reader/skills/keyword-cleanup-review/SKILL.md

name, description

name

description

keyword-cleanup-review

Review and curate this repository's daily keyword index and frequency stats. Use when the user wants to inspect `data/term_index/term_stats.json`, recent `data/term_index/daily/*.json`, `configs/term_aliases.json`, `configs/term_stopwords.json`, or `configs/filter_context.personal.json` to propose alias merges, stopwords, watch terms, or `interest_keywords` updates without directly modifying configs.

Keyword Cleanup Review

Use this skill to turn the repository's keyword statistics into reviewable cleanup suggestions.

Workflow

1. Build a compact review bundle:

python skills/keyword-cleanup-review/scripts/build_review_bundle.py

Optional knobs:

• --days 7
• --top 50
• --output outputs/term_index/review/keyword-cleanup-bundle.json

2. Read the generated bundle and the suggestion schema:

• outputs/term_index/review/keyword-cleanup-bundle.json
• skills/keyword-cleanup-review/references/suggestion-schema.md

3. Produce two outputs:

• A short Markdown review for humans
• A JSON suggestion file matching the schema

4. Keep the boundary strict:

• Suggest changes to configs/term_aliases.json
• Suggest changes to configs/term_stopwords.json
• Suggest additions to configs/filter_context.personal.json
• Do not directly edit these files unless the user explicitly asks
• Do not suggest direct edits to configs/filter_rules.json unless the user asks for rule logic changes

Review Heuristics

Prioritize these decisions:

• Alias suggestion
  • Same concept with different naming, casing, abbreviation, or Chinese/English variants
• Stopword suggestion
  • Too generic, too broad, or too noisy to help filtering
• Interest keyword suggestion
  • High-frequency and aligned with the user's backend engineering, AI-agent, and frontier-tech focus
• Watch term
  • Recent and potentially important, but evidence is still weak

Prefer conservative suggestions. If confidence is low, put the term into watch_terms.

Inputs

Primary inputs:

• data/term_index/term_stats.json
• data/term_index/daily/*.json
• configs/term_aliases.json
• configs/term_stopwords.json
• configs/filter_context.personal.json
• configs/term_cleanup_policy.json
• configs/term_watchlist.json
• configs/term_change_log.json

The bundled script already compacts these into a single review bundle.

Output Expectations

The Markdown output should:

• Summarize the current state briefly
• List the top terms worth acting on
• Separate alias, stopword, interest-keyword, and watch-term recommendations
• Explain reasoning in short, concrete sentences

The JSON output should follow:

• references/suggestion-schema.md

Repository Notes

Current repository behavior:

• Keyword stats are program-maintained, not LLM-maintained
• Stats are built from keywords, not topics
• Stats only include non-drop candidates
• data/term_index/term_stats.json is rebuilt from daily files, so reruns overwrite the same day instead of double-counting
• cleanup policy, watchlist, and change log are repository-managed governance inputs and should be respected during review

Keep suggestions aligned with that design.

Resources

• Script:
  • scripts/build_review_bundle.py
• Reference:
  • references/suggestion-schema.md