knowledge-loop

name	knowledge-loop
description	Extract domain knowledge from your notes and feed it back into CLAUDE.md. Not AI tips — your design principles, business rules, technical decisions, and quality criteria.

Knowledge Loop

Extract domain knowledge from notes and update knowledge-base.md + CLAUDE.md automatically.

Core principle: Extract knowledge that the model cannot infer from the codebase or public data, regardless of how smart it becomes.

Two litmus tests — both must pass:

1. "If the model were 10x smarter, would I still need to tell it this?" → Yes
2. "Could the model figure this out by reading the codebase?" → No

Execution Flow

1. Load Config

Read ~/.claude/knowledge-loop.json. If missing, instruct the user to run setup.sh first.

{
  "source": {
    "directory": "~/notes",
    "pattern": "*.md",
    "date_field": "filename",
    "date_format": "YYYYMMDD_*"
  },
  "output": {
    "knowledge_base": "~/.claude/docs/knowledge-base.md",
    "claude_md": "~/.claude/CLAUDE.md",
    "claude_md_section": "Domain Knowledge (auto-updated)",
    "max_claude_md_lines": 5,
    "max_items_per_category": 8
  },
  "categories": [
    "design-principles",
    "business-rules",
    "technical-decisions",
    "quality-criteria"
  ],
  "language": "en",
  "search_keywords": []
}

Expand ~ in all paths to the actual home directory.

2. Detect New Notes

Find notes modified since the last update.

• Read the knowledge-base.md header for last_updated date
• If knowledge-base.md doesn't exist, process all notes
• Filter notes by the configured date_format pattern (e.g., YYYYMMDD_*)
• Only process notes whose date (from filename) is after the last update

3. Extract Domain Knowledge

Read each new note and extract knowledge that matches the configured categories.

Default categories:

Category	What to extract
design-principles	Why this design was chosen. Architecture rationale. Trade-off decisions.
business-rules	Industry/domain-specific rules. Constraints from business context.
technical-decisions	Technology choices and their reasons (ADR-like). Migration decisions.
quality-criteria	Personal quality standards. Definition of "done". Review criteria.

Extraction litmus tests (both must pass):

1. "If the model were 10x smarter, would I still need to tell it this?" → Yes
2. "Could the model figure this out by reading the codebase?" → No

Anti-patterns (DO NOT extract):

• AI/LLM usage tips (model-dependent, will become obsolete)
• Tool-specific shortcuts or keybindings
• One-time events or meeting notes
• Generic industry knowledge the model already knows
• Subjective opinions without actionable implications
• Design patterns inferable from the codebase (e.g., "we use composition" when the code shows it)

Language: Use the language specified in config (language field).

If search_keywords is configured (non-empty array): Only scan notes containing at least one keyword. If empty, scan all new notes.

4. Deduplicate

Read existing knowledge-base.md and check for duplicates:

• Duplicate → Skip
• Refinement of existing item → Update the existing entry
• Genuinely new → Add to appropriate category

5. Update knowledge-base.md

Write/update the knowledge base file at the configured output.knowledge_base path:

# Domain Knowledge Base

last_updated: YYYY-MM-DD

---

## Design Principles

- **Principle name** — Description of the principle and why it matters
- ...

## Business Rules

- **Rule name** — Description and context
- ...

## Technical Decisions

- **Decision** — What was decided and why (date if known)
- ...

## Quality Criteria

- **Criterion** — What "good" looks like for this aspect
- ...

Rules:

• Keep each category at or under max_items_per_category entries
• When exceeding the limit, merge or remove the least specific/impactful items
• Each entry is one line: - **Bold keyword** — Explanation
• Section headers match the categories config, converted to Title Case with hyphens replaced by spaces

6. Update CLAUDE.md

Update the auto-managed section in the configured claude_md path.

Section markers:

<!-- knowledge-loop:start -->
## Domain Knowledge (auto-updated)

- Most impactful items here (max 5 lines)
- See `docs/knowledge-base.md` for full list

<!-- knowledge-loop:end -->

Rules:

• If markers exist, replace content between them
• If markers don't exist, append the section at the end of the file
• Use the section name from claude_md_section config
• Limit to max_claude_md_lines lines (default 5)
• Select the highest-impact items across all categories
• Always include a reference to the full knowledge base file

7. Health Check

After updating, run diagnostics on the overall system:

Check	Threshold	Action
CLAUDE.md total line count	> 150 lines	Warn: "CLAUDE.md is {n} lines. Research shows instruction adherence degrades beyond 150 lines. Consider pruning."
knowledge-loop section size	> max_claude_md_lines	Error: trim to limit
knowledge-base.md entry age	> 90 days since last_updated without change	Warn per entry: "'{entry}' has not been reviewed in {n} days."
Total entries across all categories	> max_items_per_category x category count	Info: utilization percentage
Codebase inferability spot-check	Sample 2-3 entries	For each, briefly check if the project codebase now makes this knowledge obvious. If so, flag: "'{entry}' may now be inferable from code — consider removing."

8. Report

Report to the user:

Extraction results:

• Number of notes scanned
• New entries added (with category breakdown)
• Entries updated
• Duplicates skipped
• Any items removed due to category limits

Health check:

• CLAUDE.md line count (with warning if > 150)
• Stale entries (> 90 days without review)
• Entries that may now be inferable from code
• Knowledge base utilization (entries / max capacity)

Custom Categories

Users can add custom categories in knowledge-loop.json:

{
  "categories": [
    "design-principles",
    "business-rules",
    "technical-decisions",
    "quality-criteria",
    "customer-insights",
    "compliance-requirements"
  ]
}

Each category will become a section in knowledge-base.md.

Knowledge that spans multiple categories should go in the single best-fit category. Don't duplicate across categories.

Quarterly Review

Every 90 days (or when entry count approaches limits), review the knowledge base:

• Remove entries that are now inferable from the codebase
• Remove entries for deprecated decisions
• Merge entries that have converged into a single principle
• Update entries where the rationale has changed

The skill will warn about entries older than 90 days during the report step.

Notes

• This skill does NOT search the web or check for model/tool updates
• This skill extracts human domain knowledge, not AI usage patterns
• Run daily or when you've written notes with significant decisions/learnings
• The skill is language-aware: set language in config to match your notes