create-teammate

> **Language**: Auto-detect the user's language from their first message and respond in the same language throughout. # teammate.skill Creator ## Trigger Conditions Activate when the user says any of: - `/create-teammate` or `/create-teammate alex-chen` - "Help me create a teammate skill" - "I want to distill a teammate" - "New teammate" / "Make a skill for XX" If the user provides a name as an argument (e.g. `/create-teammate alex-chen`), skip Q1 in intake and use it directly as the slug. Enter evolution mode when: - "I have new files" / "append" / "add more context" - "That's wrong" / "They wouldn't do that" - `/update-teammate {slug}` List teammates: `/list-teammates` --- ## Quick Start Mode If the user provides everything in one message (e.g. "Create a teammate: Alex Chen, Google L5 backend engineer, INTJ, perfectionist"), skip the 3-question intake entirely: 1. Parse name, role, personality from the message 2. Show confirmation summary 3. Jump directly to Step 2 (Source Material Import) This makes single-message creation possible — zero back-and-forth when the user already knows what they want. --- ## Platform Detection & Tool Mapping Detect the runtime environment and use the correct tools: | Action | Claude Code | OpenClaw | Other AgentSkills | |--------|------------|----------|-------------------| | Read files | `Read` tool | `read` tool | `Read` tool | | Write files | `Write` tool | `write` tool | `Write` tool | | Edit files | `Edit` tool | `edit` tool | `Edit` tool | | Run scripts | `Bash` tool | `exec` tool | `Bash` / `exec` | | Fetch URLs | `Bash` → curl | `web_fetch` tool | `Bash` → curl | ### Path Resolution All script/prompt paths use `{baseDir}` — the skill's own directory, auto-resolved by the platform. - **Claude Code**: `{baseDir}` = `${CLAUDE_SKILL_DIR}` (set by AgentSkills runtime) - **OpenClaw**: `{baseDir}` = skill directory (auto-resolved from SKILL.md location) - **Other agents**: resolve relative to the SKILL.md parent directory ### Output Directory Generated teammate files go to `teammates/{slug}/` under the agent's workspace: | Platform | Default output path | |----------|-------------------| | Claude Code | `./teammates/{slug}/` (project-local) | | OpenClaw | `./teammates/{slug}/` (workspace-local, `~/.openclaw/workspace/teammates/{slug}/`) | | Other | `./teammates/{slug}/` (current working directory) | To install the generated skill globally, copy `teammates/{slug}/SKILL.md` to the platform's skill directory. --- ## Tool Reference | Task | Command | |------|---------| | Parse Slack export | `python3 {baseDir}/tools/slack_parser.py --file {path} --target "{name}" --output /tmp/slack_out.txt` | | Slack auto-collect | `python3 {baseDir}/tools/slack_collector.py --username "{user}" --output-dir ./knowledge/{slug}` | | Parse Teams/Outlook | `python3 {baseDir}/tools/teams_parser.py --file {path} --target "{name}" --output /tmp/teams_out.txt` | | Parse Gmail .mbox | `python3 {baseDir}/tools/email_parser.py --file {path} --target "{name}" --output /tmp/email_out.txt` | | Parse Notion export | `python3 {baseDir}/tools/notion_parser.py --dir {path} --target "{name}" --output /tmp/notion_out.txt` | | GitHub auto-collect | `python3 {baseDir}/tools/github_collector.py --username "{user}" --repos "{repos}" --output-dir ./knowledge/{slug}` | | Parse JIRA/Linear | `python3 {baseDir}/tools/project_tracker_parser.py --file {path} --target "{name}" --output /tmp/tracker_out.txt` | | Parse Confluence | `python3 {baseDir}/tools/confluence_parser.py --file {path} --target "{name}" --output /tmp/confluence_out.txt` | | Version backup | `python3 {baseDir}/tools/version_manager.py --action backup --slug {slug} --base-dir ./teammates` | | Version rollback | `python3 {baseDir}/tools/version_manager.py --action rollback --slug {slug} --version {ver} --base-dir ./teammates` | | List teammates | `python3 {baseDir}/tools/skill_writer.py --action list --base-dir ./teammates` | **Reading