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CB scout-mindset-bias-check

Detects and removes cognitive biases from reasoning using Julia Galef's Scout Mindset framework. Provides reversal tests, scope sensitivity checks, status quo bias tests, confidence interval audits, and full bias audits. Use when a prediction feels emotional, stuck at 50/50, or when validating forecasting process. Use when user mentions scout mindset, soldier mindset, bias check, reversal test, scope sensitivity, or cognitive distortions.

lyndonkl/claude Agent Skills author: lyndonkl 4 files body ≈ 3 591 tokens Open the sourcegithub.com↗ analyzed 16 h ago

Detects and removes cognitive biases from reasoning using Julia Galef's Scout Mindset framework.

As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions

AnalyzerData and analyticstype 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
C
89/100
safety, quality, tests
Safety 60%
93
Quality 40%
84
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
50
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 · 7

    ✓ No critical or high findings

    Medium and low: 7
    • low Risky intent intent-offensive-security resources/cognitive-bias-catalog.md:7
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Confirmation | Confirmation | Seek supporting evidence only | Search for disconfirming evidence? | Red team your forecast |
    • low Risky intent intent-offensive-security resources/cognitive-bias-catalog.md:45
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      **Remediate:** Red team your forecast, list disconfirming evidence first, ask "How could I be wrong?"
    • low Risky intent intent-offensive-security resources/debiasing-techniques.md:184
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ## Red Teaming and Devil's Advocate
    • low Risky intent intent-offensive-security resources/debiasing-techniques.md:186
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ### Technique 12: Structured Red Team Review
    • low Risky intent intent-offensive-security resources/debiasing-techniques.md:340
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      ### Technique 22: Red Team / Blue Team Split
    • low Risky intent intent-offensive-security resources/debiasing-techniques.md:344
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Red Team:** Argues forecast should be LOW
    • low Risky intent intent-offensive-security resources/debiasing-techniques.md:360
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      **Confirmation Bias:** → Consider-opposite (#10), Red teaming (#12), Steelman (#10)

    Files scanned: 4. 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 65/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 50When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 116 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3591 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 442: enough signal without eating the budget
    • +4Structure: 41 headings
    • +3Step-by-step instructions: 116 items
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.