Turn any codebase into evidence-grounded Markdown docs plus a machine-readable index.json. Every claim cites its source; never invents deployment steps.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Turn any codebase into an evidence-grounded documentation package (overview,
operation, deployment, learning, architecture, API/data, troubleshooting,
maintenance) plus a machine-readable index.json for AI agents. Every
operational/deployment claim cites a source file and a confidence label
(Verified / Inferred / Unknown / Needs confirmation); it never invents
deployment steps and records gaps instead.
Full skill, references, templates, and a validated sample output: https://github.com/wxggzz/tracedocs (MIT).
index.json)index.json manifest; never invents
deployment steps.Use tracedocs to generate evidence-grounded study docs for this repository. Write the output to study-docs/.
User: "Document ./my-app with tracedocs"
The skill scans the repo and writes a study-docs/ package (00-10 manuals +
index.json + _evidence/), citing each operational claim's source and
labelling its confidence - and explicitly noting anything it cannot verify
(for example, "no deployment configuration found in the repo").
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。
Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
日本語の概要は準備中です。原文の説明を表示しています。
Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.
日本語の概要は準備中です。原文の説明を表示しています。
Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates.
日本語の概要は準備中です。原文の説明を表示しています。
Audit whether a website can be found, crawled, and cited by AI answer engines such as ChatGPT Search, Perplexity, Google AI Overviews, and Microsoft Copilot. Use when someone asks why their brand is missing from AI answers, whether AI crawlers can read their site, how to get cited by ChatGPT or Perplexity, or asks for a GEO or AEO (generative / answer engine optimization) review. Produces a citation baseline across buyer-intent prompts, a crawler-access check, a citability review of named pages, and a ranked fix list. Not for keyword rank tracking, paid search, or pages behind a login.
日本語の概要は準備中です。原文の説明を表示しています。
Automate Airtable tasks via Rube MCP (Composio): records, bases, tables, fields, views. Always search tools first for current schemas.
日本語の概要は準備中です。原文の説明を表示しています。