SKILLEMALL.ai
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AC ultimate-skills-finder

The ultimate multi-source OpenClaw/agent skills finder. Searches across ClawHub, skills.sh, Rush registry, LobeHub, SkillsMP, llmbase.ai, skillsllm.com, and GitHub curated collections — then cross-references, deduplicates, ranks by popularity, and optionally security-scans via Gen Digital. Use when: (1) user says 'find a skill for X', 'is there a skill that can...', 'how do I do X with OpenClaw', (2) user wants to discover new agent capabilities, (3) user asks what skills exist for a specific domain or task, (4) user wants to install a skill but isn't sure which one, (5) user wants to compare skills across multiple registries.

ClawHub Agent Skills author: Broedkrummen v1.0.0 MIT-0 5 files body ≈ 980 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ReferenceGitHubAI 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%
84
Run on models
none yet
Process rating
C
59/100
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 · 0

    ✓ No critical or high findings

    Files scanned: 5. 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 59/100

    • 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
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 980 tokens
    • 100Running it twice. Mutating operations check current state

    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
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +3Description length 634: enough signal without eating the budget
    • +4Structure: 11 headings
    • +3Step-by-step instructions: 22 items
    • +4Has examples (6 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

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

    External checks

    ClawHub: suspicious
    This appears to be a legitimate skill-discovery helper, but it needs review because it can read local skill data, contact multiple external services, share searched URLs with an outside scanner, and guide installs without clearly bounded disclosure.
    LLM: suspicious (medium) · VirusTotal: · 29 May 2026