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AB teamclaw

Orchestrate a virtual AI software team to build features, fix bugs, or complete any software task. Use when the user wants to delegate work to a multi-role team (developer, architect, QA, etc.), check team progress, review task results, or answer worker clarifications. Triggers: "build me ...", "create a ...", "team status", "assign to team", "teamclaw", "have the team ...", "delegate this to ...", "what are the workers doing".

ClawHub Agent Skills author: topcheer v1.0.0 MIT-0 3 files body ≈ 1 554 tokens Open the sourceclawhub.ai↗ analyzed 5 d ago

As a process B 73/100 · Nearly there — weak spots: consistency, running it twice

GeneratorGitHubInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
73/100
Nearly there
Running it twice w 4
30
Consistency w 8
40
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 73/100

    • 30Running it twice. 6 mutating operations with no state check
    • 40Consistency. Frontmatter name (teamclaw) differs from the folder (teamclaw-orchestrator)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 25 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 1554 tokens
    • 100Progress reporting. Reports progress

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +4Description does not say when NOT to use the skill (false activations)
    • -5TODO / placeholder text left in the skill
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 8 example trigger phrases
    • +3Description length 431: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 25 items
    • +3Output format is stated explicitly
    • +4Has examples (12 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: suspicious
    This skill is coherent, but it can route broad coding requests into a local multi-agent controller without clear user confirmation or data-use warnings.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026