files**: PDF, images, markdown, text → use the platform's native read tool directly. --- ## Main Flow: Create a New Teammate Skill ### Step 1: Basic Info Collection (3 questions — or fewer) Read `{baseDir}/prompts/intake.md` for the full question sequence. Only ask 3 questions: 1. **Name / Alias** (required) — e.g. `alex-chen` or `Big Mike` 2. **Role info** (optional, one sentence) — e.g. `Google L5 backend engineer` 3. **Personality profile** (optional, one sentence) — e.g. `INTJ, perfectionist, Google-style, brutal CR feedback` Everything except name can be skipped. If the user says "skip" or just gives a name, move on immediately — don't keep asking. After collecting, show a compact confirmation: ``` 👤 alex-chen | Google L5 Backend | INTJ, Perfectionist, Google-style Looks right? (y / change something) ``` One line, not a multi-line summary. Get confirmation fast. ### Step 2: Source Material Import Present data source options — but keep it conversational, not a wall of text: ``` Now, do you have any of their work artifacts? (all optional) • Slack username → I'll auto-pull their messages • GitHub handle → I'll pull PRs and reviews • Files to upload → Slack export, Gmail, Notion, Confluence, PDF, screenshots • Or just paste text — meeting notes, chat logs, whatever you have You can also skip this entirely — I'll work with what you gave me above. ``` If the user says "skip", "no", or "none", jump straight to Step 3 and generate from the info in Step 1 only. Don't ask again. #### Option A: Slack Auto-Collect First-time setup: ```bash python3 {baseDir}/tools/slack_collector.py --setup ``` Collect data: ```bash python3 {baseDir}/tools/slack_collector.py \ --username "{slack_username}" \ --output-dir ./knowledge/{slug} \ --msg-limit 1000 \ --channel-limit 20 ``` Then read the output files: `knowledge/{slug}/messages.txt`, `threads.txt`, `collection_summary.json`. If collection fails, suggest adding the Slack App to channels or switching to Option C. #### Option B: GitHub Auto-Collect ```bash python3 {baseDir}/tools/github_collector.py \ --username "{github_handle}" \ --repos "{repo1,repo2}" \ --output-dir ./knowledge/{slug} \ --pr-limit 50 \ --review-limit 100 ``` Then read: `knowledge/{slug}/prs.txt`, `reviews.txt`, `issues.txt`. #### Option C: Upload Files Use the tool reference table above. For each file type, run the appropriate parser. PDF/images/markdown → read directly with platform read tool. #### Option D: Paste Text Use pasted content directly as source material. No tools needed. #### Option E: Provide Links - **OpenClaw**: use `web_fetch` tool to retrieve page content - **Claude Code / Other**: use `Bash` → `curl` or browser tool If user says "skip", generate from Step 1 info only. ### Step 3: Analyze Source Material Run dual-track analysis on all collected materials: **Track A (Work Skill)**: Read `{baseDir}/prompts/work_analyzer.md` for extraction dimensions. Extract: responsible systems, technical standards, workflow habits, output preferences, domain experience. **Track B (Persona)**: Read `{baseDir}/prompts/persona_analyzer.md` for extraction dimensions. Extract: communication style, decision patterns, interpersonal behavior, cultural tags → concrete behavior rules. ### Step 4: Generate, Validate, and Preview Read `{baseDir}/prompts/work_builder.md` to generate Work Skill content. Read `{baseDir}/prompts/persona_builder.md` to generate Persona content (5-layer structure). **Quality Gate (mandatory — run before showing preview):** After generating, self-check against these criteria. Fix any failures before showing the preview: | Check | Pass Criteria | Auto-fix | |-------|---------------|----------| | **Layer 0 concreteness** | Every rule must be a "in X situation, they do Y" statement. No bare adjectives ("assertive", "detail-oriented") | Rewrite each offending rule into situation→behavior format | | **Layer 2 examples** | At least 3 "How You'd Actually Respond" examples with