From llm-wiki
Use when the user invokes /llm-wiki:enrich or asks to re-enrich specific concept docs. Fans out okf-concept-enricher subagents for the named concepts (or all concepts if none specified).
How this skill is triggered — by the user, by Claude, or both
Slash command
/llm-wiki:enrichThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Re-runs enrichment on specific concepts (or all) using the `okf-concept-enricher` agent. Useful for refreshing stale docs after a source schema change.
Re-runs enrichment on specific concepts (or all) using the okf-concept-enricher agent. Useful for refreshing stale docs after a source schema change.
/llm-wiki:enrich [concept-id…]
Without arguments, re-enriches ALL concepts in the bundle.
If concept IDs are specified, use them directly. Otherwise, walk the bundle and collect all concept IDs (from non-reserved .md files with a type field).
For each concept ID, read the existing doc:
python3 <plugin_root>/scripts/okf_doc.py read <bundle_root> <concept_id>
Determine the source for fresh metadata:
resource URI pointing to BigQuery, use okf_bq.py describe.raw/.Fan out okf-concept-enricher subagents in parallel (one per concept). Each agent:
okf_doc.py writeReport: N docs updated, M unchanged, any errors.
Suggest /llm-wiki:index to regenerate indexes after bulk updates.
To re-enrich just one table:
/llm-wiki:enrich tables/events_
To re-enrich all reference docs:
/llm-wiki:enrich references/metrics/dau references/joins/events___users
npx claudepluginhub mlarkin00/plugins --plugin llm-wikiGuides creation and editing of skills using test-driven development with pressure scenarios and subagents to verify agent compliance.
Creates platform-native content for X, LinkedIn, TikTok, YouTube, and newsletters from source material. Adapts voice and format per platform while avoiding engagement bait and filler.