback
# Verify: /setup-telegram-voice
# Enable: /enable-telegram-voice

Full docs: .claude/docs/TELEGRAM_ARCHITECTURE.md

Memory Marketplace Platform (v3.2.0)

Agent Studio v3.2.0 ships two tightly integrated capabilities: structured memory provenance and verified skill distribution.

CAT7 Memory extends the STM/MTM/LTM memory tiers with a 7-field schema that records concept, attributes, temporality, provenance, confidence, lineage, and embedding_refs on every record. The cat7-writer.cjs routes records automatically to the correct tier based on temporality. The MMP CLI (pnpm mmp:lineage, pnpm mmp:descendants) lets you walk and inspect the full derivation graph of any memory record, so agents can audit where a belief came from and which downstream records it influenced.

Skill Marketplace provides a verified distribution channel for skill packages. Packages are signed with HMAC-SHA256 and scored on a 4-tier trust ladder before installation. Path-traversal guards and a minimum-key-length policy prevent supply-chain abuse. Install a package with pnpm skill:install <package> — the installer verifies the signature, checks the trust score against SKILL_MARKETPLACE_MIN_TRUST, and unpacks only to the allowed skills directory.

Observability & Cost Control (v2.4.0)

Agent Studio v2.4.0 is the "production-grade" release. It addresses the two most-reported community pain points: opaque agent execution and unpredictable API spend.

Structured Tracing

Every agent spawn, skill invocation, and tool call now emits a structured OpenTelemetry GenAI event with parent_span_id and span_type. You can reconstruct the full call tree for any session.

# Inspect per-component token burn for a session
pnpm session:audit <session-id>

Output: a colored table showing token consumption broken down by agent, skill, and tool — no external observability service required.

Cost Ceilings

Spend-guard auto-downgrade switches agents from sonnet to haiku when session cost approaches the configured ceiling:

# Set per-session spend ceiling (default: $5)
SPEND_GUARD_CEILING_USD=5

# Disable entirely
SPEND_GUARD=off

Context Budget Pre-flight

Before any agent spawn, the budget hook checks projected context size and warns before the session reaches the compression threshold:

# Warning threshold in tokens (default: 50000)
SPAWN_BUDGET_DEFAULT_CONTEXT=50000

# Hard-block spawns that exceed 1.6x threshold
SPAWN_BUDGET_HARD=on

New Environment Variables (v2.4.0)

Variable	Default	Purpose
SPAWN_BUDGET_DEFAULT_CONTEXT	50000	Token threshold for spawn pre-flight warning
SPAWN_BUDGET_HARD	off	Set on to hard-block over-budget spawns
SPEND_GUARD_CEILING_USD	5	Per-session cost ceiling before haiku downgrade
SPEND_GUARD	on	Set off to disable spend-guard entirely

See CHANGELOG.md and .claude/docs/HOOKS_REFERENCE.md for full details.

Migrating from 2.x to 3.0

v3.0.0 introduces four breaking changes. The migration script handles most of them automatically.

Copy-Paste Checklist

# 1. Pull latest and install
git pull && pnpm install

# 2. Preview changes (no files written)
pnpm migrate:2x-to-3 --dry-run

# 3. Apply changes (backfills agent manifests, flags SSE transport)
pnpm migrate:2x-to-3

# 4. Review backups created for modified agents
#    .claude/context/tmp/agents-pre-v3-migration/

# 5. Update any mcp.transport: "sse" entries in config to "streamable-http"
#    (BC-1 — the script flags locations but does not rewrite config files)

# 6. Regenerate agent registry in v3 schema format
pnpm agents:registry

# 7. Enable enforcement when ready (optional — off by default)
#    Set V3_MANIFEST_REQUIRED=on in .env

# 8. Verify
pnpm test:framework

Breaking Changes Summary

#	Change	Fix
BC-1	mcp.transport: "sse" rejected	Update to "streamable-http" in config
BC-2	Agents without manifest: block fail startup	Run pnpm migrate:2x-to-3
BC-3	Task() spawns require AIP token	Router auto-injects; set AIP_TOKENS=off for dev
BC-4	agent-registry.json v2 not auto-loaded	Run pnpm agents:registry

Full guide: docs/migration/v2-to-v3.md

Recent Changes

Release-Readiness Hardening

• Flight recorder stabilized: Removed a syntax regression, stopped redundant rotation probes while writes are buffered, and added targeted coverage for the missing-file debounce path
• Routing tables corrected: Legacy debug-agent intent aliases now resolve to advanced-debugging, and the overlap-prone fallback keywords were trimmed so routing validation stays green
• Timing-sensitive suites de-flaked: Phase 1A cost tracking and minimal profiler tests now use warmup/margin-aware thresholds instead of brittle 10 ms boundaries
• Full suite re-verified: pnpm test completed cleanly at 3,063 top-level tests with 12,528 passing assertions and 0 failures

Prompt Cache Optimization (Zylos-inspired)

• Envelope fingerprint: Stable hash across spawns of same agent type (excludes per-spawn basePrompt). Enables cache hits for tools/skills/safety prefix.
• Memory query batch cache: 60s file-based cache prevents redundant LanceDB/SQLite queries on burst spawns
• Configurable memory cap: MEMORY_INJECTION_MAX_CHARS env var (default 3600, raise to 8000+)

Phase 10 — Paper-Inspired: Dual-Level Indexing + Memory Versioning

• Dual-level skill+agent index: 339 skill + 124 agent prototypes in shared vector space. Retrieve N=50, collapse to K=5 unique agents via skill-to-agent owner trace (+19.4% recall, arXiv:2511.01854)
• Memory version links: supersedes + archived fields on pattern/gotcha entries. Semantic matches create version chains instead of silent drops (arXiv:2603.19595)

Phase 9 — Routing Recalibration

• Semantic-first routing: Embedding-based routing promoted to primary (ROUTING_PRIORITY=semantic); keyword classification demoted to metadata/tiebreaker
• Hierarchical routing ON by default: 124 agents grouped into 9 domain sub-routers (HIERARCHICAL_ROUTING=on)
• Model router wired: Dynamic haiku/sonnet/opus selection based on complexity + budget (MODEL_ROUTER_ENABLED=on)
• Intent feedback loop closed: Success/failure recorded per intent, read back into routing weights
• Guard overhead reduced: 2 redundant checks removed, 5 advisory hooks converted to async

Post-Phase 8 — Audit Fixes

• Consolidation wiring: Connected Dream-equivalent pipeline (shouldConsolidate → acquireLock → consolidate → mtime stamp) in session-end hook — modules were built but never called
• Flat-file rotation fix: parseSections() line-based fallback prevents 572KB bloat recurrence
• 6 unreachable agents routed: Added 22 flat routing keywords for product/business agents
• Cleanup: Deleted 2 duplicate hooks, 1 orphaned workflow, fixed stale agent count

