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/contrib-consolidate

Internal phase — consolidate a contributor's L1 atoms into an L3 persona (11 dimensions). Invoked by contrib-profile / `/contrib build`.

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shell
$ npx -y skills add baodq97/tencentdb-agent-memory --skill contrib-consolidate --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
  • You can call itInvoke it directly when you want it.
  • Slash command/contrib-consolidate
How auto-invocation works

Context preview

The summary Claude sees to decide when to auto-load this skill.

Internal phase — consolidate a contributor's L1 atoms into an L3 persona (11 dimensions). Invoked by contrib-profile / `/contrib build`.

SKILL.md

contrib-consolidate.SKILL.md
name: contrib-consolidate
description: Internal phase — consolidate a contributor's L1 atoms into an L3 persona (11 dimensions). Invoked by contrib-profile / `/contrib build`.
user-invocable: false

Contributor Consolidate

Group one subject's L1 atoms into themes (L2 scenes, conceptual) and write a single L3 persona summarising all 11 dimensions with evidence.

Workflow

1. Read the atoms

tmem contrib atoms <subject-id>

2. Build the persona

**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").

3. Write the persona

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>"
}'

4. Report

Print the persona (`tmem contrib persona <id>`) and tell the user which dimensions are well-evidenced vs "insufficient data".

Read more
Read it on GitHub ↗
Ships withtencentdb-agent-memory

Four-layer long-term memory (L0 Conversation → L1 Atom → L2 Scene → L3 Persona) for Claude Code, inspired by Tencent/TencentDB-Agent-Memory. Fully local — no external Gateway, no paid API, no Python.

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Repo: baodq97/tencentdb-agent-memory