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compilecompile 搜索

Agent Skill

compile 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

423

周安装

18

GitHub Stars

318

下载量

148
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:compile(compile 搜索)
来源仓库:https://github.com/boshu2/agentops
仓库路径:skills/compile
安装命令:
npx skills add https://github.com/boshu2/agentops --skill compile
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/boshu2/agentops --skill compile

简介

compile 用于将 .agents/ 中的原始工件编译成结构化的、相互关联的 Markdown 知识库。

  • 适用于需要从现有代码和文档中提取信号,并生成可验证、可链接的知识文章的场景。
  • 通过 /compile 命令触发,自动处理 git 历史和工件文件,输出结构化知识库。
  • 需确认项目是否有 .agents/ 目录及写入权限,避免误改系统文件。
  • compile 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Compile — Knowledge Compiler

Reads raw .agents/ artifacts and compiles them into a structured, interlinked markdown wiki. Inspired by Karpathy's LLM Knowledge Bases.

What This Skill Does

The knowledge flywheel captures signal reactively (via /retro, /post-mortem, /forge). /compile closes the loop by:

  1. Mining unextracted signal from git and .agents/ (existing)
  2. Growing learnings via validation, synthesis, and gap detection (existing)
  3. Compiling raw artifacts into interlinked wiki articles (NEW — the core value)
  4. Linting the compiled wiki for contradictions, orphans, and gaps (NEW)
  5. Defragging stale and duplicate artifacts (existing)

No vector DB. At personal scale (~100-400 articles), the compiled wiki fits in context windows. The wiki IS the retrieval layer.

Output: .agents/compiled/ — encyclopedia-style markdown with [[backlinks]], index.md catalog, and log.md chronological record.

Pluggable Compute Backend

Set AGENTOPS_COMPILE_RUNTIME to choose the LLM backend:

ValueBackendNotes
claude-cliLocal claude binaryZero-config. Inherits your Claude Code auth — no API key needed. Auto-selected if claude is on PATH and nothing else is set.
ollamaOllama APIDefault model: gemma3:27b. Set OLLAMA_HOST for remote (e.g., ssh -L 11435:localhost:11435 bushido-windows).
claudeClaude API (HTTP)Uses ANTHROPIC_API_KEY. Model: claude-sonnet-4-20250514.
openaiOpenAI-compatibleUses OPENAI_API_KEY + OPENAI_BASE_URL.
(unset)Claude Code sessionCompilation happens inline via the current session's LLM.

When AGENTOPS_COMPILE_RUNTIME is unset, ao compile first tries to auto-detect a local claude binary (claude-cli runtime). If that is also absent, headless compile fails fast with an explicit error naming the env var to set. Interactive /compile invocations still run compilation prompts inline — the agent reading this SKILL.md IS the compiler.

Runtime preference (override auto-detect)

To force a non-auto-detected runtime permanently (e.g. you have claude installed but prefer Ollama for privacy), set it in ~/.agentops/config.yaml:

compile:
  preferred_runtime: ollama

Precedence (high → low): --runtime flag, AGENTOPS_COMPILE_RUNTIME env, compile.preferred_runtime config, claude-binary auto-detect, empty (error).

Large-corpus batching

ao compile passes --batch-size to the headless compiler (default 25 changed files per LLM prompt). A fresh run against a 2000+ file corpus will split into batches automatically instead of sending one giant prompt.

Flags:

  • --batch-size N — files per batch (default 25)
  • --max-batches N — cap batches per invocation; remaining files are picked up on the next run (default 0 = unlimited)

Execution Steps

Phase-by-phase detail lives in references/phases.md. Summary of modes:

  • /compile — Full cycle: Mine → Grow → Compile → Lint → Defrag
  • /compile --compile-only — Skip mine/grow, just compile + lint
  • /compile --lint-only — Only lint the existing compiled wiki
  • /compile --defrag-only — Only run defrag/cleanup
  • /compile --mine-only — Only run mine + grow (legacy behavior)

The steps are:

  1. Mine — extract signal from git + .agents/research/ + complexity hotspots
  2. Grow — LLM-driven validation, synthesis, gap detection; adjust learning confidence
  3. Compile — inventory → topic extraction → wiki articles with [[backlinks]]
  4. Lint — contradictions, orphans, missing cross-refs, stale claims
  5. Defrag — prune stale, dedup near-duplicates, sweep oscillating goals, normalization scan
  6. Report — write .agents/compile/YYYY-MM-DD-report.md

See references/phases.md for the full per-phase procedure, confidence-scoring table, auto-promotion rules, template shapes for article / index / log / lint-report / compile-report, and the normalization defect scan.