Phase 8 — Memory Consolidation (Dream-inspired)

• Index discipline: 25KB/200-line dual caps on markdown memory files, [PERMANENT] section preservation, automatic archival with warning lines, memory health reporting (16 tests)
• Daily log + consolidation: Append-only timestamped daily logs at logs/YYYY/MM/YYYY-MM-DD.md, 4-phase Dream-inspired consolidation (Orient/Gather/Consolidate/Prune), heuristic keyword extraction, idempotent processing with manifest tracking, session-end hook integration (48 tests)
• Mtime lock + session trigger: CC-style mtime-as-timestamp lock file, PID-based holder tracking with 60min stale detection, 24h time gate + 5-session count gate + 10min scan throttle, rollback on failure (28 tests)
• Cross-area integration: Full cycle (log/trigger/consolidate/prune), lock contention, failure rollback, MemoryRecord coexistence, STM/MTM/LTM backward compatibility, first-run scenario (13 tests)
• Source: 4 patterns adopted from Claude Code's Dream memory consolidation system

Phase 7 — Security & Advanced Integration

• Security hardening: Case-normalized path comparison for case-insensitive filesystems, UNC path blocking (NTLM leak prevention), URL-encoded/backslash traversal detection, CC dangerous-pattern alignment with word-boundary matching, compound command analysis (single &, $(...), backtick substitutions) (76 tests)
• Hook enhancements: updatedInput for bash safety prefixes (set -euo pipefail injection on unsafe multi-line scripts), suppressOutput on security blocks to prevent context inflation, denial-based routing feedback with agent suggestions after repeated tool denials (10 tests)
• Agent enhancements: disallowedTools field (excludes tools from prompt assembly with conflict resolution), mcpServers scoping (limits MCP visibility per agent), fork_eligible boolean field in agent schema (29 tests)
• Cross-area integration: Case-normalized blocking + cache stability, denial tracking to routing feedback end-to-end, suppressOutput context inflation prevention, agent schema round-trip (14 tests)

Phase 6 — Prompt Cache & Context Intelligence

• Prompt cache optimization: Alphabetical tool/skill sorting, section memoization, duplicate contract elimination, cache-break-detector with SHA-256 hashing (45 tests)
• Context management: Pre-compact activeFiles persistence, threshold alignment with CC auto-compact at 93.5%, microcompact detection, circuit breaker (23 tests)
• Cross-area integration: Cache stability, tool change isolation, combined monitor output, prefix hash stability (10 tests)

Phase 5 — Foundation & Performance

• Rules compression: CLAUDE.md + rules 66,374 to 36,679 chars (-45%), file merges, individual compression, frontmatter scoping (70 tests)
• Hook overhaul: 25 hooks to async, 63 timeout_ms additions, 3 deduplications, 2 consolidation bundles, 5 startup sentinels (142 tests)
• New hook events: SubagentStart (Iron Law validator), PermissionDenied (denial logger), SessionStart (watchPaths) (193 tests with cross-area)

Phase 4 — Hermes Assimilation (Competitive Parity)

• Intelligent runtime: Cost pricing table with real $/MTok rates and auto-downgrade (opus->sonnet->haiku), flight recorder redaction with SENSITIVE_KEYS, mixture-of-agents consensus tool (96 tests)
• Autonomous skills: Skill auto-creator from session transcripts with Jaccard similarity and security scanning, SQLite FTS5 full-text search over session JSONL logs (79 tests)
• Execution infrastructure: Process registry with spawn/stop/checkpoint/restore and stdout RingBuffer, plugin tool registration with manifest schema extension (55 tests)
• Cross-area integration: Budget-aware background tasks, redacted log secrets, skill creation with plugin tool refs, cost-to-budget flow (20 tests)
• Source: 8 high-value features assimilated from nousresearch/hermes-agent (~20K LOC analyzed)

Phase 3 — Self-Evolving Skills, GitHub Integration, Nomenclature & Production Audit

• Self-evolving skills: Usage tracking, pattern detection, suggestion generation, evolution triggers (91 tests)
• GitHub integration: CLI client wrapping gh, webhook simulator, mention parser, task dispatcher, CI status reporter (152 tests)
• Nomenclature cleanup: droid → agent, .factory-plugin → .claude-plugin across plugin system
• Production audit: Fixed all missing hooks (62 verified), replaced stub scripts, wired 32 unreachable agents to flat routing, fixed 7 misrouted keywords, fixed broken imports, added smoke tests for 14 untested modules (178 tests)

Phase 2 — Model Routing, Readiness CLI, Knowledge Graph & Observability

• Model routing: Registry, cost prediction, provider compatibility, dynamic router with budget-aware auto-downgrade (opus→sonnet→haiku) (174 tests)
• Readiness CLI: Score/report/remediate commands with 4-format output (terminal/markdown/JSON/summary) (123 tests)
• Cross-repo knowledge graph: Federated query across repositories, relationship inference, portable exports to ~/.claude/knowledge/ (119 tests)
• Observability CLI: Unified log aggregation, alert management, cost reporting with status/events/alerts/costs commands (169 tests)

Phase 1 — Mission Orchestrator, Plugin Marketplace, Headless Execution & Code Review

• Mission orchestrator: Dispatch loop, handoff pipeline, milestone gates, state recovery, E2E tests (100+ tests)
• Plugin marketplace: Manifest validation, 3-scope resolution, git marketplace, runtime loading (160 tests)
• Headless execution: 5-tier autonomy, 4 output formats (JSON/markdown/SARIF/JUnit), permission enforcement (139 tests)
• Code review pipeline: Diff parsing, P0-P3 severity, 8-criteria bug detection, 2-pass review pipeline (101 tests)

Phase 8 — System Repair (Prior)

• Test suite: 201 failures → 0 (framework 3256/0, tools 462/0)
• Reflection system: Fixed score normalization, registered missing hooks, token reporting
• A2A Protocol: Auto-start hook, graceful shutdown, lazy client dispatch
• Skills ecosystem: 69 low-scoring skills improved to 100; average score 87 → 96
• Windows platform: YAML block scalar parsing, path resolution, glob expansion fixes

See CHANGELOG.md for full details.

Quick Start (TL;DR)

Runtime: Node >=22.5.0, pnpm.
Windows Setup: Requires Python and C++ Build Tools installed for compiling native AST add-ons during setup.
Indexing Acceleration: Natively supports automatic Multi-GPU distribution for semantic indexing (dynamically spreading LanceDB embeddings across all detected NVIDIA GPUs via ONNX). Defaults gracefully to fully parallelized CPU parsing if GPUs are unavailable or disabled.
Agent Studio runs seamlessly on Windows PowerShell, WSL, macOS, and Linux.

