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Consolidate a contributor's L1 atoms into an L3 persona across 11 dimensions. Triggers when the user says "build contributor persona", "consolidate <user>", or after contrib-ingest completes. This ORGANIZES existing atoms — for creating atoms use contrib-ingest.
How this skill is triggered — by the user, by Claude, or both
Slash command
/tencentdb-agent-memory:contrib-consolidateThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Group one subject's L1 atoms into themes (L2 scenes, conceptual) and write a
Group one subject's L1 atoms into themes (L2 scenes, conceptual) and write a single L3 persona summarising all 11 dimensions with evidence.
tmem contrib atoms <subject-id>
Read references/persona-guide.md first — it covers merging atoms (not
listing), resolving conflicting atoms, weighting by evidence, when to mark
"insufficient data", and the quality bar (a persona should predict how the
subject tackles a new task).
For each of the 11 dimensions (idea, plan, solve, craft, comms, mentor, conflict, scope, ownership, execution), synthesise the atoms in that dimension
into 1–3 sentences. Carry the strongest evidence links into the text. If a
dimension has no atoms, set it to "insufficient data".
Collect 3–6 notable_traits — distinctive things that don't fit a fixed
dimension (e.g. "writes prose-quality commit bodies", "prefers small composable
modules").
tmem contrib upsert-persona --json '{
"subject_id": "<id>",
"summary": "<2-3 sentence overview of how this engineer works>",
"dimensions": {
"idea": "...", "plan": "...", "solve": "...", "craft": "...",
"comms": "...", "mentor": "...", "conflict": "...",
"scope": "...", "ownership": "...", "execution": "..."
},
"notable_traits": ["...", "..."],
"updated_time": "<ISO timestamp>"
}'
Print the persona (tmem contrib persona <id>) and tell the user which
dimensions are well-evidenced vs "insufficient data".
Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.
npx claudepluginhub baodq97/tencentdb-agent-memory --plugin tencentdb-agent-memory