grind-research
Skill12k starsChanged 9 days agoApache-2.0View on GitHub
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.
- Installs packages
We split this skill into the rules it gives an agent
39 rules. About 0 are common, 34 are distinctive, and 5 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈34
Distinctive · estimate
This author's own choices. Worth a look.
5
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
Use Read tool WITHOUT limit/offset to read the entire document(s)
- Distinctive: line 15
Don't read any other files in the rpi/ directory for the task, focus on the research document.
- Distinctive: line 28
Use when a section mentions a component but doesn't provide file paths
- Distinctive: line 29
Use to discover related files not yet documented
- Distinctive: line 31
Use when a section describes behavior but lacks implementation detail
- Distinctive: line 32
Use to trace data flow through components mentioned in the doc
- Distinctive: line 34
Use when a section describes a pattern but lacks concrete code examples
- Distinctive: line 36
Use to add color around library behavior, external systems, and behavior that cannot be discovered through codebase research
- Distinctive: line 39
Start with locator agents to find what exists
- Distinctive: line 41
Run multiple agents in parallel when searching for different things
- Distinctive: line 49
codebase-pattern-finder: Verify that described patterns and code examples still exist as documented
- Distinctive: line 50
codebase-analyzer: Verify that described behavior and data flows are accurate
- Very specific: line 55
Update the document at the same docPath
- Distinctive: line 56
Add new findings to relevant existing sections
- Distinctive: line 60
Add any new open questions discovered during the deep-dive
- Distinctive: line 61
Keep the same YAML frontmatter and format
- Distinctive: line 64
Check if any document is over 1000 lines
- Distinctive: line 65
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
- Very specific: line 68
Read the final output template: Read({SKILLBASE}/references/research_final_answer.md)
- Distinctive: line 75
DO NOT suggest improvements or changes unless explicitly asked
- Distinctive: line 76
DO NOT perform root cause analysis unless explicitly asked
- Distinctive: line 77
DO NOT propose future enhancements unless explicitly asked
- Distinctive: line 78
DO NOT critique the implementation or identify problems
- Distinctive: line 79
DO NOT recommend refactoring, optimization, or architectural changes
- Distinctive: line 80
ONLY describe what exists, where it exists, how it works, and how components interact
- Distinctive: line 82
Document structure should include:
- Very specific: line 92
When you write or edit documents in .humanlayer/tasks/, a cloud permalink is automatically provided in the hook response.
- Distinctive: line 94
Use this permalink in your final output for easy navigation
- Very specific: line 99
When writing markdown files that contain code blocks showing other markdown (like README examples or SKILL.md templates), use 4 backticks …
- Distinctive: line 109
Use parallel Task agents to maximize efficiency and minimize context usage
- Distinctive: line 113
Document cross-component connections and how systems interact
- Distinctive: line 117
REMEMBER: Document and Ask about what IS and WHY, not what SHOULD BE
- Distinctive: line 118
NO RECOMMENDATIONS OR IMPLEMENTATION SUGGESTIONS: Only describe the current state of the codebase
- Distinctive: line 119
File reading: Always read mentioned files FULLY (no limit/offset) before spawning sub-tasks
- Distinctive: line 120
Critical ordering: Follow the numbered steps exactly
- Distinctive: line 121
ALWAYS read mentioned files first before spawning sub-tasks (step 1)
- Distinctive: line 122
ALWAYS wait for all sub-agents to complete before synthesizing (step 5)
- Distinctive: line 123
NEVER write the research document with placeholder values
- Very specific: line 125
Use .humanlayer/tasks/ENG-XXXX-description/YYYY-MM-DD-research.md for task research </guidance>
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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
- 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.
- 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
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
- 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
- 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
- Update the document at the same
- 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
- 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.
- Read the final output template:
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 PermalinksWhen you write or edit documents in .humanlayer/tasks/, a cloud permalink is automatically provided in the hook response.
