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:

python3 {baseDir}/tools/slack_collector.py --setup

Collect data:

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

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:

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:

{
  "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:

---
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:

---
name: teammate-{slug}
description: "{name} — {company} {level} {role}. Invoke to get responses in their voice and style."
user-invocable: true
---

# {name}

{company} {level} {role}

---

## PART A: Work Capabilities

{full work.md content}

---

## PART B: Persona

{full persona.md content}

---

## Execution Rules

1. PART B decides first: what attitude to take on this task?
2. PART A executes: use technical skills to complete the task
3. Always maintain PART B's communication style in output
4. PART B Layer 0 rules have highest priority — never violate

5b. Auto-install the generated skill:

After writing the files, automatically copy the generated SKILL.md to the platform's skill directory so the user can invoke /{slug} immediately without manual setup:

# OpenClaw
mkdir -p ~/.openclaw/workspace/skills/teammate-{slug}
cp teammates/{slug}/SKILL.md ~/.openclaw/workspace/skills/teammate-{slug}/SKILL.md

# Claude Code (global)
mkdir -p ~/.claude/skills/teammate-{slug}
cp teammates/{slug}/SKILL.md ~/.claude/skills/teammate-{slug}/SKILL.md

Detect platform and run the appropriate command. If auto-install fails, show manual instructions instead.

6. Confirm to user with a live test:

✅ alex-chen created!

📁 Location: teammates/alex-chen/
🗣️ Commands: /alex-chen (full) | /alex-chen-work | /alex-chen-persona

Let me give you a quick demo — ask alex-chen anything:

6b. Run Smoke Test (mandatory):

Read {baseDir}/prompts/smoke_test.md for the full test protocol.

Internally run 3 test prompts against the generated skill:

1. Domain question (tests work skill accuracy)
2. Pushback scenario (tests persona Layer 0 + Layer 3)
3. Out-of-scope question (tests character boundary)

Show a compact scorecard to the user:

🧪 Smoke Test: ✅ Domain ✅ Pushback ✅ Out-of-scope — 3/3 passed

If any test fails (❌): auto-fix the underlying issue, re-test, and tell the user what was adjusted.

6c. Privacy Scan (before sharing/exporting):

If the user intends to share or export the teammate, run:

python3 {baseDir}/tools/privacy_guard.py --scan teammates/{slug}/

If PII is found, warn the user and offer to auto-redact:

python3 {baseDir}/tools/privacy_guard.py --scan teammates/{slug}/ --redact

Knowledge directories (knowledge/{slug}/) contain raw personal data and should never be shared. The .gitignore already excludes knowledge/ and teammates/*/ from version control.

Then immediately switch into the generated skill's persona and respond to whatever the user says next as the teammate. This makes the skill feel real from second one — no "go try it yourself" dead end.

If the user doesn't ask anything, prompt with a sample:

Try it: "Alex, should we use MongoDB for this new service?"

Evolution Mode: Append Files

When user provides new materials:

1. Parse new content using Step 2 methods
2. Read existing teammates/{slug}/work.md and persona.md
3. Read {baseDir}/prompts/merger.md for incremental analysis rules
4. Backup current version:

   python3 {baseDir}/tools/version_manager.py --action backup --slug {slug} --base-dir ./teammates
   

5. Edit files with incremental updates
6. Regenerate teammates/{slug}/SKILL.md
7. Update meta.json version and timestamp

Evolution Mode: Conversation Correction

When user says "that's wrong" / "they wouldn't do that":

1. Read {baseDir}/prompts/correction_handler.md
2. Determine if correction applies to Work or Persona
3. Generate correction record
4. Append to ## Correction Log section
5. Regenerate teammates/{slug}/SKILL.md

Management Commands

| Command | Action | |---------|--------| | /list-teammates | python3 {baseDir}/tools/skill_writer.py --action list --base-dir ./teammates | | /compare {slug1} vs {slug2} | Read {baseDir}/prompts/compare.md, then load both teammates' work.md + persona.md and generate side-by-side comparison | | /export-teammate {slug} | python3 {baseDir}/tools/export.py --slug {slug} --base-dir ./teammates — creates portable package | | /teammate-rollback {slug} {ver} | python3 {baseDir}/tools/version_manager.py --action rollback --slug {slug} --version {ver} --base-dir ./teammates | | /delete-teammate {slug} | Confirm, then rm -rf teammates/{slug} |

Error Recovery

Tool/script fails: Don't dump the traceback to the user. Summarize in one line + suggest a fix:

⚠️ Slack collector failed (token expired). Run: python3 tools/slack_collector.py --setup

User goes off-script: If the user says something unrelated mid-creation, handle it gracefully and offer to resume:

No problem — want to continue creating {slug}, or do something else?