Initialize the entire ecosystem (installs deps, compiles registries, indexes code):

pnpm run setup

Search immediately after indexing:

pnpm search:code "authentication logic"   # hybrid text + semantic search (~5ms cached)
pnpm search:compress "how routing works"   # search + compress + dedup pipeline
pnpm search:structure                      # project structure + deps + Mermaid diagram
pnpm search:tokens .claude/lib             # token budget analysis + refactor recommendations
pnpm search:file .claude/lib/code-indexing/hybrid-lazy-indexer.cjs 1 60

Text search (pnpm search:code) works instantly even without the full index build.
Running code:index:reindex adds semantic ranking for concept-level queries.
Repeated queries are auto-cached (~5ms hit vs ~800ms miss). BM25 index auto-updates on file edits.

search:compress combines search + adaptive compression + memory dedup into a single command — use it when a topic spans many files and you need a compressed summary.

search:tokens shows file/directory sizes, token estimates, and recommends splitting oversized source files (>15K tokens) into smaller modules for better AI agent readability.

Optional API Keys (AI-Powered Skills)

Some skills require external API keys. All are optional — core functionality works without them.

# Copy the example env file and add your keys
cp .env.example .env

Variable	Used by	Notes
OPENAI_API_KEY	tts-generation (OpenAI TTS), transcription (cloud backend)	Optional — local alternatives available
ELEVENLABS_API_KEY	tts-generation (ElevenLabs voices)	Optional — OpenAI TTS or gTTS (free) as fallback
EXA_API_KEY	deep-research (enhanced semantic search)	Optional — web search works without it

Skills that work without any API key: transcription (local via faster-whisper), tts-generation (gTTS, free), browser-automation, diagram-generator, all code/routing skills.

Dynamic Agent Worktrees

Agent Studio dynamically supports Git Worktree isolation for dangerous/massive subagent tasks. The orchestrator spawns isolated-* agents (e.g., isolated-developer, isolated-architect) for high-risk or sweeping refactors. These agents inherently use the -w flag in Claude Code to sandbox their work in isolated branches—preventing race conditions during parallel execution.

Important for Worktrees: The ecosystem setup wizard automatically enables Git optimization (core.untrackedCache true and core.fsmonitor true). This prevents Git from hanging or triggering "too many active changes" warnings during massive parallel file generation or background vector indexing operations.

Agent Teams (Experimental)

Agent Studio is designed to support Claude Code's Agent Teams feature for multi-session parallel coordination (Claude Code v2.1.32+, Opus 4.6 required). The Router-subordinate architecture allows the router to dispatch work to teammate agents running in parallel sessions. A WAL (Write-Ahead Log) memory synchronization protocol is planned to ensure safe concurrent writes to shared memory files during parallel execution. Enable via CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 and optionally set CLAUDE_CODE_SUBAGENT_MODEL for sub-agent cost optimization. Configure display mode via teammateMode in settings.json or the --teammate-mode CLI flag.

Multi-LLM Consulting & Council

Agent Studio natively supports integrating with other headless LLM Code CLIs (Gemini, Codex, Cursor, and Claude Code). The multi-llm-consultant agent can dynamically detect which of these CLIs are authenticated on your system and distribute prompts in parallel. It also features a built-in llm-council skill that automatically runs a robust 3-stage deliberation protocol (independent completions -> anonymized peer review & ranking -> chairman synthesis) for complex architectural decisions.

Current Footprint

• Agents: 124 files (includes 12 isolated worktree variants)
• Skills: 476 SKILL.md definitions
• Rules: 16 docs
• Schemas: 318 *.schema.json
• Commands: 262 .claude/commands/*.md

Systems Architecture

Agent Studio includes several integrated subsystems built across four development phases:

System	Path	Purpose
Mission Orchestrator	.claude/lib/mission/	Dispatch loop, handoff pipeline, milestone gates, state recovery
Plugin Marketplace	.claude/lib/plugins/	Manifest validation, 3-scope resolution, git marketplace, runtime loading
Headless Execution	.claude/lib/exec/	5-tier autonomy enforcement, multi-format output (JSON/markdown/SARIF/JUnit)
Code Review Pipeline	.claude/lib/review/	Diff parsing, P0-P3 severity classification, 8-criteria bug detection
Model Router	.claude/lib/routing/	Cost-aware model selection, budget engine with auto-downgrade chain
Readiness CLI	.claude/lib/readiness/	Project readiness scoring, configurable thresholds, 4-format reporting
Knowledge Graph	.claude/lib/memory/	Cross-repo federated query, relationship inference, portable exports
Observability CLI	.claude/lib/monitoring/	Unified log aggregation, alert management, cost tracking
Self-Evolving Skills	.claude/lib/evolution/	Usage tracking, pattern detection, suggestion generation, evolution triggers
GitHub Integration	.claude/lib/github/	gh CLI wrapper, webhook simulation, mention parsing, CI status reporting
Consensus Engine	.claude/lib/consensus/	Mixture-of-agents fan-out, multi-model consensus synthesis
Skill Auto-Creator	.claude/lib/evolution/	Session transcript analysis, autonomous skill generation, security scanning
Session FTS Index	.claude/lib/memory/	SQLite FTS5 full-text search over session JSONL logs
Process Registry	.claude/lib/workers/	Background process lifecycle, checkpoint/restore, stdout ring buffer

Framework Upgrade Initiative (Phase 1 Complete)

Agent Studio's roadmap includes a structured multi-phase upgrade derived from analysing 8 external agent frameworks:

Framework	Focus area
GSD (Get Shit Done)	Task discipline, atomic commits, deviation docs
BMAD-METHOD	Project constitution, workflow snapshots
CrewAI	Failure taxonomy, role-based routing
lossless-claw	Context compression, anomaly preservation
AgentRx	Agent fingerprinting, structured diagnostics
agency-agents	Review severity, code quality vocabulary
MetaClaw	Frontmatter parsing, skill metadata
awesome-llm-apps	Composable utility patterns

The analysis produced 47 candidate features (12 P0, 25 P1, 10 P2). Phase 1 shipped 6 features:

ID	Feature	Artifact
D8	Configurable context thresholds	.env.example + spawn-token-guard.cjs
F1	10-category failure taxonomy	.claude/schemas/failure-taxonomy.schema.json
C4	Review severity taxonomy	.claude/schemas/review-severity.schema.json
G1	Agent fingerprinting	.claude/lib/utils/agent-fingerprint.cjs
D7	Anomaly preservation	.claude/lib/utils/anomaly-detector.cjs
H1	SKILL.md frontmatter parser	.claude/lib/utils/skill-frontmatter-parser.cjs

Full implementation plan: .claude/context/plans/framework-upgrade-plan-2026-03-17.md

Quality Gates and Verification Patterns

Agent Studio ships several features that enforce completion quality and reduce plan drift across agent pipelines.

