Document a recently solved problem to compound your team's knowledge. Creates structured learnings in knowledge-base/project/learnings/.
Scanned 9/3/2026
Install to Claude Code
npx -y skills add jikig-ai/soleur --skill compound --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Compound?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/jikig-ai-compound-soleur)More formats (shields.io, HTML) on the badges page.
---
name: compound
description: "Document a recently solved problem to compound your team's knowledge. Creates structured learnings in knowledge-base/project/learnings/."
---
# Compound
Capture problem solutions while context is fresh, creating structured documentation in `knowledge-base/project/learnings/` with YAML frontmatter for searchability.
## Usage
Activate this skill after solving a non-trivial problem. If context about the problem is not already in the conversation, use `ask_user` to request a brief description.
## Phase 0: Setup
Read project conventions from `GEMINI.md` if it exists. Apply conventions during documentation.
**Branch safety check:** Run `run_shell_command` with `git branch --show-current`. If the result is `main` or `master`, abort with: "Error: compound cannot run on main/master. Checkout a feature branch first."
## Phase 0.5: Session Error Inventory
Before writing any learning, enumerate ALL errors encountered in this session. Output a numbered list. This step cannot be skipped.
Include:
- Wrong file paths, directories, or branch confusion
- Failed shell commands or unexpected exit codes
- API errors or unexpected responses
- Wrong assumptions that required backtracking
If genuinely no errors occurred, output: "Session error inventory: none detected."
## Execution Strategy
Analyze the session context to extract the learning. Unlike the Claude Code version which spawns 5 parallel subagents, this version runs sequentially within the conversation:
### 1. Context Analysis
- Extract conversation history
- Identify problem type, component, symptoms
- Determine YAML frontmatter fields: title, date, category, tags
### 2. Solution Extraction
- Analyze investigation steps
- Identify root cause
- Extract working solution with code examples
### 3. Related Documentation Search
- Search `knowledge-base/project/learnings/` for related documentation using `grep_search`
- Identify cross-references and links
- Find related GitHub issues via `run_shell_command` with `gh issue list`
### 4. Prevention Strategy
- Develop prevention strategies
- Create best practices guidance
- Propose enforcement: hook (strongest), skill instruction (moderate), prose rule (weakest)
### 5. Write Learning File
Determine the optimal `knowledge-base/project/learnings/` category subdirectory. Create the file with this structure:
```markdown
---
title: "<descriptive title>"
date: YYYY-MM-DD
category: <category>
tags: [tag1, tag2]
---
# Learning: <title>
## Problem
<What went wrong or what was unclear>
## Solution
<What fixed it, with code examples>
## Key Insight
<The non-obvious takeaway>
## Session Errors
<Errors from Phase 0.5 inventory, each with a **Prevention:** line>
```
Use `write_file` to create the learning document.
### 6. Constitution Promotion
After writing the learning, check if the insight should be promoted to a project convention:
- If the learning addresses a recurring pattern → propose adding to project conventions
- If the learning exposes a gap in rules → propose a rule addition
- Use `ask_user` to confirm before modifying convention files
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!