Stats
Actions
Tags
Use this skill when someone wants to LEARN from engineers they've already profiled — extracting actionable lessons from contributor personas. Trigger on intents like: "what do the top SWEs / engineers / contributors I've profiled have in common", "extract their shared / common capabilities", "turn a contributor's profile into copyable habits, heuristics, or a learnable playbook", "what habits should I copy from <person>", or "compare me / my style to <role-model> dimension-by-dimension and find my gap". Also covers surfacing exemplar teaching quotes across profiled engineers. Operates on existing personas (built via contrib-consolidate); cross-engineer comparison or common-capability synthesis needs ≥2 subjects. Do NOT use for: building, ingesting, or inspecting a single persona's raw contents; commit/trajectory stats; summarizing documents; recalling what you know about the user; or playbooks unrelated to engineer profiles (e.g. team incident-response runbooks).
How this skill is triggered — by the user, by Claude, or both
Slash command
/tencentdb-agent-memory:contrib-synthesizeThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Turn multiple contributor personas into (a) the L4 capability model, (b) a
Turn multiple contributor personas into (a) the L4 capability model, (b) a learnable playbook per subject, and (c) exemplar quotes. The deterministic prevalence math is done by the CLI; you write the interpretation for learners.
Read references/synthesis-guide.md first — the altitude test for playbook
heuristics, how to mine exemplar quotes, the you-vs-role-model gap table, and the
"preliminary until ≥3 subjects" caveat for L4.
tmem contrib capabilities
Prints each common capability with prevalence % and exemplar. If it says "need >=2 subjects", tell the user to ingest + build at least one more subject and stop.
Pull every persona in one call to reason across them:
tmem contrib personas # all subject personas as JSON
For each common capability (from step 1), write 1–2 sentences: what the shared behaviour is, the prevalence ("N/M subjects"), and who exemplifies it best (the exemplar subject), citing one piece of their evidence.
Read the subject persona:
tmem contrib persona <id>
Distil it into ≥8 emulable heuristics — concrete enough to copy. Examples of the right altitude:
Each heuristic must trace to evidence in the persona/atoms.
From the subject's review comments (in their atoms' evidence), surface 3–5 of the best teaching comments verbatim, each with the principle it illustrates. These are lessons, not metrics.
The user already has a self-persona — the plugin built it from their own Claude Code history. Don't make them ingest their own GitHub. Pull both sides directly:
tmem persona # the user's existing self-persona (conversation-derived)
tmem contrib persona <id> # the role model's 11-dimension persona
These use different schemas (the self-persona is user/feedback/project style,
not the 11 GitHub dimensions), so this is a qualitative gap analysis, not a
1:1 table. For each role-model dimension, state what their habit is, what the
user's persona suggests about that area (or "not evidenced" if the self-persona
is silent), and one concrete thing to adopt. Lead with the gaps worth closing.
If the user instead wants to compare two profiled contributors (peer/team), use
the deterministic table command: tmem contrib compare <id-a> <id-b>.
tmem contrib trajectory <id>
This prints per-year cadence + style (commits, PRs, reviews given, avg commit subject length, conventional-prefix %). Narrate the arc: when output scaled, when commit style matured, when they shifted from authoring toward review. Be honest that this measures cadence/style, not PR size (LOC is not available).
npx claudepluginhub baodq97/tencentdb-agent-memory --plugin tencentdb-agent-memorySearches 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.