跳到正文
cccskills
免费在 GitHub 公开

grind-research

Deepen existing codebase research documents without ongoing user feedback. It investigates gaps, verifies claims against the current implementation, adds file and line references, corrects outdated details, and updates open questions while preserving the document’s format.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

autonomous deep-dive pass on research document. Use when you want to grind on a research doc without user feedback - digs deeper, vets claims, adds detail.

实测评分 未实测

查看安装方法

适用场景

  • Expanding thin sections in existing research documents
  • Verifying documented code behavior and references
  • Investigating open questions about a codebase
  • Splitting research documents longer than 1,000 lines

包含的文件(2)

  • SKILL.md6.5 KB
  • references/research_final_answer.md759 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Grind Research

You are performing an autonomous deep-dive pass on an existing research document. Self-direct the review by identifying gaps and verifying claims.

Steps

  1. Read the existing document FULLY:

    • Understand what research was previously conducted - there may be more than one research file - they are ALL in scope unless a single doc is explicitly specified
    • Use Read tool WITHOUT limit/offset to read the entire document(s)
    • Don't read any other files in the rpi/ directory for the task, focus on the research document.
    • DO NOT read ticket files - research must stay objective about the current codebase.
  2. Gap analysis — identify thin sections:

    • Review every section of the document
    • Identify areas where related components or interactions are mentioned but not explored
    • Note any "Open Questions" that could be answered with codebase research or web research
    • Build a list of investigation targets
  3. Spawn sub-agents to fill gaps:

    • For each gap identified, spawn the appropriate sub-agent:

    • codebase-locator: Find WHERE files and components live

      • Use when a section mentions a component but doesn't provide file paths
      • Use to discover related files not yet documented
    • codebase-analyzer: Understand HOW specific code works (without critiquing it)

      • Use when a section describes behavior but lacks implementation detail
      • Use to trace data flow through components mentioned in the doc
    • codebase-pattern-finder: Find examples of existing patterns (without evaluating them)

      • Use when a section describes a pattern but lacks concrete code examples
    • web-researcher: Deep research the web

      • Use to add color around library behavior, external systems, and behavior that cannot be discovered through codebase research

    Agent usage tips:

    • Start with locator agents to find what exists
    • Then use analyzer agents on the most promising findings
    • Run multiple agents in parallel when searching for different things
    • Each agent knows its job — just tell it what you're looking for
  4. Claim verification — vet existing claims:

    • Re-read the document after gap-filling

    • Identify specific factual claims about the codebase (file paths, line numbers, function names, behavior descriptions)

    • Spawn sub-agents to verify each claim is still accurate:

    • codebase-pattern-finder: Verify that described patterns and code examples still exist as documented

    • codebase-analyzer: Verify that described behavior and data flows are accurate

    • For any claim that is outdated or incorrect, note it for correction in the update step

  5. Update document:

    • Update the document at the same docPath
    • Add new findings to relevant existing sections
    • Deepen shallow sections with file:line references and implementation details
    • Correct any outdated or inaccurate claims found during verification
    • Move answered items out of "Open Questions"
    • Add any new open questions discovered during the deep-dive
    • Keep the same YAML frontmatter and format
  6. Refine and Split:

    • Check if any document is over 1000 lines
    • if so, split it into 2-3 documents, using your judgement to decide subject matter boundaries and what makes sense, don't ask the user
  7. Update the user

    • Read the final output template: Read({SKILLBASE}/references/research_final_answer.md)
    • Respond with a summary following the template, including GitHub permalinks.

Research Guidelines

Your job is to DOCUMENT AND EXPLAIN THE CODEBASE AS IT EXISTS TODAY:

  • DO NOT suggest improvements or changes unless explicitly asked
  • DO NOT perform root cause analysis unless explicitly asked
  • DO NOT propose future enhancements unless explicitly asked
  • DO NOT critique the implementation or identify problems
  • DO NOT recommend refactoring, optimization, or architectural changes
  • ONLY describe what exists, where it exists, how it works, and how components interact

Document structure should include:

  • Summary answering the research question
  • Detailed findings by component/area with file:line references
  • Code references with descriptions
  • Architecture documentation (patterns, conventions, design)
  • Open questions for areas needing further investigation
<guidance> ## Cloud Permalinks

When you write or edit documents in .humanlayer/tasks/, a cloud permalink is automatically provided in the hook response.