Project Constitution

A project constitution file (.claude/context/project-context.md) is auto-injected into spawn prompts. It carries operational constraints — scope boundaries, architecture conventions, and non-negotiables — so every spawned agent operates from the same baseline without needing them restated per-task.

Analysis Paralysis Guard

A hook at .claude/hooks/session/analysis-paralysis-guard.cjs monitors consecutive read-only tool calls and fires a warning when an agent exceeds its tier threshold. Thresholds are agent-type-aware:

Agent type	Read-only call limit
executor	5
analyst	15
orchestrator	20
hunter	25

Must-Haves Verification

The must-haves schema (.claude/schemas/must-haves.schema.json) provides goal-backward verification. Planners declare truths (facts that must hold), artifacts (files that must exist), and key_links (cross-references) as acceptance criteria. The reflection-agent scores each task completion against the must_haves block.

Deviation Protocol

When a developer agent needs to deviate from a plan, it documents the deviation — reason, scope change, impact — before making changes. This creates an audit trail and keeps planner state consistent with what was actually built.

SUCCESS/FAILURE Metrics

The universal spawn template includes a criteria_met/criteria_failed block in TaskUpdate metadata. Every agent completion carries structured evidence of what passed and what did not, enabling downstream agents and the reflection pipeline to make data-driven decisions.

Verification Gap Reporting

QA agents emit structured gap reports using the verification-gap schema (.claude/schemas/verification-gap.schema.json). Each gap has an ID (G1, G2...), severity (critical, high, medium, low), and a description. The planner ingests these reports and generates targeted fix tasks — closing the feedback loop between QA findings and implementation work.

Token Budget Estimation

Planners attach an estimated_tokens field to every task. Tasks projected to exceed 80K tokens are split before dispatch. This prevents agents from running into context overflow mid-task and avoids silent truncation.

Live Token Usage and Cost Tracking

A UserPromptSubmit hook (ccusage-statusline.cjs) parses Claude Code's JSONL session logs on every prompt and writes a live status file to .claude/context/runtime/ccusage-status.txt. The router reads this file and includes token usage in pipeline summaries.

The status tracks three layers of cost optimization:

[tokens]      57,685 today (in: 1,403 / out: 56,282) | Cost: $86.82
[cache]       $316.97 saved | 66,701,262 reads, 7,961,389 writes
[compression] 18 events | 596.2KB freed (~152,627 tokens) | ~$0.76 saved

Line	What it measures	Optimization layer
[tokens]	Actual API spend using real pricing tables	Raw cost
[cache]	Savings from Anthropic's prompt caching (90% discount on repeated context)	Server-side
[compression]	Tokens avoided by the framework's context compression pipeline	Client-side

Pricing is calculated per-model using built-in rate tables (updated March 2026):

Model	Input	Output	Cache Write	Cache Read
Opus 4.6	$5.00/M	$25.00/M	$6.25/M	$0.50/M
Sonnet 4.6	$3.00/M	$15.00/M	$3.75/M	$0.30/M
Haiku 4.5	$1.00/M	$5.00/M	$1.25/M	$0.10/M

Set CCUSAGE_MODEL=sonnet or CCUSAGE_MODEL=haiku to match your model. Defaults to opus.
Set CCUSAGE_STATUSLINE=off to disable.

Workflow Continuation Snapshots

Execution context is persisted using the workflow-snapshot schema (.claude/schemas/workflow-snapshot.schema.json). When a session is interrupted, the snapshot carries enough state for a new session to resume without re-running completed phases.

Checkpoint Taxonomy

Pipelines emit standardized checkpoints (.claude/schemas/checkpoint-taxonomy.schema.json) at wave_complete, phase_gate, and quality_gate boundaries. Orchestrators use these to verify forward progress before advancing.

Repository Layout

.claude/   # agents, skills, rules, hooks, tools, schemas, docs
.cursor/   # Cursor-specific assets
scripts/   # validation and maintenance scripts
tests/     # project and framework tests
.tmp/      # local debug/temp artifacts (not release docs)

For External Contributors

Use this path if you are proposing changes to the ecosystem itself.

1. Install and bootstrap:

pnpm run setup

2. Run baseline validation:

pnpm validate
pnpm validate:full
pnpm validate:schemas
pnpm validate:commands
pnpm validate:routing

3. Run tests relevant to your change:

pnpm test
pnpm test:framework
pnpm test:tools
pnpm test:code-indexing

4. Enforce style before shipping:

pnpm lint
pnpm format:check

5. Mission CLI (Factory Droid-aligned orchestration):

pnpm mission:init                      # scaffold new mission bundle
pnpm mission:validate <mission-path>   # validate features.json + schemas
pnpm mission:lint <mission-path>       # lint features for circular deps
pnpm mission:grade <mission-path>      # grade against 17 alignment rules (0-100)
pnpm mission:audit <mission-path>      # query audit trail
pnpm mission:status <mission-path>     # feature vs assertion progress

Notes:

• Prefer package.json scripts as the source of truth for runnable workflows.
• Archived test suites are intentionally stubbed in scripts (see script output messages).

For Internal Agent Operators

Use this path if you are running Agent Studio as an operational control plane.

1. Keep registries and routing artifacts fresh:

pnpm agents:registry
pnpm skills:index
pnpm manifest:generate
pnpm routing:prototypes

3. Memory lineage and Skill Marketplace (v3.2.0):

pnpm mmp:lineage <record-id>      # walk ancestry chain for a CAT7 memory record
pnpm mmp:descendants <record-id>  # list all downstream records
pnpm skill:install <package>      # install a verified skill package from the marketplace

2. Track memory and operational health:

pnpm memory:status
pnpm memory:health
pnpm worker:summary

3. Run integration checks before larger pipeline runs:

pnpm integration:headless:json
pnpm validate:full

4. Reset context safely when sessions get noisy:

pnpm context:reset --scope soft --force

Memory System (Current Operating Model)

The memory path now supports two operating modes for spawned agents:

• MEMORY_MODE=hybrid (default): legacy memory injection (gotchas/patterns/decisions/...).
• MEMORY_MODE=observational: injects observations_summary.md + recent rows from observations.jsonl.
• OBSERVATIONAL_MEMORY_ENABLED=off: kill switch that forces hybrid mode.