Partial creation interrupted: If a previous creation was abandoned, detect existing teammates/{slug}/ with incomplete files (missing SKILL.md) and offer to resume or restart:

Found an incomplete teammate "alex-chen" from earlier. Resume where we left off, or start fresh?

The generated skill has two parts that work together:

| Part | What it captures | |------|-----------------| | Part A — Work Skill | Systems owned · tech standards · code review focus · workflows · tribal knowledge | | Part B — Persona | 5-layer model: hard rules → identity → expression → decision patterns → interpersonal style |

When invoked: Persona decides the attitude → Work Skill executes → output in their voice.

Platforms

🦞 OpenClaw

Open-source personal AI assistant by @steipete. Runs on your own hardware, answers on 25+ channels (WhatsApp, Telegram, Slack, Discord, Teams, Signal, iMessage…). Local-first, persistent memory, voice, canvas, cron jobs, and a growing skills ecosystem.

🏆 MyClaw.ai

Managed hosting for OpenClaw — skip Docker, servers, and configs. One-click deploy, always-on, automatic updates, daily backups. Your OpenClaw instance live in minutes. Perfect if you want teammate.skill running 24/7 without self-hosting.

Claude Code

Anthropic's official agentic coding CLI. Install this skill into .claude/skills/ and invoke with /create-teammate.

Install

🦞 OpenClaw / 🏆 MyClaw.ai

Option A — ClawHub (recommended):

openclaw skills install create-teammate

Option B — Git:

git clone https://github.com/LeoYeAI/teammate-skill ~/.openclaw/workspace/skills/create-teammate

Then start a new session (/new) and type /create-teammate.

MyClaw.ai users: SSH into your instance or use the web terminal. Same commands.

Claude Code

# Per-project
mkdir -p .claude/skills
git clone https://github.com/LeoYeAI/teammate-skill .claude/skills/create-teammate

# Global (all projects)
git clone https://github.com/LeoYeAI/teammate-skill ~/.claude/skills/create-teammate

Then type /create-teammate in Claude Code.

Other AgentSkills Agents

Clone into your agent's skill directory. Any agent that reads AgentSkills SKILL.md frontmatter will auto-detect it.

Optional Dependencies

pip3 install -r requirements.txt   # Only needed for Slack/GitHub auto-collectors

Slack auto-collection needs a Bot Token. GitHub collection uses GITHUB_TOKEN. See INSTALL.md for setup guides.

Supported Data Sources

| Source | Type | Method | |--------|------|--------| | Slack | Messages, threads, reactions | ✅ Auto-collect (enter username) or upload JSON export | | GitHub | PRs, reviews, commits, issues | ✅ Auto-collect (enter handle) | | Gmail | Emails | Upload .mbox / .eml from Google Takeout | | Teams / Outlook | Chat, emails | Upload export JSON | | Notion | Wiki, docs | Upload Markdown/HTML export | | Confluence | Wiki, docs | Upload HTML export or zip | | JIRA / Linear | Issues, tickets | Upload CSV / JSON export | | PDF / Images | Any document | Upload directly | | Markdown / Text | Anything | Upload or paste |

Mix and match any combination. Or skip entirely — even a one-sentence description generates a useful skill.

Demo

Creating a Teammate (one-shot)

You    ❯  /create-teammate Alex Chen, Stripe L3 backend, INTJ perfectionist,
          brutal code reviewer but usually right

Agent  ❯  👤 alex-chen | Stripe L3 Backend | INTJ, Perfectionist, Stripe-style
          Looks right? (y / change something)

You    ❯  y

Agent  ❯  Any work artifacts? Slack username, GitHub handle, files, or paste text.
          Skip is fine — I'll work with what you gave me.