  • The permalink appears as additionalContext after Write/Edit/MultiEdit/Read operations
  • Use this permalink in your final output for easy navigation
  • Example format: http(s)://{DOMAIN}/artifacts/{artifactId}

Markdown Formatting

When writing markdown files that contain code blocks showing other markdown (like README examples or SKILL.md templates), use 4 backticks (````) for the outer fence so inner 3-backtick code blocks don't prematurely close it:

# Example README
## Installation
```bash
npm install example
```

Important notes:

  • Use parallel Task agents to maximize efficiency and minimize context usage
  • Focus on finding concrete file paths and line numbers for developer reference
  • Research documents should be self-contained with all necessary context
  • Each sub-agent prompt should be specific and focused on read-only documentation operations
  • Document cross-component connections and how systems interact
  • Link to GitHub when possible for permanent references
  • Stay focused on synthesis, not deep file reading
  • Have sub-agents document examples and usage patterns as they exist
  • REMEMBER: Document and Ask about what IS and WHY, not what SHOULD BE
  • NO RECOMMENDATIONS OR IMPLEMENTATION SUGGESTIONS: Only describe the current state of the codebase
  • File reading: Always read mentioned files FULLY (no limit/offset) before spawning sub-tasks
  • Critical ordering: Follow the numbered steps exactly
    • ALWAYS read mentioned files first before spawning sub-tasks (step 1)
    • ALWAYS wait for all sub-agents to complete before synthesizing (step 5)
    • NEVER write the research document with placeholder values
  • Path handling: Task-specific research goes in .humanlayer/tasks/
    • Use .humanlayer/tasks/ENG-XXXX-description/YYYY-MM-DD-research.md for task research </guidance>

Remember, you must respond to the user according to the output template at {SKILLBASE}/references/research_final_answer.md

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Automate website navigation, form entry, and clicks through a browser command line tool. Extract page text, capture screenshots, and compare page states to check interactions or test web app behavior across screen sizes.

中文简介正在准备中。当前显示英语简介。

  • Automate form entry and button clicks
  • Extract text from web pages
  • Save page screenshots and PDFs
humanlayer/humanlayer1.2万2026年10月23日 更新

Rewrite dense or ambiguous English into short, direct sentences using principles from ASD-STE100, a standard for clear technical English. It preserves facts and conditions, identifies writing issues, and shows the original and revised text side by side.

中文简介正在准备中。当前显示英语简介。

  • Clarifying English instructions for another agent
  • Simplifying English tool descriptions and error messages
  • Making English easier for non-native readers
humanlayer/humanlayer1.2万2026年10月23日 更新

Inspect GitHub pull requests, read review conversations, and investigate failed automated checks with dedicated scripts. Get consolidated status reports, unresolved discussion threads, and failing job or step logs, with full logs saved for further inspection.

中文简介正在准备中。当前显示英语简介。

  • Checking pull request status in one report
  • Reading unresolved review conversations
  • Investigating failed CI jobs and steps
humanlayer/humanlayer1.2万2026年10月23日 更新

Update release base versions for supported packages, keep matching version fields in sync, and verify generated versions. The workflow covers committing and pushing the changes, with guidance for release steps after merging.

中文简介正在准备中。当前显示英语简介。

  • Prepare a package version update for release
  • Synchronize version fields across related files
  • Verify generated versions before pushing
humanlayer/humanlayer1.2万2026年10月23日 更新

calldiff

免费

Compare function call structures across Git commits, branches, or working files to support code review. Display call trees and trace paths from an entry function to a target function, with support for 22 languages.

中文简介正在准备中。当前显示英语简介。

  • Compare function calls before and after a change
  • Review call structure changes between branches
  • Trace paths from an entry function to a target
humanlayer/humanlayer1.2万2026年10月23日 更新

Automate browser tasks from the terminal using the chrome-devtools-mcp CLI. Inspect page elements, click and fill forms, navigate tabs, capture screenshots, and investigate network requests, console messages, performance, and memory through shell commands.

中文简介正在准备中。当前显示英语简介。

  • Automating form input and page navigation
  • Capturing page or element screenshots
  • Investigating network requests and console errors
humanlayer/humanlayer1.2万2026年10月23日 更新

查看 humanlayer 的全部技能

报告此技能的问题