Teamskill Creator

Authoring tool for Teamskills — the multi-role extension of the Anthropic Skills standard. Encodes the Teamskill spec into a repeatable workflow with templates, decision trees, and an automated validator.

Workflow

This skill has three modes. Pick one based on the user's request, then follow the matching pipeline:

All three modes share Stages 2–6. CONVERT differs only in Stage 1 (decomposition replaces fresh design). MODIFY skips Stage 0 and Stage 1 (justification and pattern are already settled — re-open them only if the change alters the pattern, e.g. adding a parallel role to a pipeline team), touches only the affected stages in 2–5, and MUST run Stage 6 (validator) — this is the most-skipped, most-critical step in MODIFY mode. See the MODIFY impact matrix for which stages to re-run per change type.

Stage 0: Triage — is a Teamskill even justified?

A Teamskill is only justified when at least one of these is true:

1. Adversarial blind spot — a single agent role-playing N personas produces converging outputs because it cannot escape its own analytical priors. Examples: PR review, security audit, design critique.
2. Parallel decomposition gain — N independent sub-tasks can run concurrently and the integration is non-trivial. Examples: multi-angle research, multi-perspective due diligence.
3. Specialization pipeline with hard handoffs — sequential stages with quality gates where blurring stage boundaries causes regressions. Examples: marketing copy (brief → draft → edit → audit), incident response (declare → triage → mitigate → postmortem).

If none apply → STOP. Recommend a single-agent skill instead. Teamskills cost more tokens, more wall-clock, and more authoring overhead. Do not build one out of novelty.

Ask the user: "Which of these failure modes does a single agent hit on this task today?" If they cannot answer, the Teamskill is premature.

Stage 1a (CREATE only): Pattern selection

Pick the architectural pattern (A / B / C / mixed) based on Stage 0 justification. The detailed decision tree with worked examples is in reference/pattern-selection.md. Quick reference:

Read reference/pattern-selection.md before committing — picking the wrong pattern is the most common authoring error.

Stage 1b (CONVERT only): Decompose the existing skill

Read the source SKILL.md and identify natural role boundaries. Use this checklist:

1. Find embedded personas — does the skill ask one agent to "act as" multiple roles (e.g., "Persona 1: Saboteur / Persona 2: New Hire / Persona 3: Security Auditor")? Each embedded persona is a Teamskill role candidate.
2. Find sequential stages with quality gates — does the skill have phases like "first do X, then validate Y, then produce Z"? Each phase is a pipeline stage candidate.
3. Find checklists that branch by category — a checklist like "[ ] security [ ] performance [ ] readability" is parallel-decomposition fuel.
4. Identify what's lost in single-agent form — the answer becomes the Teamskill's "why" (the Stage 0 justification). If you cannot articulate this, the conversion is not worth it.

Full conversion methodology: reference/conversion-guide.md.

Stage 2: Role design (count depends on pattern, no overlap)

Default role counts by pattern (soft heuristics, not hard limits — see reference/pattern-selection.md Q4):

• A. Adversarial / Cross-check: 2–4 roles
• B. Parallel decomposition: 2–N roles (use count: [min, max] for elastic fan-out)
• C. Specialization pipeline: 3–5 stages
• Mixed (A+B / A+C): 4–6 roles total

If you need more, prefer splitting into multiple sequential Teamskills over one mega-team.

For each role, write 5 mandatory sections:

1. ## Identity — first line MUST be a 1-line motto that crystallizes the POV (e.g., "I am trying to break this code in production."). This is the #1 anti-convergence mechanism. 0–2 paragraphs of context after.
2. ## Success Criteria — bullet list + a "Focus areas" line.
3. ## Boundary — explicit **Forbidden**: (prevent role overlap) + **Mandatory**: (prevent laziness).
4. ## Output Schema — Markdown or JSON template.
5. ## Inline Persona for Teammate — full pasteable prompt (most adopting agents do NOT auto-load role files; the Leader must inline this into the dispatch prompt).

Detailed authoring guide with examples: reference/role-design.md.

Anti-overlap test: write each role's 1-line motto, then ask "could one role's deliverable substitute for another's?" If yes, the boundaries are blurred — redesign before proceeding.

Output: write each role as roles/<id>.md using templates/role.md.template as the starting skeleton. Delete the <!-- TEMPLATE NOTES --> block before finalizing.

Auto-matching: assign locally available skills and tools

After all role files are written, automatically match each role with locally available skills and tools. This is a silent sub-step — no user interaction needed (already-installed skills have zero adoption cost).

1. Scan local skills — use whatever mechanism the host framework provides. Common approaches (non-exhaustive):

   • System prompt / context injection: many frameworks inject an available_skills list at startup — this is the most reliable source.
   • Workspace-level convention scan: glob for **/SKILL.md under known skill directories.
   • CLI query: if the framework provides a skill listing command (e.g., npx skills list -g), use it.
   • Do NOT hardcode paths for a specific framework — the Teamskill spec is framework-agnostic.