Additional controls:

• Section token budgets:
  • MEMORY_SUMMARY_BLOCK_MAX_TOKENS (default 400)
  • MEMORY_RECENT_OBSERVATIONS_MAX_TOKENS (default 400)
  • MEMORY_TIER_B_MAX_TOKENS (default 400)
• Session compaction:
  • OBSERVATIONS_COMPACT_ON_SESSION_END=on (default)
  • OBSERVATIONS_COMPACT_MAX=50 (default)
• Contradiction tagging is deferred by default:
  • OBSERVATIONS_CONTRADICTION_ENABLED=off
  • OBSERVATIONS_CONTRADICTION_MAX_AGE_DAYS=90

Primary reference:

• .claude/docs/MEMORY_SYSTEM.md

Operational gates:

• pnpm run test:memory:ci
• pnpm run metrics:memory:slo:ci
• pnpm run metrics:memory-cache:ci
• pnpm run test:framework

CI workflows:

• .github/workflows/memory-ci.yml
• .github/workflows/memory-mvp-gate.yml

Hybrid Lazy Code Search

Agent Studio uses a hybrid lazy search model:

• Instant text retrieval via ripgrep (no upfront full indexing)
• Semantic vector ranking via fastembed (BGE-small) with GPU acceleration
• Reciprocal Rank Fusion (RRF) to combine lexical and semantic candidates
• Subprocess embedding isolation to prevent ONNX Runtime memory leaks

Setup:

# Build the full index (BM25 text + semantic vectors)
pnpm code:index:reindex    # ~12 min with GPU, ~17 min CPU-only

# Enable semantic search in .env
HYBRID_EMBEDDINGS=on       # text + semantic ranking (default after setup)
EMBED_SUBPROCESS=on        # ONNX memory leak workaround (default)

Without code:index:reindex, text search still works but semantic/concept queries
(e.g. "authentication flow for refresh tokens") will return poor results.

Guidance:

• Use pnpm search:code for broad discovery and ranked matches.
• Use pnpm search:structure for structure-oriented lookup.
• Use rg directly for strict literal/symbol matches and exact filters.

Search Mode Comparison

Tool/Mode	What it does best	Latency profile	Determinism	Token/output profile
pnpm search:code "query"	Conceptual discovery and ranked candidates	Fast (~0.2-0.7s on this repo)	High	Compact ranked output (good for agents)
pnpm search:code "ast:pattern"	Structural intent with optional ast-grep refinement	Moderate (~0.18s warm daemon baseline, higher for explicit ast:)	High if pattern is explicit	Compact, structure-aware candidates
pnpm search:structure	Repo map, entrypoints, dependency orientation	Fast one-shot structure pass	High	Very low output volume
rg -F "literal"	Exact symbol/literal lookup	Fastest (~15-35ms measured)	Highest	Larger raw output unless scoped
rga "query"	Cross-file search (pdf/docs/archives)	Slower than rg	High	Can be noisy; scope early
rg → fzf	Human interactive narrowing/selection	Interactive	Operator-dependent	Great for manual triage, not default agent path

Selection contract:

• Agents should default to pnpm search:code for discovery.
• Use rg -F for exact anchors before edits/refactors.
• Use ast: only when the question is structural (shape/pattern), not plain text intent.
• Keep fzf optional and human-in-the-loop; do not make it a hard dependency of automated wrappers.

Perf Runbook (Daemon + Prewarm)

Use daemon mode for repeated searches in active sessions.

# Start/inspect daemon
pnpm search:daemon:start
pnpm search:daemon:status

# Prewarm rg + LanceDB + semantic path
pnpm search:daemon:prewarm

# Run searches (daemon on by default)
pnpm search:code "authentication logic"

# Stop daemon when done
pnpm search:daemon:stop

Query Cache and BM25 Incremental Updates

Repeated or semantically similar queries are served from a local cache, avoiding redundant embedding lookups. The cache uses cosine similarity to match queries, so slight rephrasings still hit the cache.

After file edits, the BM25 text index updates incrementally (no full reindex needed).

Variable	Default	Purpose
SEARCH_CACHE_ENABLED	on	Semantic query cache (set to off to disable)
SEARCH_CACHE_TTL_MS	300000	Cache entry TTL (5 min)
SEARCH_CACHE_SIMILARITY	0.95	Cosine threshold for cache hit
BM25_INCREMENTAL_UPDATE	on	Post-edit BM25 fast update (set to off to disable)

Disable daemon or semantic mode when you need deterministic baselines:

# Direct (no daemon transport)
HYBRID_SEARCH_DAEMON=off pnpm search:code "authentication logic"

# Text-only (skip semantic ranking)
HYBRID_EMBEDDINGS=off pnpm search:code "authentication logic"

# Force semantic ranking
HYBRID_EMBEDDINGS=on pnpm search:code "authentication logic"

Daemon tuning toggles:

# Auto-prewarm on daemon startup
HYBRID_DAEMON_PREWARM=true pnpm search:daemon:start

# Idle timeout (ms) before daemon auto-exit
HYBRID_DAEMON_IDLE_MS=600000 pnpm search:daemon:start

# Custom daemon port
HYBRID_DAEMON_PORT=47653 pnpm search:daemon:start

Expected latency profile on this repo (Windows, measured):

• Cold daemon first query (no prewarm): ~1.35s average
• First query after search:daemon:prewarm: ~0.40s average
• Warm repeated daemon queries: ~0.18-0.19s steady state
• Direct mode (HYBRID_SEARCH_DAEMON=off) repeated CLI calls: ~0.73s average

Memory + Search + Token Saver (Simple Flow)

If you only remember one thing, remember this:

1. Search finds candidates
2. Memory keeps what matters
3. Token saver compresses only when context gets too large

Step-by-step

1. Start with search:
   • Run pnpm search:code "your query" to find likely files/snippets quickly.
2. Read only the best matches:
   • Open a small set of top results instead of dumping whole folders.
3. If the result set is still too big:
   • Use Skill({ skill: 'token-saver-context-compression' }) to compress/summarize evidence.
4. Save useful outcomes to memory:
   • Store durable findings (patterns, gotchas, decisions, issues).
5. On future spawns:
   • The spawn prompt injects memory/RAG evidence and expects citation IDs like [mem:...] and [rag:...].

When to use token saver vs normal flow

• Use normal flow (default):
  • Small task, few files, short snippets.
• Use token saver:
  • Many search hits, long logs, or large cross-file synthesis.
  • You need a compact evidence pack for handoff/review.

Why this is the default design

• Search is fast and good for discovery.
• Memory prevents relearning the same lessons.
• Token saver is a pressure valve, not the first step.
• This keeps prompts smaller while staying grounded in evidence.

Minimal operator recipe

# 1) Discover
pnpm search:code "auth token refresh bug"

# 2) If context is too large, compress (inside agent flow via Skill)
# Skill({ skill: 'token-saver-context-compression' })

# 3) Persist useful outcomes (via MemoryRecord or write paths that trigger memory sync hooks)

# 4) Validate memory/search pipeline health
pnpm test:memory:ci
pnpm metrics:memory:slo:ci

Autonomous Quality Daemon (New)

This repo is still session/CI-driven, but now includes a background quality daemon you can run independently.

