SKILLEMALL.ai
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BC first-principle-analyzer

Use this skill when analyzing complex problems through first principles. Provides structured 7-phase analysis framework, assumption identification and challenging, basic truth extraction, and innovative solution generation from fundamentals.

ClawHub Agent Skills author: pagoda v2.4.2 MIT-0 18 files body ≈ 1 008 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

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%
80
Run on models
none yet
Process rating
C
53/100
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:275
      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:291
      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:304
      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:317
      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:333
      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: 17. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "origin"
    • note frontmatter-key unknown frontmatter key "tools"

    Process rating: all ten parameters 53/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 43 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1008 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -221 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 241: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 43 items
    • +4Has examples (7 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    External checks

    ClawHub: suspicious
    This appears to be a legitimate analysis helper, but it asks for more local command authority and sharing capability than its purpose clearly justifies.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026