Scheduling / Auto-Trigger

Lightweight defrag (prune + dedup, no mining or compilation) runs automatically at session end via the compile-session-defrag.sh hook. This keeps the knowledge store clean without requiring manual /compile invocations. The hook:

  • Fires on every SessionEnd event after session-end-maintenance.sh
  • Skips silently if the ao CLI is not available
  • Runs only ao defrag --prune --dedup (no compilation or mining)
  • Has a 20-second timeout to avoid blocking session teardown

For full compilation, invoke /compile manually or schedule the headless compiler script with your host OS:

# Example: external cron entry for nightly compilation on bushido
0 3 * * * cd /path/to/repo && AGENTOPS_COMPILE_RUNTIME=ollama bash skills/compile/scripts/compile.sh --force

AgentOps exposes this flow through ao compile. If you want unattended compilation, use your host scheduler (launchd, cron, systemd, CI, etc.) to invoke ao compile --force --runtime ollama or call the lower-level bash skills/compile/scripts/compile.sh directly. If you want the broader private overnight loop, use ao overnight start instead of inventing a parallel Dream wrapper inside /compile.

Interactive Modes

These modes describe the interactive /compile skill behavior:

ModeDescription
--compile-onlySkip mine/grow, just compile + lint
--lint-onlyOnly lint the existing compiled wiki
--defrag-onlyOnly run defrag/cleanup
--mine-onlyOnly run mine + grow (legacy behavior)
--fullFull cycle: mine → grow → compile → lint → defrag
--since 26hTime window for the mine phase
--incrementalSkip unchanged source files (hash-based)
--forceRecompile all articles regardless of hashes

Headless Script Flags

For unattended runs, bash skills/compile/scripts/compile.sh supports:

FlagDefaultDescription
--sources <dir>.agentsSource root for learnings, patterns, research, retros, forge, and knowledge
--output <dir>.agents/compiledTarget directory for compiled wiki output
--incrementalonSkip unchanged source files (hash-based)
--forceoffRecompile all articles regardless of hashes
--lint-onlyoffOnly run the lint pass on the existing compiled wiki
--fullonAccepted for parity; default behavior already runs the full headless compile path

Examples

User says: /compile — Full Mine → Grow → Compile → Lint → Defrag cycle.

User says: /compile --compile-only — Just compile raw artifacts into wiki.

User says: /compile --lint-only — Scan existing wiki for health issues.

User says: /compile --since 7d — Mines with a wider window (7 days).

Scheduled externally: Nightly compilation on bushido GPU via Ollama.

Pre-evolve warmup: Run /compile before /evolve for a fresh, validated knowledge base.

Troubleshooting

ProblemCauseSolution
ao mine not foundao CLI not in PATHUse manual fallback in Step 1
No orphaned researchAll research already referencedSkip 2b, proceed to synthesis
Empty mine outputNo recent activityWiden --since window
Oscillation sweep emptyNo oscillating goalsHealthy state — no action needed
Ollama connection refusedTunnel not running or wrong hostRun ssh -L 11435:localhost:11435 bushido-windows or check OLLAMA_HOST
Compilation too slowLarge corpus on small modelUse --incremental or switch to larger model
Hash file missingFirst compilationNormal — full compile runs, hashes saved after

Reference Documents

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.39%
按下载量换算52

Claude

26.97%
按下载量换算40

Cursor

19.7%
按下载量换算29

Gemini CLI

8.62%
按下载量换算13

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安装前确认

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