What it does:

• Runs the artifact regression gate on a timer
• Writes heartbeat/state to .claude/context/runtime/artifact-quality-daemon-state.json
• Opens/resolves system remediation events in .claude/context/runtime/remediation-queue.jsonl

Commands:

# One cycle now
pnpm quality:daemon:run-once

# Continuous loop (foreground)
pnpm quality:daemon:start

# Inspect daemon heartbeat/state
pnpm quality:daemon:status

Key env var:

• ARTIFACT_QUALITY_DAEMON_INTERVAL_MS (default 300000)

Heartbeat Ecosystem & Telegram Control

Agent Studio includes a background heartbeat ecosystem that keeps the agent runtime healthy, indexed, informed, and reachable from your phone.

Heartbeat Auto-Start

Heartbeat loops start automatically at each session. The router's Step 0.5 preflight reads
heartbeat-active.json and spawns heartbeat-orchestrator if any loops are missing or expired.

You can also start them manually with /heartbeat-start in your Claude Code session.

The 8 loops:

Loop	Schedule	What it does
0 — Auto-reschedule	Every 2 days	Re-registers any loops that expired (3-day Claude Code limit)
1 — Continuous reflection	Every 2 hours	Extracts patterns from session transcripts; rotates memory when learnings.md exceeds 35KB
2 — Agent evolution	Daily at 3am	Applies accumulated learnings to improve agent definitions
3 — Morning briefing	8am weekdays	Summarizes open issues, recent commits, and 2 priority tasks for the day
4 — Codebase indexing	Every 4 hours	Keeps the hybrid BM25 + semantic search index fresh
5 — Context drain	Every 15 minutes	Detects when the task pipeline is idle and prompts for /clear
6 — Telegram polling	Every 2 minutes	Polls your Telegram bot for commands and routes them to agents
7 — Research digest	Daily at 7am	Fetches ArXiv papers and Exa web results matching your configured topics

All loops are session-scoped — they restart when you open a new terminal. Loop 0 prevents silent
expiry within a session by re-registering loops before the 3-day Claude Code limit is reached.

Full loop contracts and state files: .claude/docs/HEARTBEAT_STATE_CONTRACTS.md

Telegram Phone Control Setup

Control Agent Studio from Telegram while the session is running.

Your bot: @Agent_studio_bot — already created and wired in.

Steps to connect your account:

1. Find your Telegram user ID: message @userinfobot on Telegram. It
   replies with your numeric ID (e.g., 123456789).

2. Edit .env and set these three variables:

   TELEGRAM_BOT_TOKEN=<your_token>          # From @BotFather — already set if you followed setup
   TELEGRAM_OWNER_ID=123456789              # Your numeric Telegram user ID
   TELEGRAM_ALLOWED_USERS=123456789         # Comma-separated IDs allowed to use the bot
   

3. Start the heartbeat ecosystem (this activates Telegram polling as Loop 6):

   /heartbeat-start
   

4. Open Telegram and message @Agent_studio_bot. Try /status
   or /help to confirm the connection.

Telegram Commands Reference

Command	Who	What it does
/help	Anyone	List all commands
/status	Anyone	Active loops, pending tasks, last heartbeat time
/tasks	Anyone	Current task list with status
/loops	Anyone	Active heartbeat loops
/logs	Anyone	Last 20 session gap log entries
/memory QUERY	Anyone	Search recent learnings for a keyword
/ask QUESTION	Owner	Ask the AI a question and get a reply
/spawn TYPE DESC	Owner	Spawn an agent (general-assistant, researcher, technical-writer)
/approve TASK_ID	Owner	Two-step task approval (then /confirm TASK_ID within 60 seconds)
/deny TASK_ID	Owner	Cancel a pending task

Owner-only commands require your Telegram user ID to match TELEGRAM_OWNER_ID.

File Drop Support

Send any file in the Telegram chat to automatically convert it to Markdown and store it as agent memory:

File Type	Extensions	Converted To
Documents	.pdf, .docx, .pptx, .xlsx	Structured Markdown
Web pages	.html, .htm	Clean Markdown
Data files	.csv, .json, .xml	Markdown tables
Images	.jpg, .png, .gif, .webp	Alt-text description
Audio	.mp3, .wav, .m4a, .ogg	Transcription (if supported)

How it works:

1. Drop a file in the Telegram chat
2. Bot downloads and converts it using MarkItDown
3. Content is stored as agent memory (searchable by all agents)
4. Bot replies with a confirmation

Requirements: Python with markitdown: pip install 'markitdown[all]'

File size limit: 20MB (Telegram Bot API limit)

Environment Variables Quick Reference

For the full list, see .env.example and .claude/docs/@ENVIRONMENT_CONFIG.md.

Variable	Required	Description
TELEGRAM_BOT_TOKEN	For Telegram	Bot API token from @BotFather
TELEGRAM_OWNER_ID	For Telegram	Your Telegram numeric user ID (privileged commands)
TELEGRAM_ALLOWED_USERS	For Telegram	Comma-separated user IDs allowed to use the bot
TELEGRAM_OWNER_USERNAME	Optional	Your @username (no @ prefix, display only)
TELEGRAM_OWNER_CHAT_ID	Recommended	Numeric user ID (get from @userinfobot)
ARXIV_KEYWORDS	For research loop	Comma-separated ArXiv search topics
EXA_MONITOR_TOPICS	For research loop	JSON array of web monitoring topics

Drop-In Setup (Use In Another Repo)

1. Copy .claude/ into the target repository.
2. Install dependencies required by the copied tooling.
3. Initialize core artifacts:

pnpm memory:init
pnpm agents:registry
pnpm routing:prototypes

Environment

cp .env.example .env

Common controls:

• AGENT_STUDIO_ENV
• REFLECTION_ENABLED
• DEBUG_HOOKS
• HYBRID_EMBEDDINGS

See .env.example and .claude/docs/@ENVIRONMENT_CONFIG.md.

Windows Search Tooling (Scoop)

If you want fast local terminal search tooling on Windows (non-admin), install rga and fzf via Scoop.

Install Scoop (non-admin PowerShell):

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
Invoke-RestMethod -Uri https://get.scoop.sh | Invoke-Expression

Install ripgrep-all + fuzzy finder + ast-grep:

# Install rga (ripgrep-all)
scoop install rga

# Install fzf
scoop install fzf

# Install ast-grep (includes `sg` shim)
scoop install ast-grep

Verify install:

rga --version
fzf --version
sg --version

Runtime discovery behavior:

• Search wrappers auto-discover binaries from node_modules/.bin, Scoop shims, and PATH.
• If your shell PATH is stale after install, wrappers still resolve common Scoop shim paths.
• You can force specific binaries with env overrides (RG_BIN, AST_GREP_BIN, RGA_BIN, FZF_BIN).

fzf Workflows (Interactive Narrowing)

fzf is most useful as an interactive selector on top of rg/rga output.
It improves usability and reduces noise, but does not replace search engines.
For AI/automation, keep fzf optional; interactive prompts are non-deterministic for unattended runs.