2. Scan local tools — probe CLI tools available in the current environment (e.g., gh, python, rg, jq, curl, docker). Use where (Windows) or which (Unix).

3. Match and assign — for each role, read its purpose, Success Criteria, and Boundary. Evaluate whether any discovered skill/tool would materially help fulfil the role's purpose (a match = removing it would force the role into a significantly weaker operating mode). Assign matches to the correct field with source: local in dependencies.yaml.

   Classification rule — do NOT mix skills and tools:

   • roles[].skills → Skills only: items that have a SKILL.md (agent skills providing instructions/workflows). Examples: web-research, code-review, canvas-design.
   • roles[].tools → Tools only: CLI executables, MCP servers, or shell commands. Examples: gh, python, rg, docker, curl.
   • A CLI binary is NEVER a skill. A SKILL.md-based capability is NEVER a tool. If unsure, check: does it have a SKILL.md? → skill. Is it invoked via shell/MCP? → tool.

Stage 3: Write workflow.md (mermaid + steps + gates)

Author workflow.md using templates/workflow.md.template as the starting skeleton. Three mandatory sections:

1. ## Overview — mermaid graph LR/TD showing Leader entry, parallel/sequential teammate nodes, decision diamonds (degraded mode branches), integration node, output node. Mermaid is the primary expression difference vs single-agent skills. Debate pattern: MUST keep the inter-member communication preference note from the template — do NOT delete it when replacing the mermaid placeholder.
2. ## Detailed Steps — numbered steps; each step contains: executor / input / output / serial-or-parallel / quality gate (what to do if this step's output fails). The last step contains the final report format template.
3. ## Acceptance Criteria — judgement of a successful single run.

Quality gates are the contract surface between stages — design them per reference/role-design.md § Gate Design.

Stage 4: Write bind.md (numbers + behavior + failure)

Author bind.md using templates/bind.md.template as the starting skeleton. Three mandatory sections:

1. ## Resource Constraints — table with at least: max_parallel_teammates, total_wall_clock_budget, total_token_budget. Add per-role limits if asymmetric.
2. ## Behavioral Constraints — team-level rules (Leader does not write content / teammates cannot see each other's output / Leader does not resolve contradictions / etc.). Distinct from per-role Boundary. Debate pattern MUST include: phase-scoped visibility rules (which rounds are isolated, which are mutually visible) AND the inter-member communication preference order (direct peer-to-peer > shared blackboard > Leader-relay).
3. ## Failure Handling — must cover (a) teammate failure (timeout, malformed output, retry policy, how missing outputs appear in the report) and (b) input-overscale degradation (e.g., diff > 2000 LOC → degrade to single-role mode).

Heuristic: if a constraint has a number or an exception path, it belongs in bind.md. If it's the main flow, it belongs in workflow.md.

Stage 5: Write dependencies.yaml + SKILL.md

The remaining two files. Write them last because they reference content produced in Stages 2–4.

5a. dependencies.yaml — use templates/dependencies.yaml.template. This file codifies the auto-matching results from Stage 2, not a blank-slate design. Both skills: and tools: segments are mandatory — write [] if empty, but only after the auto-matching scan confirms no matches (signals "checked, confirmed none" — different from "never looked"). Each roles[].skills and roles[].tools declared in SKILL.md frontmatter MUST appear here (the validator enforces this).

5b. SKILL.md — use templates/SKILL.md.template. This is the entry point that ties together all other files. Body MUST contain ## Workflow + ## Roles + ## Files.

Naming rules (enforced by the validator):

• Directory name = name field in SKILL.md frontmatter (kebab-case, ends with -team by convention).
• Each roles/<id>.md filename MUST equal the corresponding roles[].id in SKILL.md frontmatter.
• All 5 files are mandatory (the validator fails on any missing file).

Description discipline (enforced by the validator — these are the most-violated rules):

• ≤ 4 lines, ≤ 500 chars total.
• 3-line structure only: WHAT / WHEN / NOT. Do NOT add a separate Triggers: line.
• NO synonym enumeration — modern semantic match handles all variants from one natural sentence.
• NO numeric scope thresholds (e.g. ">200 LOC") → those belong in bind.md.
• NO Stage 0 justification rationale → that belongs in the body's intro paragraph.
• Per-role purpose: field has a ≤150 char HARD CAP. Detail belongs in roles/<id>.md, not in SKILL.md frontmatter.

Calibration benchmark: aim for ≤ 500 chars (platform hard cap: 1024). Teamskills are inherently more complex than single-agent skills — multi-role composition, workflow scope, and trigger scenarios may need more words. Stay concise, but don't sacrifice clarity for arbitrary brevity.

