skillreaper

README

Your AI agent reads 187 skill descriptions every session.
You use 4. Reap the rest.

brew install thousandflowers/tap/skillreaper
reap

One command. Zero config. Read-only. It scans your transcripts, finds every skill/agent/MCP your AI loads but never uses, and tells you exactly what it costs in context window, latency, and money.

The problem

Every Claude Code session loads 150–300 skill descriptions, agent configs, and rule files into context. Most of it is dead weight:

• 187 items scanned
• 142 never used (76 %)
• 8 000 tok/session wasted
• ~2 160 000 tok/month burned on irrelevant instructions

Your agent scrolls through a wall of irrelevant tools looking for the right one. Wrong picks cost turns. Turns cost tokens. Tokens cost money.

reap points at the waste. You decide what goes.

Before → After

Before skillreaper	After skillreaper
187 items loaded every session	45 items, all actively used
Wrong tool 1 in 5 turns	Right tool on first try
8 000 tok/session dead	Full context budget for real work
~30 pages of irrelevant instructions read monthly	Zero
Lower cache hit rate = higher latency	Smaller prompt fits in cache

Install

# macOS — Homebrew
brew install thousandflowers/tap/skillreaper

# Any platform — Go (Go ≥ 1.22)
go install github.com/thousandflowers/skillreaper/cmd/reap@latest

Binary downloads — macOS (Intel + Apple Silicon), Linux (amd64 + arm64), Windows (amd64 + arm64) — all on the releases page. Single static binary, no dependencies.

Upgrading, uninstalling, and platform-specific tips → INSTALL.md.

Usage

reap                     # scan + report (read-only)
reap gap                 # loaded-vs-fired utilization breakdown
reap prune               # quarantine unused items (reversible)
reap keep <name>         # protect an item from pruning
reap restore --all       # undo every prune
reap --json              # structured JSON output
reap --md                # markdown report
reap --days 7            # shorter evidence window
reap version             # print version

Everything is reversible. reap prune moves files to a reaped/ directory with a versioned manifest. Nothing is ever deleted. Run reap restore --all and everything goes back exactly where it was.

Verdicts

Label	Meaning
REAP	Zero uses — safe to quarantine
KEEP	Used, tiny, or manually protected
REVIEW	Too new or not enough sessions

Every verdict includes a reason suffix explaining why.

Loaded vs fired

Beyond the prune verdicts, reap gap shows your utilization rate — how much of what you load you actually use.

⟡ loaded vs fired — last 30 days · 142 sessions

CATEGORY   LOADED  FIRED   UTIL   ────────────       TOKENS
skills        187      4    2%    ▰▱▱▱▱▱▱▱▱▱     ~8 000 →   210
mcp            12      3   25%    ▰▰▱▱▱▱▱▱▱▱          ? →     ?
agents         30      2    7%    ▰▱▱▱▱▱▱▱▱▱     ~1 200 →    90
───────────────────────────────────────────────────────────────
total         229      9    4%    ▰▱▱▱▱▱▱▱▱▱     ~9 200 →   300

Each row breaks down by category (skill, MCP, agent) with item count, token weight, and a 10-segment utilization bar. Low utilization (<10 %) is red, medium (<50 %) yellow, high (≥50 %) green.

The default reap report also includes a compact utilization summary line:

⟡ utilization 4%  —  9/229 items fired · ~300/9 200 tok touched (30d)

This is the real gap between what your agent carries and what it fires — complementary to the shock box (which only counts items that are safe to prune right now).

reap gap          # text breakdown
reap gap --json   # JSON output
reap gap --md     # markdown table

Privacy

100 % local. Zero telemetry, zero network, zero uploads. Reads config files and session transcripts on disk — your data never leaves your machine.

Platform support

Platform	Full support
Claude Code	✅
OpenCode	✅
Codex CLI	✅
Hermes	✅
Cursor	Inventory only (no local transcripts)
OpenClaw	Inventory only (no session history)

How it works

1. Auto-detect — probes every known config directory. Only installed platforms are scanned. No flags needed.
2. Inventory — scans skills, agents, MCP servers, hooks, and prose files across all detected platforms.
3. Evidence — parses session transcripts (JSONL or SQLite). Counts tool_use blocks and command invocations with timestamps.
4. Cost — character weight (ceil(chars / 3.7)) + init parser tool declarations. Model pricing auto-resolves by model name.
5. Verdict — REAP / KEEP / REVIEW with machine-readable reason.
6. Act — reap prune quarantines. reap restore --all undoes.

Limitations (transparency)

Token counts are approximate. The tool estimates tokens as ceil(chars / 3.7), based on the average English BPE tokenizer rate. Real token counts vary by tokenizer (Claude vs GPT vs Gemini) and content (more code ≈ more tokens per char). This is a documented approximation — the relative ranking matters more than the absolute number.

Platform format stability. Each supported platform has its own config layout and transcript format. These change over time as platforms evolve. Parser updates are an ongoing maintenance reality. The project is architected for easy fixes (one struct per platform in internal/platform/), but format changes can lag by days to weeks after a platform update.

Not a tool declaration fix. Claude Code's deferred tools reduce the init-time tool declaration overhead. Skillreaper addresses a different problem: always-loaded skill/agent/prose files. If a skill description is 248 characters, it is read into context every session — regardless of lazy tool loading. These two optimizations are complementary, not competing.

Design

• 100 % local, zero dependencies, single static binary (Go ≥ 1.22)
• Multi-platform — adding a new platform is one struct in internal/platform/
• Reversible quarantine — never deletes, never destructive
• MIT licensed

cmd/reap/       CLI entry point
internal/
  platform/     platform definitions + auto-detection
  scan/         inventory scanners
  usage/        transcript parser (JSONL + SQLite)
  report/       verdict logic + ANSI/JSON/MD renderers
  prune/        reversible quarantine
  cost/         model pricing
docs/           demo assets

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