
Twiceshy
@dotts-h
Twiceshy について
Once bitten, twice shy — a shared memory of validated engineering traps, dead-ends and fixes for coding agents, served over MCP. AGPL-3.0.
基本情報
設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"twiceshy": {
"type": "streamable-http",
"url": "https://api.twiceshy.app",
"headers": {
"Authorization": "Bearer tok_YOUR_TOKEN_FROM_twiceshy.app"
}
}
}
}ツール
ツールは検出されませんでした
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
What is Twiceshy?
A self‑hosted service that feeds hard‑won engineering experience — issues, dead‑ends, root causes, validated fixes — to LLM coding agents at decision time, so they stop repeating known mistakes on autopilot. It operates like a private, curated, validated StackOverflow that injects itself at the right moment.
How to use Twiceshy?
Use the hosted instance at twiceshy.app with the remote MCP endpoint https://api.twiceshy.app/, or self‑host following the development instructions (make ci). Interact via MCP tools: search_experience, get_experience (read path) and record_experience (propose‑only write path).
Key features of Twiceshy
- Git‑backed markdown experience records with YAML frontmatter.
- Derived SQLite index with FTS5 (dense retrieval planned).
- Multi‑stage retrieval: fingerprint‑exact → BM25 → dense, with relevance floor and hard cap of 3 results.
- Two channels: push (Claude Code hooks) and pull (MCP tools over streamable HTTP).
- Trust model: agent‑proposed records quarantined, promoted only after sandbox validation and human PR review.
- Background “doctors” for dedup, staleness, repro re‑execution, decay, and abstraction.
Use cases of Twiceshy
- Prevent an agent from re‑introducing a known bug or deprecated API.
- Inject verified workarounds for third‑party issues at decision time.
- Record and propagate root‑cause analyses across a team.
- Capture validated fixes so future agents skip dead‑end investigations.
- Automatically quarantine untested proposals until they pass human review.
FAQ from Twiceshy
What retrieval stages does Twiceshy use?
Retrieval follows a cascade: fingerprint‑exact → BM25 → dense (RRF), with stack‑fingerprint filtering and a relevance floor. At most 3 results are injected; below the floor nothing is injected.
How does Twiceshy ensure experience records are trustworthy?
Agent‑proposed records are quarantined. Promotion requires a sandbox fail‑to‑pass validation and a human‑reviewed pull request. A new record is a PR.
What is the license?
The Twiceshy engine is AGPL‑3.0‑only. Contributions require a signed CLA, and the corpus has a separate licensing strategy (see ADR‑0002).
What is the current status of development?
Bootstrapping. Phase 1 (read path: parser/validator, FTS5 index, fingerprint + lexical search, MCP search_experience/get_experience) is complete. Phase 3 write path (record_experience – propose‑only) has landed. Remaining phases (hooks push channel, dense retrieval, doctors) are tracked as issues.
「その他」の他のコンテンツ
AutoBrowser MCP
autobrowser-aiBrowser MCP is a Model Context Provider (MCP) server that allows AI applications to control your browser
MCP Registry
modelcontextprotocolA community driven registry service for Model Context Protocol (MCP) servers.
Website
FunnyWolfAdversary simulation and Red teaming platform with AI
Mcp
browsermcpBrowser MCP is a Model Context Provider (MCP) server that allows AI applications to control your browser

Peekaboo MCP – lightning-fast macOS screenshots for AI agents
steipetePeekaboo is a macOS CLI & optional MCP server that enables AI agents to capture screenshots of applications, or the entire system, with optional visual question answering through local or remote AI models.
コメント