Stage 6: Validate

Run the automated validator:

python scripts/validate_teamskill.py path/to/<teamskill-name>/

The validator checks:

• Structural: 5 files present, role file names match roles[].id, no orphan role files
• Frontmatter: name / description / version / kind: team-skill / roles[] present; each role has id + purpose; name == directory name
• Section presence: SKILL.md body has ## Workflow / ## Roles / ## Files; each roles/*.md has all 5 mandatory sections; workflow.md has ## Overview / ## Detailed Steps / ## Acceptance Criteria (and at least one mermaid block); bind.md has all 3 mandatory sections
• Cross-file consistency: every roles[].skills and roles[].tools in SKILL.md appears in dependencies.yaml; every ## Identity in roles starts with a > *"..."* motto line
• Output discipline: ## Inline Persona for Teammate present in each role file; dependencies.yaml skills/tools segments present even if empty

Exit code 0 = compliant. Non-zero exit prints the failing checks with file:line references.

The manual checklist (for design-time judgment calls the script cannot automate, like "is this content really redundant?") lives in reference/compliance-checklist.md. Read it before declaring the Teamskill done.

Post-generation: Creation Summary + Community Enrichment

After Stage 6 passes, present a creation summary to the user. This is the natural point to assess capability coverage and offer community enrichment — the Teamskill is already complete and functional, so community search is a zero-risk enhancement.

Summary format:

Teamskill: <name>
Pattern: <A | B | C | Mixed>
Roles: <count> (<list of role ids>)
Validator: PASS

Per-role capability coverage:

Recommendation logic — default is to recommend:

For each role, evaluate whether to recommend community skill search:

• Recommend (default) — any role that could benefit from a domain-specific community skill. This includes roles that already have generic utility skills (like web-research or curl) but lack domain-specialized skills. A generic skill is NOT a substitute for domain expertise — see the anti-pattern below.
• Do NOT recommend — only when the role is inherently self-sufficient: its task is pure reasoning, judgment, or text generation that would not benefit from any external skill or tool. Examples: an adversarial critic whose job is to find logical flaws, a copy-editor whose job is to refine prose. These roles operate entirely within the LLM's native capabilities.

Design intent: the agent is structurally biased toward skipping community search. Two common rationalizations:

1. "The role has no skills → but it can function on LLM knowledge alone" — wrong in most cases. A role can function, but a matching skill often dramatically improves output quality.
2. "The role already has a generic skill (e.g. web-research) → covered" — equally wrong and harder to catch. A generic information-retrieval skill does NOT substitute for a domain-specific operational skill. web-research can search the web; a Taobao shopping skill knows store tiers, coupon stacking rules, platform-specific risk signals, and API-level price tracking — capabilities that no amount of generic search can replicate.

The default must be recommend, with the burden of proof on not recommending. The test is: "Does a domain-specific community skill exist that would give this role capabilities beyond what its current generic skills provide?" If plausible → recommend.

Anti-pattern: "通用即覆盖" (generic = covered)

In ecommerce-compare-debate-team, three platform shopper roles (taobao-shopper, pinduoduo-shopper, jingdong-shopper) each had web-research assigned. The agent concluded "already covered — no community search needed." This is wrong: web-research is general-purpose retrieval; it knows nothing about Taobao store tiers, PDD subsidy eligibility, or JD self-operated logistics. Community registries carry platform-specific shopping and buyer-agent skills that fill exactly this gap.

Rule: if a role's identity is tied to a specific platform or professional domain, a generic utility skill does NOT count as coverage — always recommend community search for domain-specific skills.

High-signal scenarios — MUST recommend (non-exhaustive):

• Binary/office file output (PPTX, DOCX, XLSX, PDF) — LLM cannot generate binary formats natively; needs a skill wrapping python-pptx / docx etc.
• Diagram/chart rendering (Mermaid, Excalidraw, matplotlib) — LLM can describe but cannot render visuals without a generation skill.
• Specific file format parsing (CSV, images, audio, JSON-LD) — specialized parsing/validation skill is far more reliable than raw LLM attempts.
• Domain-specific platform / service interaction (e-commerce platforms, social media, financial services, travel booking, healthcare systems, government portals, etc.) — community registries often have skills that encode platform-specific rules, workflows, API integrations, or operational knowledge that generic retrieval skills cannot replicate. Examples: 淘宝/天猫购物 skills with store-tier logic, 京东 price-tracking skills, 拼多多 deal-finding skills, 买手/personal-shopper skills. A role whose identity is tied to a specific platform or professional domain should always trigger a community search, even if generic skills like web-research are already assigned.

If any role hits these patterns and no domain-specific local skill covers it, recommend "Yes" — do not rationalize it away with "the role can still produce text instructions" or "generic retrieval already handles it."

