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AD roast

Use when someone asks to roast an idea, pressure-test or stress-test an idea, validate a business idea, "convene the panel", get a brutal second opinion before building something, or says "/roast". Spins up a 5-angle panel (Critic, Champion, Analyst, Investigator, Customer) that attacks the idea from every angle, then a Judge returns one GO / RESHAPE / KILL verdict with the cheapest test to de-risk it.

LeoYeAI/openclaw-master-skills Claude Code author: LeoYeAI 10 files · 3 scripts body ≈ 2 420 tokens Open the sourcegithub.com↗ analyzed 10 d ago

Use when someone asks to roast an idea, pressure-test or stress-test an idea, validate a business idea, "convene the panel", get a brutal second opinion…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSoftware developmentAI and agentstype 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%
95
Quality 40%
97
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Risky intent intent-offensive-security references/adversarial_panel_canon.md:30
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | The Critic | Red team / pre-mortem | The fatal flaw, the fastest path to death |
    • low Risky intent intent-offensive-security references/adversarial_panel_canon.md:36
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Red teaming.** The U.S. intelligence and security communities formalized red teaming precisely
    • low Risky intent intent-offensive-security references/adversarial_panel_canon.md:37
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      because internal teams cannot critique their own plans objectively (Micah Zenko, *Red Team: How to
    • low Risky intent intent-offensive-security references/adversarial_panel_canon.md:38
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      Succeed by Thinking Like the Enemy*, 2015). The Critic is a one-seat red team.
    • low Risky intent intent-offensive-security references/adversarial_panel_canon.md:80
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      4. Micah Zenko, *Red Team: How to Succeed by Thinking Like the Enemy*, Basic Books, 2015.

    Files scanned: 10. 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 49/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2420 tokens
    • low 10 top-level sections: this looks like several domains in one skill

    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)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +3Description length 405: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 97.