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AB think-tank

Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural/design/strategic decisions. Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says "think tank", "debate this", "perspectives on", "trade-offs", "should I use X or Y", "help me decide", "before we plan", or asks for pros/cons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.

davila7/claude-code-templates Agent Skills author: davila7 MIT 2 files body ≈ 2 813 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural/design/strategic decisions.

As a process B 69/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting

PersonaAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
B
69/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
Running it twice w 4
30
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: 2. 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 69/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 60Result and completion. Output format stated, no completion criterion
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 53 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2813 tokens

    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)
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 667: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 53 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.