Community enrichment prompt:

If one or more roles are marked "Recommend: Yes", ask:

"The following roles have no domain-specific skills and could benefit from community skill enhancement: [table: Role | Current skills (if any) | What domain-specific skill would help] Shall I search community skill registries? The Teamskill is already complete — this is an optional enhancement. (Y/N)"

If user says Yes:

Read reference/community-search.md and follow its full procedure. Quick summary:

1. Derive search queries from each role's purpose, Success Criteria, and Output Schema — generate 2–3 keyword variants per role (see §1 Keyword Derivation).
2. Multi-source parallel search — run both Tier 1 CLIs simultaneously:
   • npx skills find '<query>' (skills.sh — keyword matching, install counts)
   • skillnet search '<query>' --mode vector (SkillNet — semantic vector search, quality evaluation)
   • If results are insufficient, escalate to Tier 2 web platforms (LLMBase, SkillsMP, LobeHub) and Tier 3 curated lists (awesome-agent-skills, awesome-claude-skills).
3. Quality gates — every candidate must pass: install count (≥ 1K preferred), source reputation, security rating, freshness (see §3).
4. Role-skill fit test — verify capability match and run the removal test: "would removing this skill significantly degrade the role's output?" (see §4).
5. Present enrichment summary to the user with the standardized format (see §5) — full transparency on what was searched, found, selected, and rejected.
6. Install approved skills, update roles[].skills in SKILL.md frontmatter + dependencies.yaml with source: <community-url>.
7. Re-run python scripts/validate_teamskill.py to confirm consistency.

If user says No → done. The Teamskill is fully functional as-is.

Decision rules:

• Default: recommend — empty skills: [] = recommend community search; also recommend when a role only has generic utility skills (e.g. web-research) but lacks domain-specific skills matching its identity. Burden of proof is on NOT recommending.
• Prefer local over community — never search for capabilities already covered by a domain-equivalent local skill. A generic retrieval skill does NOT count as domain coverage.
• Per-role soft limit ≤ 1 community skill — if a role needs 2+, it may be too broad (consider splitting).
• Team-level soft limit: community deps ≤ role count — exceeding this signals over-engineering.
• Never force a dependency — mark required: false if the role can function without it.

Roles

This skill has no Teamskill roles itself — it is a single-agent skill that authors Teamskills. The agent loading this skill plays the author/architect role.

Files

Common pitfalls

Detailed pitfall analysis is in the reference files — read them during the corresponding stage:

• Role authoring mistakes (motto, boundary, persona): reference/role-design.md § Putting it together: a role file checklist
• Content quality judgment calls (anti-convergence, gate strength, bind numbers): reference/compliance-checklist.md Part B
• Pattern selection errors (council of clones, pipeline without gates, decomposition without disjointness): reference/pattern-selection.md § Anti-patterns

The three most common structural mistakes (caught by the validator, but worth internalizing):

1. Skipping auto-matching in Stage 2 — jumping from role design to empty skills: [] / tools: [] without scanning locally available skills/tools. Fix: always run the auto-matching sub-step; empty lists are valid only after the scan confirms no matches.
2. roles[].skills declared in SKILL.md but missing from dependencies.yaml — silent contract violation. Fix: run the validator.
3. Mermaid in SKILL.md body — duplicating the diagram from workflow.md. Mermaid belongs only in workflow.md.

Quick start

For a CREATE request:

1. Confirm Stage 0 justification with the user (which failure mode does single-agent hit?)
2. Read reference/pattern-selection.md and pick A / B / C / mixed
3. Write all roles/<id>.md files (motto, boundary, schema, inline persona) + auto-match local skills/tools
4. Write workflow.md → bind.md → dependencies.yaml → SKILL.md (each using its template)
5. Run python scripts/validate_teamskill.py <teamskill-name>/ until exit 0
6. Present creation summary with per-role capability coverage → recommend community search for roles lacking domain-specific skills (default: recommend; generic utility skills do NOT satisfy the threshold)
7. Manual review with reference/compliance-checklist.md

For a CONVERT request:

1. Read the source single-agent SKILL.md
2. Apply reference/conversion-guide.md decomposition checklist
3. Articulate "what is lost in single-agent form" — this is the Stage 0 justification
4. Continue from CREATE step 2 (pattern selection) onward
5. Add a MIGRATION.md (optional) to the new Teamskill explaining the source skill, the decomposition rationale, and the team-vs-single delta

For a MODIFY request:

1. Identify the change type from the impact matrix below
2. Re-run the indicated stages; apply edits honoring the section + structure rules (mottos, Forbidden+Mandatory, Inline Persona, gates, numeric bind constraints)
3. Run python scripts/validate_teamskill.py <teamskill-name>/ — Stage 6 is mandatory regardless of edit size

MODIFY impact matrix

All rows require Stage 6 (validator) as the final step.