Quick file+line picker with preview:

rg --line-number --no-heading --color=always "auth|token|session" . `
  | fzf --ansi --delimiter ":" `
    --preview "bat --color=always --style=numbers --highlight-line {2} {1}"

Search inside office/pdf/archive content (via rga) and narrow interactively:

rga --line-number --no-heading --color=always "invoice|receipt|policy" . `
  | fzf --ansi --delimiter ":" `
    --preview "bat --color=always --style=numbers --line-range=:300 {1}"

Advanced interactive ripgrep launcher (fzf reload pattern):

: | rg_prefix='rg --column --line-number --no-heading --color=always --smart-case' \
  fzf --ansi --disabled \
      --bind 'start:reload:$rg_prefix ""' \
      --bind 'change:reload:$rg_prefix {q} || true'

AST + RG + fzf (structural triage workflow):

# 1) Structural file candidates
ast-grep -p "function `$NAME(`$$$) { `$$$ }" --lang javascript --files-with-matches . `
  | fzf --ansi --delimiter ":" `
    --preview "bat --color=always --style=numbers --line-range=:220 {}"

# 2) Then run exact literal checks inside chosen files
rg -F "function " <chosen-file>

Wrapper policy:

• Keep pnpm search:code non-interactive and deterministic for agents.
• Offer fzf as an optional terminal UX layer for humans doing investigative triage.
• Prefer pnpm search:structure or pnpm search:code "ast:..." for agent structural queries; use sg directly for manual structural audits.

Sources:

• https://scoop.sh/
• https://github.com/phiresky/ripgrep-all?tab=readme-ov-file#scoop
• https://github.com/junegunn/fzf (interactive ripgrep + reload)
• https://junegunn.github.io/fzf/tips/ripgrep-integration/ (official rg+fzf pattern)
• https://github.com/phiresky/ripgrep-all (rga + fzf integration notes)
• https://github.com/phiresky/ripgrep-all/wiki/fzf-Integration (rga-fzf notes)
• https://ast-grep.github.io/guide/pattern-syntax.html (ast-grep pattern language)
• https://ast-grep.github.io/reference/cli.html (ast-grep CLI options)
• https://github.com/sharkdp/bat (fzf preview examples)

Debug Log Utilities

When a session produces unexpected behavior, reduce the raw Claude Code debug log to signal-only lines:

pnpm debug:reduce    # Auto-find most recent ~/.claude/debug/*.txt, copy to .tmp/, and reduce to signal-only lines

The reduced file lands at .tmp/<session-id>-reduced.txt. Kept lines include [ERROR], [WARN], failed/blocked/timeout messages, and stack traces. Repeated identical lines are collapsed.

You can also pass an explicit path:

pnpm reduce-debug-log -- .tmp/session-abc.txt
pnpm reduce-debug-log -- .tmp/session-abc.txt --output .tmp/session-abc.cleaned.txt

The debug-log-analysis skill (Skill({ skill: 'debug-log-analysis' })) documents the full structured workflow for working with these reduced logs.

Skills Catalog

476 active skills across 21 categories. Full details: .claude/context/artifacts/catalogs/skill-catalog.md

Invoke any skill: Skill({ skill: 'name' })

Core Development

Skill	Description
tdd	TDD with RED/GREEN/REFACTOR cycle
debugging	Systematic 4-phase root cause investigation
smart-debug	AI-assisted hypothesis ranking and structured instrumentation
debug-log-analysis	Structured debug log analysis for Claude Code sessions
ripgrep	Enhanced code search with ES module support
code-quality-expert	Clean code principles and refactoring
code-analyzer	Static analysis and complexity metrics
code-semantic-search	Semantic code search with vector index
code-structural-search	AST-based structural pattern matching
verification-before-completion	Evidence-based completion gate function
subagent-driven-development	Implementation via autonomous subagents with two-stage review
requesting-code-review	Dispatch structured two-stage code review
receiving-code-review	Process and act on code review feedback
best-practices-guidelines	Cross-cutting development best practices

Planning & Architecture

Skill	Description
brainstorming	Structured ideation with convergence
plan-generator	Implementation plan generation
prd-generator	Product requirements document creation
architecture-review	System architecture analysis
complexity-assessment	Task complexity classification
diagram-generator	Mermaid diagram generation
wave-executor	EPIC-tier batch pipeline orchestration via fresh Bun processes
sparc-methodology	SPARC methodology workflow
spec-critique	Specification review and gap analysis
spec-gathering	Requirements elicitation
spec-init	Specification bootstrapping
dispatching-parallel-agents	Parallel agent dispatch patterns
ralph-loop	Autonomous iteration via Stop hook loop with verification gate

Security

Skill	Description
security-architect	OWASP/STRIDE/AI threat modeling
auth-security-expert	OAuth 2.1 and JWT security patterns
static-analysis	Semgrep and CodeQL pipelines
variant-analysis	Vulnerability variant discovery
semgrep-rule-creator	Custom Semgrep rule authoring
binary-analysis-patterns	Binary analysis and reverse engineering
memory-forensics	Memory forensics workflows
differential-review	Security-focused diff review
insecure-defaults	Insecure default detection
content-security-scan	Content security scanning
audit-context-building	Security audit context assembly
fix-review	Security fix regression verification
yara-authoring	YARA rule authoring for threat detection
medusa-security	Medusa security patterns

DevOps & Infrastructure

Skill	Description
terraform-infra	Terraform IaC with safety controls
docker-compose	Docker Compose workflows
k8s-manifest-generator	Kubernetes manifest generation
sentry-monitoring	Sentry error monitoring setup
kafka-development-practices	Kafka patterns and best practices
monorepo-and-tooling	Monorepo setup and tooling
cloud-devops-expert	Cloud DevOps workflows
container-expert	Container orchestration patterns

Languages

Skill	Description
typescript-expert	TypeScript type systems and patterns
python-backend-expert	Python backend development
go-expert	Go idioms and patterns
nodejs-expert	Node.js patterns and tooling
java-expert	Java development
rust-expert	Rust safety patterns
php-expert	PHP development
elixir-expert	Elixir/OTP patterns
cpp	C++ development
poetry-rye-dependency-management	Python dependency management (Poetry/Rye)
modern-python	Modern Python with uv/ruff/ty

