Project Learnings

Capture project-specific patterns and anti-patterns into the project's knowledge base (e.g., CLAUDE.md, .cursorrules, or equivalent). This creates a self-improving feedback loop where discoveries from debugging, development, and review make future sessions smarter.

Only project-specific knowledge qualifies. Generic programming advice does not belong in a project knowledge base.

Step 1: Evaluate Discovery

Determine if the finding qualifies as project-specific. The finding must pass at least ONE of these criteria:

If NO to all criteria -> STOP. Do not add generic programming knowledge. Return to the calling skill and report that no project-specific learning was found.

If YES to any -> proceed to Step 2.

Step 2: Read Existing Project Knowledge Base

1. Find the project's knowledge base file:

   • Check the repository root for CLAUDE.md, .cursorrules, or similar AI instruction files
   • If not found, check if there is a project-level configuration directory

2. Parse existing content:

   • Understand the existing structure, headings, and conventions
   • Look for sections where this learning would fit (e.g., "Known Gotchas", "Bug Patterns", "Conventions", "Known Challenges")
   • Check for duplicate or similar entries already present

3. If a similar entry already exists -> STOP. Report to the calling skill that this knowledge is already captured. Do not create duplicates.

4. Identify placement:

   • If an appropriate section exists, plan to add the entry there
   • If no appropriate section exists, plan to propose a new section (e.g., ## Known Gotchas or ## Project-Specific Patterns)
   • New sections should be placed after the main documentation sections but before appendices or settings

Step 3: Format the Learning

Write a concise, actionable instruction following these rules:

Format:

• Use imperative form: "Always validate X before calling Y"
• Include the WHY: "...because the API returns dates as strings, not Date objects"
• Keep it to 1-3 lines
• Follow the existing knowledge base style and conventions

Templates:

For bug patterns:

- **[Area/Component]**: [What to do/avoid] -- [why, with specific details]

For API gotchas:

- `functionName()` in `path/to/file`: [What is surprising about it] -- [consequence if ignored]

For architectural constraints:

- [Constraint description] -- [why it exists and what breaks if violated]

Examples of well-formatted learnings:

• Order processing: Always multiply amounts by 100 before passing to processOrder() -- it expects cents, not dollars
• db.query() in src/database.ts: Always pass the timeout option -- the default is infinite and has caused production hangs (30 second timeout recommended)
• Never import from internal/ directories in src/api/ -- the build system treats these as separate compilation units and circular dependencies will silently break HMR

Step 4: Confirm with User

Present the proposed addition to the user.

Show:

1. The exact text to be added
2. Where it will be placed in the knowledge base (section name, after which line/entry)
3. Why this qualifies as project-specific

Prompt the user:

• "Add this" -- Write the entry as proposed
• "Edit before adding" -- User provides modified text, then write that instead
• "Skip" -- Do not add anything, return to calling skill

Step 5: Write Update

If the user confirmed (or provided edited text):

1. Edit the knowledge base file to add the entry at the identified location
2. If a new section was needed, create the section heading first
3. Verify the edit was applied correctly by reading the modified area
4. Report success to the calling skill with a summary of what was added

If the user chose "Skip":

• Report to the calling skill that the learning was declined
• Do not modify any files

Integration Notes

What this component does: Evaluates debugging and development discoveries for project-specific relevance, then captures qualifying learnings into the project's AI knowledge base file (CLAUDE.md or equivalent) with user approval.

Capabilities needed:

• File read/edit operations (to read and update the project knowledge base)
• User interaction (to confirm additions)
• Search for files matching patterns (to locate the knowledge base file)

Adaptation guidance:

• The target file (CLAUDE.md) is specific to Claude Code -- adapt the file detection logic in Step 2 to find your platform's equivalent (e.g., .cursorrules for Cursor, .github/copilot-instructions.md for Copilot)
• The evaluation criteria in Step 1 are universal -- keep them regardless of platform
• This skill is always invoked by other skills (bug-killer, feature-dev), never directly by the user

Configurable parameters: None