SKILLEMALL.ai
GitHub ↗ RU Sign in

AB curiosity-engine

Curiosity-driven reasoning enhancement for OpenClaw agents. Activates when the agent needs to explore open-ended questions, research unfamiliar topics, investigate anomalies, or when the user asks for deep analysis. Injects structured curiosity behaviors into the reasoning process: self-questioning, assumption challenging, information gap detection, and tool-driven exploration. Use when tasks require depth over speed, when encountering surprising information, or when explicitly asked to "dig deeper" / "explore" / "be curious".

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 1 343 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Curiosity-driven reasoning enhancement for OpenClaw agents.

As a process B 77/100 · Nearly there — weak spots: inputs and preconditions

ProcedureAI 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
B
77/100
Nearly there
Inputs and preconditions w 11
0
Failures and branches w 10
55
Result and completion w 14
60
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: 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 77/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 56 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1343 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress

    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)
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 532: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 56 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.