Frameworks

Skill	Description
react-expert	React patterns and hooks
nextjs-expert	Next.js App Router and RSC
svelte-expert	SvelteKit patterns
vue-expert	Vue 3 Composition API and Pinia
angular-expert	Angular patterns
astro-expert	Astro framework
qwik-expert	Qwik resumability patterns
solidjs-expert	SolidJS fine-grained reactivity
graphql-expert	GraphQL schema and resolvers
htmx-expert	HTMX hypermedia patterns
webmcp-browser-tools	WebMCP browser-side tool exposure to AI agents
starknet-react-rules	StarkNet React blockchain integration
drizzle-orm-rules	Drizzle ORM patterns
convex-development-general	Convex backend development

Vercel & Web Performance

Skill	Description
vercel-deploy	Zero-auth Vercel deployment for 20+ frameworks
vercel-ai-sdk-best-practices	Vercel AI SDK streaming patterns
web-perf	5-phase Core Web Vitals audit workflow
next-upgrade	Next.js upgrade migration
next-cache-components	Next.js caching strategies
shadcn-ui	shadcn/ui component integration
enhance-prompt	AI prompt enhancement patterns

Mobile

Skill	Description
ios-expert	iOS SwiftUI development
android-expert	Android Compose development
flutter-expert	Flutter cross-platform development
expo-framework-rule	Expo framework patterns
tauri-native-api-integration	Tauri native API integration
mobile-first-design-rules	Mobile-first design patterns
nativewind-and-tailwind-css-compatibility	NativeWind Tailwind compatibility
nativescript	NativeScript patterns

Data & Database

Skill	Description
database-architect	Database schema design
database-expert	Database query optimization
data-expert	Data engineering patterns
text-to-sql	Natural language to SQL conversion
large-data-with-dask	Large dataset processing with Dask

Documentation

Skill	Description
doc-generator	Technical documentation generation
writing-skills	TDD applied to skill authoring
readme	README generation patterns
summarize-changes	Change summary generation
markitdown-converter	Convert files to Markdown (PDF, DOCX, XLSX, images, audio)

Git & Version Control

Skill	Description
commit-validator	Conventional commit validation
git-expert	Advanced Git workflows
github-ops	GitHub operations and PR workflows
finishing-a-development-branch	Branch completion checklist
using-git-worktrees	Isolated development workspaces
smart-revert	Safe revert with impact analysis

Creator Tools

Skill	Description
research-synthesis	Multi-source research and synthesis
skill-creator	Create new skills
skill-updater	Update existing skills to production-ready status
agent-creator	Create new agents
agent-updater	Update existing agents
workflow-creator	Create new workflows
workflow-updater	Update existing workflows
hook-creator	Create new hooks
template-creator	Create new templates
schema-creator	Create new schemas
rule-creator	Create new rules
command-creator	Create new commands
tool-creator	Create new framework tools
artifact-integrator	Integrate artifacts into framework
artifact-updater	Update existing artifacts

Memory & Context

Skill	Description
context-compressor	Context window compression
token-saver-context-compression	Search-aware context compression with MemoryRecord
memory-quality-auditor	Memory file quality audit
session-handoff	Cross-session handoff artifacts
task-management-protocol	Task tracking and structured handoff
track-management	Work unit lifecycle management
context-degradation	Context degradation detection
framework-context	Framework context loading
recommend-evolution	Framework evolution recommendations
assimilate	External repository assimilation
creation-feasibility-gate	Pre-creation feasibility check
compliance-policy-check	Policy compliance validation
troubleshooting-regression	Regression diagnosis and fix verification
memory-search	Semantic memory search
insight-extraction	Knowledge extraction from context

Validation & Quality

Skill	Description
checklist-generator	Quality checklist generation
proactive-audit	Proactive framework audit after pipeline changes
response-rater	Agent response quality rating
test-generator	Automated test code generation
accessibility	Accessibility audit and fixes
eval-harness-updater	Evaluation harness maintenance
qa-workflow	Systematic QA validation with fix loops
agent-evaluation	Agent capability evaluation
strict-user-requirements-adherence	Requirements traceability
property-based-testing	Property-based test generation
behavioral-loop-detection	Detect agent behavioral loops via Jaccard similarity scoring
judge-verification	Independent LLM judge evaluation with 4-dimension scoring
error-recovery-escalation	5-level structured error recovery: retry → nudge → replan → fallback → force-done

Specialized Patterns

Skill	Description
thinking-tools	Structured self-reflection checkpoints
sequential-thinking	Dynamic step-by-step hypothesis reasoning
consensus-voting	Multi-perspective decision voting
swarm-coordination	Multi-agent swarm patterns
interactive-requirements-gathering	Guided requirements elicitation
planning-with-files	File-based planning patterns
context-driven-development	Context-aware development workflow
pipeline-reflection-ux	Pipeline reflection UX patterns

External Integrations

Skill	Description
jira-pm	Jira project management
linear-pm	Linear project management
medusa	Medusa e-commerce platform
dynamic-api-integration	Dynamic API integration patterns
project-onboarding	Project onboarding workflow
github-mcp	GitHub MCP integration
arxiv-mcp	arXiv paper retrieval
slack-notifications	Slack notification patterns
gemini-cli-security	Gemini CLI security audit patterns

Incident Response

Skill	Description
incident-runbook-templates	Incident runbook templates
on-call-handoff-patterns	On-call handoff protocols
postmortem-writing	Blameless postmortem writing

Scientific Research

Skill	Description
scientific-skills	Scientific computing (parent with 139 sub-skills)

Other

Skill	Description
advanced-elicitation	Advanced prompt elicitation techniques
ai-ml-expert	AI/ML patterns and best practices
agent-tool-design	Agent tool API design
api-development-expert	REST API development patterns
ask-questions-if-underspecified	Requirements clarification
sharp-edges	Known codebase hazard patterns
webapp-testing	Playwright browser automation testing
stale-module-pruner	Stale module detection and pruning
skill-discovery	Skill discovery and selection
code-style-validator	Programmatic AST-based style validation
dry-principle	DRY enforcement patterns
async-operations	Async/await patterns and anti-patterns

Troubleshooting

"Prompt is too long" / Context Saturation on Startup

If Claude Code subagents crash immediately on spawn with an API size limit error (e.g., "Prompt is too long" or saturating the 200,000 token limit without executing), ensure that the .claudeignore file is present in the repository root.

By default, the Claude Code CLI actively scopes any massive Markdown files (CHANGELOG.md, README.md, GETTING_STARTED.md) and data directories located in the repository root into its invisible system context payload. The .claudeignore file securely blocks this eager-loading behavior, freeing up an estimated 65,000+ tokens and preventing instant crashes.

Operational Notes

• .claude/context/ stores runtime artifacts and persistent operational memory.
• .tmp/ contains temporary/debug outputs and should not be treated as product documentation.
• Schema and command validation should be treated as blocking gates for release-quality changes.