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BC munger-decision

Charlie Munger's mental model decision assistant. Analyzes your decision scenario, recommends the most relevant thinking models, and guides you through structured analysis. Free version includes 12 core models. Use when: making investment, product, strategy, or life decisions. NOT for: general Q&A or information lookup.

ClawHub Agent Skills author: David v1.2.4 MIT-0 45 files body ≈ 544 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerInfrastructuretype 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
B
89/100
safety, quality, tests
Safety 60%
95
Quality 40%
81
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 Secrets in code secret-high-entropy-token package-lock.json:282
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "integrity": "sha5…ywo+qwL+oL8H…C1U+vRfLQDvw==",
      quoted
    • low Secrets in code secret-high-entropy-token package-lock.json:298
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha512-Yqfm+XDx0+Prh3…1yC+JWZ2…IL7+vK+Clp7…D7g==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:311
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…xZl+RoGR…fbT/ZgrF…0EA==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:324
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
      "integrity": "sha5…3bJ+V0If…IXN+CL65…a4w==",
      detector
    • low Secrets in code secret-high-entropy-token package-lock.json:340
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "integrity": "sha5…H47+FFon…OsV/4+RRsz…0ig==",
      quoted

    Files scanned: 40. 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 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (munger-decision) differs from the folder (munger-decision-free)
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 40 steps
    • 100Execution cost. Instruction body is 544 tokens
    • 100Running it twice. No mutating operations

    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
    • +3Output format is not stated: the model decides each time
    • -413 reference files, but SKILL.md never points to them: the model will not open them
    • -34 of 4 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 321: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 40 items
    • +4Has examples (5 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: clean
    This is a local decision-framework skill, but users should not treat its investment allocation examples as personalized financial advice.
    LLM: benign (medium) · VirusTotal: · 29 May 2026