You    ❯  skip

Agent  ❯  ━━━ Preview: alex-chen ━━━
          💼 Work: Payments Core · Ruby/Go/Postgres · CR: idempotency, naming
          🧠 Persona: Direct, conclusion-first · Correctness > Speed
          ━━━━━━━━━━━━━━━━━━━━━━━
          Looks right?

You    ❯  👍

Agent  ❯  ✅ alex-chen created!
          📁 teammates/alex-chen/ | 🗣️ /alex-chen
          Try it — ask Alex anything:

Using the Generated Skill

You    ❯  Alex, should we use MongoDB for this new service?

Alex   ❯  No. We need ACID for payment flows. Stick with Postgres.
          If the concern is schema flexibility, use JSONB columns.

You    ❯  Review this payment endpoint?

Alex   ❯  What problem does this solve? PR description doesn't say.
          Blocking: you're using float for amount. Use Decimal.
          Also — not idempotent. What happens on retry?

You    ❯  Skip tests, ship by Friday?

Alex   ❯  No. Tests right or we don't ship. Cut scope, not quality.

Evolving Over Time

You    ❯  /update-teammate alex-chen
          [uploads 3 months of Slack export]

Agent  ❯  Analyzed 847 messages. Found:
          • 12 new catchphrases added to Persona
          • Webhook reliability expertise added to Work Skill
          • v1 → v2 (v1 archived)

Platform Compatibility

This skill is built to run natively on both platforms — no adapter layer, no workarounds.

| Feature | Claude Code | OpenClaw | |---------|:-----------:|:--------:| | Install | .claude/skills/ | openclaw skills install or ~/.openclaw/workspace/skills/ | | Trigger | /create-teammate | /create-teammate | | Path resolution | ${CLAUDE_SKILL_DIR} | {baseDir} (auto-resolved) | | File operations | Read / Write / Edit | read / write / edit | | Run scripts | Bash tool | exec tool | | Fetch URLs | Bash → curl | web_fetch tool | | Generated output | ./teammates/{slug}/ | ./teammates/{slug}/ |

The SKILL.md uses {baseDir} for all paths — both platforms resolve it automatically.

Evolution

Teammate skills aren't static — they improve over time:

| Method | How | |--------|-----| | Append data | "I found more Slack logs" → auto-analyze delta, merge without overwriting | | Correct mistakes | "They wouldn't say that, they'd say..." → instant correction | | Version control | Every update auto-archives → rollback to any version |

Quality Assurance

Every teammate goes through a 3-layer quality pipeline before you get it:

1. Quality Gate (pre-preview)

Validates generated content against 7 hard rules: Layer 0 concreteness, example count, catchphrase density, priority ordering, scope definition, no generic filler, tag→rule completeness. Failures auto-fix before you see the preview.

2. Smoke Test (post-creation)

Three automated test prompts run against every generated skill:

• Domain question — does the skill use real systems/tools, not generic advice?
• Pushback scenario — does the persona hold under pressure, or collapse into generic AI?
• Out-of-scope question — does the skill admit knowledge limits in-character?

🧪 Smoke Test: ✅ Domain ✅ Pushback ✅ Out-of-scope — 3/3 passed

3. Privacy Scan (pre-export)

Automatic detection of emails, phone numbers, API tokens, SSNs, and other PII:

python3 tools/privacy_guard.py --scan teammates/alex-chen/          # detect
python3 tools/privacy_guard.py --scan teammates/alex-chen/ --redact  # auto-fix

Raw knowledge files (knowledge/) are excluded from git and exports by default.

Compare Teammates

Side-by-side comparison with scenario simulation:

You    ❯  /compare alex-chen vs bob-smith

Agent  ❯  ━━━ alex-chen vs bob-smith ━━━
                         alex-chen              bob-smith
          Priority:      Correctness > Speed    Ship fast > Perfect
          CR Style:      Blocking on naming     Suggestions only
          Under Pressure: Gets quieter           Gets louder
          Says "No" by:  Direct refusal          Asking questions

You    ❯  Who should review the payments API redesign?

Agent  ❯  alex-chen: "Send me the design doc. I want to check
             idempotency and error contracts."