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AA adi-decision-engine

Structured multi-criteria decision analysis for ranking options with weights, constraints, confidence, tradeoff reasoning, sensitivity analysis, and explainable recommendations. Use when the user asks for decision support, MCDA, weighted scoring, prioritization, vendor selection, route planning, hiring shortlist ranking, tool comparison, procurement decisions, or auditable agent decision logic.

ClawHub Agent Skills author: Dimitris Gousopoulos v0.1.0 16 files body ≈ 1 054 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process A 85/100 · Runs to the end — no weak spots found

AnalyzerInfrastructurePeople and hiringAI 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
A
85/100
Runs to the end
Result and completion w 14
60
When it triggers w 12
70
Inputs and preconditions w 11
70
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: 16. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 85/100

    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Failures and branches. 7 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 57 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1054 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 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)
    • -31 of 4 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 397: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 57 items
    • +3Output format is stated explicitly
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    The skill appears to be a local decision-support helper with no evidence of exfiltration, persistence, credential access, or destructive behavior, but users should be careful applying it to hiring or other sensitive decisions.
    LLM: benign (medium) · VirusTotal: benign · 28 May 2026