colleague-distillation

name: colleague-distillation description: Distill a colleague into a reusable AI skill (work + persona) using tool connections — Slack, Slack AI, Jira, GHE, Bitbucket, Confluence, SharePoint, Teams, Outlook, Notion, Linear, Google Docs, and more — without manual paste. Use when the user wants a colleague skill, digital twin of a coworker, or capture of someone's technical voice from workplace systems. Requires tool_connections + 10xProductivity verified_connections (or equivalent .env).

10xProductivity skill: This file is the Cursor entry point for colleague distillation when working in this repo.

Colleague distillation (tool-backed)

Purpose

Produce a colleague skill: structured work knowledge (systems, standards, review style) plus persona (tone, decisions, interpersonal habits), using APIs and search already wired in tool_connections / 10xProductivity — not hand-pasted exports.

Output layout matches the open colleague-skill convention so results can coexist with that generator:

• colleagues/{slug}/work.md
• colleagues/{slug}/persona.md
• colleagues/{slug}/meta.json
• colleagues/{slug}/SKILL.md (merged invocable skill)

Optional: Clone colleague-skill for its prompts/work_analyzer.md, persona_analyzer.md, work_builder.md, persona_builder.md if you want identical extraction templates; this skill defines what to fetch and where to write.

Prerequisites

1. Load the relevant 10xProductivity connection docs under tool_connections/ and any allowed private recipes under $TENX_PRIVATE_DIR/personal/.
2. Load $TENX_PRIVATE_DIR/verified_connections.md — only call tools listed there (or documented in 10xProductivity/tool_connections/ / personal/).
3. For Jira, use the verified Jira connection documentation and recipes in this repo.
4. Credentials: source or load $TENX_PRIVATE_DIR/.env (or project .env) before curl / scripts. Never commit secrets.

Cursor vs Claude Code

Use the same slug and folder layout in both; only the skill install path differs.

Slug rules

• Slug = unique directory name: michael_donnelly, michael_donnelly_2, … (ASCII, underscores).
• Collisions: Same slug overwrites an existing colleague folder. Disambiguate with _2, _3, or a distinct codename.
• Store display name and aliases in meta.json, not only in the slug.

Phase 1 — Resolve identity

Before searching, pin who the colleague is:

1. Active Directory (if configured in tool_connections): resolve email, manager chain, department — use for Jira/Slack account mapping when IDs are unknown.
2. Slack: From verified_connections.md, use Slack API recipes to resolve @handle → user id (U…) for from:@user / from:U… search syntax.
3. Jira: Resolve accountId (assignee, reporter, comment author) via Jira user search API — see jira skill.

Record: slack_user_id, jira_account_id, email, ad_cn (as available).

Phase 2 — Pull source material (priority order)

Gather raw excerpts (save under colleagues/{slug}/knowledge/raw/ as .md or .json snippets) with source + URL/ticket/channel + date in each chunk header. Cap volume per source (e.g. last 90–180 days) unless the user asks for full history.

Tier A — Highest signal for “how they work and sound”

Tier B — Depth and standards

Tier C — Optional / role-specific

Tier D — Do not rely on for persona without extra care

• Raw git blame without PR context — noisy.
• HR systems — use only for title/team if needed, not personality inference.

Phase 3 — Synthesize (work vs persona)

Work (work.md): Systems, stacks, coding/review conventions, doc habits, Jira/workflow patterns, incident/release behavior — cite patterns, not one-off jokes.

Persona (persona.md): Use a layered structure compatible with colleague-skill:

1. Layer 0 — Hard rules (non-negotiables: respect, no slurs, no real harassment simulation beyond professional friction the user explicitly asked for).
2. Identity — role, scope, team context.
3. Expression — vocabulary, sentence length, directness, humor.
4. Decisions — risk posture, escalation, “how they say no.”
5. Interpersonal — meetings, async, conflict.

Grounding: Prefer quoted paraphrases with source pointers; flag low-confidence traits when sample size is small.

Phase 4 — Write artifacts

meta.json (minimal)

{
  "name": "Display Name",
  "slug": "michael_donnelly",
  "created_at": "<ISO8601 UTC>",
  "updated_at": "<ISO8601 UTC>",
  "version": "v1",
  "profile": {
    "company": "",
    "level": "",
    "role": "",
    "email": ""
  },
  "ids": {
    "slack_user_id": "",
    "jira_account_id": ""
  },
  "knowledge_sources": ["slack", "jira", "ghe"],
  "corrections_count": 0
}

SKILL.md (invocable)

YAML frontmatter:

---
name: colleague_{slug}
description: "<Name> — distilled work + persona (tool-sourced)."
user-invocable: true
---

Body: short intro + full work.md + full persona.md + run rules:

1. Persona decides attitude; work block executes the task.
2. Output matches persona expression.
3. Layer 0 never violated.

Optional: also write work_skill.md / persona_skill.md with names colleague_{slug}_work / colleague_{slug}_persona if your host expects split invocations (see colleague-skill skill_writer.py).

Consent and safety

• Build skills only for legitimate work purposes and policy-compliant use of company tools.
• Do not exfiltrate secrets, PII bundles, or restricted content into colleagues/ — redact tokens, customer data, and health/financial identifiers.
• When unsure whether content is allowed in a repo, ask the user before writing.

Outputs checklist

• colleagues/{slug}/ created with knowledge/raw/ containing sourced excerpts
• work.md and persona.md complete
• meta.json with ids and source list
• SKILL.md merged and invocable
• User told how to invoke (/{slug} or host-specific command) and how to disambiguate duplicates (_2, _3)

Related skills

• team_learner — team/domain bootstrap (similar tool sweep, different output shape).
• skill_creation — promote reusable methodology after validation.
• jira, tool_connections — all authenticated access.