- The permalink appears as
additionalContextafter 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.mdfor task research</guidance>
- Use
Remember, you must respond to the user according to the output template at {SKILLBASE}/references/research_final_answer.md
Trending
Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data
Who's talking about humanlayer/humanlayer
- anthropics/claude-code“Missing Final Result Event in Streaming JSON Output with sdk”GitHub · Jun 11, 2025 · 5 reactions
- A Hacker News story“Show HN: HumanLayer – Human-in-the-Loop for AI Agents”Hacker News · Aug 21, 2024 · 5 points
- @zzstoatzz.io“fwiw github.com/humanlayer/h...”Bluesky · Nov 25, 2024 · 4 likes
- @github-trending.bsky.social“📦 humanlayer / humanlayer ⭐ 1,333 (+33) 🗒 TypeScript HumanLayer enables AI agents to communicate with …”Bluesky · Aug 29, 2025 · 3 likes
- code-yeongyu/oh-my-openagent“[Question]: How is OMOC compared to the other OpenCode tools?”GitHub · Jan 9, 2026 · 2 reactions
The biggest 5 of 63 mentions linking to the repository that our crawl found. Each line is their own headline.
See all 63
Talked about on GitHub, Bluesky and Hacker News (63 times).
On GitHub · 48
- Jun 11, 2025Missing Final Result Event in Streaming JSON Output with sdk — anthropics/claude-code5 reactions · 4 comments
- Jan 9, 2026[Question]: How is OMOC compared to the other OpenCode tools? — code-yeongyu/oh-my-openagent2 reactions · 2 comments
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On Bluesky · 12
- Nov 25, 2024fwiw github.com/humanlayer/h... — @zzstoatzz.io4 likes and reposts · 1 reply
- Aug 29, 2025📦 humanlayer / humanlayer ⭐ 1,333 (+33) 🗒 TypeScript HumanLayer enables AI agents to communicate with humans in tool-based and async… — @github-trending.bsky.social3 likes and reposts
- Oct 1, 2025Agree 100% I’ve been altering my stack to mimic HumanLayer’s, because I believe that the (very near) future will be working with LLMs to… — @scottlanoue.comsays it tested it2 likes and reposts
- Sep 29, 2025🚀 Skyrocketing! 🚀 (200+ new stars) 📦 humanlayer / humanlayer ⭐ 4,805 (+338) 🗒 TypeScript The best way to get AI coding agents to solve… — @github-trending.bsky.social2 likes and reposts
- Aug 28, 2025📦 humanlayer / humanlayer ⭐ 1,278 (+35) 🗒 TypeScript HumanLayer enables AI agents to communicate with humans in tool-based and async… — @github-trending-js.bsky.social2 likes and reposts
7 more on Bluesky
- Mar 30, 2026📦 humanlayer / humanlayer ⭐ 10,141 (+42) 🗒 TypeScript The best way to get AI coding agents to solve hard problems in complex codebases. — @github-trending-js.bsky.social1 likes and repost
- Dec 28, 2025📦 humanlayer / humanlayer ⭐ 8,241 (+46) 🗒 TypeScript The best way to get AI coding agents to solve hard problems in complex codebases. — @github-trending-js.bsky.social1 likes and repost
- Dec 9, 2025🔥 Hot Repo! 🔥 (100+ new stars) 📦 humanlayer / humanlayer ⭐ 7,566 (+105) 🗒 TypeScript The best way to get AI coding agents to solve hard… — @github-trending-js.bsky.social1 likes and repost
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- Sep 29, 2025🚀 Skyrocketing! 🚀 (200+ new stars) 📦 humanlayer / humanlayer ⭐ 4,806 (+338) 🗒 TypeScript The best way to get AI coding agents to solve… — @github-trending-js.bsky.social1 likes and repost
- Sep 25, 2025📦 humanlayer / humanlayer ⭐ 2,782 (+82) 🗒 TypeScript The best way to get AI to solve hard problems in complex codebases. — @github-trending.bsky.social1 likes and repost
- Sep 1, 2025🚀 Skyrocketing! 🚀 (200+ new stars) 📦 humanlayer / humanlayer ⭐ 1,879 (+225) 🗒 TypeScript HumanLayer enables AI agents to communicate… — @github-trending-js.bsky.social1 likes and repost
On Hacker News · 3
- Aug 21, 2024Show HN: HumanLayer – Human-in-the-Loop for AI Agents5 points · 2 comments
- Aug 30, 2025HumanLayer2 points
- Oct 26, 2026Humanlayer: Way to get AI coding agents in complex codebases1 point
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