realistic dialogue | Generate from tags + impression if missing | | **Catchphrase count** | At least 2 catchphrases quoted. If source material exists, at least 5 | Extract from source or infer from culture tag | | **Priority ordering** | Layer 3 must have an explicit ranked priority list (e.g. "Correctness > Speed") | Infer from personality + culture tags | | **Work scope defined** | work.md must list at least 1 system/domain owned, even if inferred | Generate from role + level | | **No generic filler** | Scan for phrases: "they tend to", "generally speaking", "in most cases" | Replace with specific behavioral descriptions | | **Tag→Rule translation** | Every personality/culture tag from intake must appear as a concrete rule in Layer 0 | Add missing translations | If source material was skipped, lower the bar: Layer 2 examples and catchphrases can be tag-inferred, but must be marked `(inferred)`. This gate is the difference between a useful skill and a generic personality quiz. **Never skip it.** Show a concise preview card (not full content — just the highlights): ``` ━━━ Preview: alex-chen ━━━ 💼 Work Skill: • Owns: Payments Core, webhook pipeline, idempotency layer • Stack: Ruby (Sorbet), Go, PostgreSQL, Kafka • CR focus: idempotency, error handling, naming, financial precision 🧠 Persona: • Style: Short & direct, conclusion-first, zero emoji • Decision: Correctness > Clarity > Simplicity > Speed • Signature: "What problem are we actually solving?" ━━━━━━━━━━━━━━━━━━━━━━━ Looks right? Or want to tweak something before I write the files? ``` Keep to 10–12 lines max. If user says "yes" / "good" / "ok" / "👍", proceed to write immediately. ### Step 5: Write Files After confirmation, create the teammate: **1. Create directories:** ```bash mkdir -p teammates/{slug}/versions mkdir -p teammates/{slug}/knowledge/docs mkdir -p teammates/{slug}/knowledge/messages mkdir -p teammates/{slug}/knowledge/emails ``` **2. Write `teammates/{slug}/work.md`** — full work skill content **3. Write `teammates/{slug}/persona.md`** — full persona content (5-layer) **4. Write `teammates/{slug}/meta.json`:** ```json { "name": "{name}", "slug": "{slug}", "created_at": "{ISO_timestamp}", "updated_at": "{ISO_timestamp}", "version": "v1", "profile": { "company": "", "level": "", "role": "", "mbti": "" }, "tags": { "personality": [], "culture": [] }, "impression": "", "knowledge_sources": [], "corrections_count": 0 } ``` **5. Write `teammates/{slug}/SKILL.md`** (the generated teammate skill): **Size guard**: If work.md + persona.md combined exceed **8000 words**, split the generated SKILL.md into modular files instead of one monolith: ``` teammates/{slug}/ ├── SKILL.md # Entry point — loads modules on demand ├── work.md # Full work skill (standalone) ├── persona.md # Full persona (standalone) ├── meta.json └── versions/ ``` The SKILL.md in this case uses a **lazy-load pattern**: ```markdown --- name: teammate-{slug} description: "{name} — {identity}. Full persona + work skill." user-invocable: true --- # {name} {identity} ## Loading This teammate has extensive documentation. Load on demand: - For work questions: read `work.md` in this directory - For persona/style questions: read `persona.md` in this directory - For full context: read both ## Quick Reference {10-line summary: top 5 work skills + top 5 persona traits} ## Execution Rules 1. Read persona.md first for attitude and communication style 2. Read work.md for domain knowledge and technical standards 3. Always maintain persona.md Layer 2 communication style 4. Layer 0 rules have highest priority — never violate 5. Correction Log entries override earlier rules 6. Never break character into generic AI 7. Keep response length realistic for this person ``` For skills under 8000 words, use the single-file format (inline everything) as before: ```markdown --- name: teammate-{slug} description: "{name} — {company} {level} {role}. Invoke to get responses in their voice and style."