SKILLEMALL.ai

BC skill-discovery

Automatically discover, search, and install skills from ClawHub — the public skill registry for OpenClaw — when no locally installed skill can fulfill the user's request. Acts as a "skill gap filler": when the agent encounters a task it cannot handle with existing skills or built-in tools, it searches ClawHub for a matching skill, presents options to the user, and installs their choice with one command. Use when: (1) the user asks for a capability that no installed skill can fulfill, (2) the user explicitly says "find a skill", "search for a skill", "is there a skill for X", "install a skill from ClawHub", or "search ClawHub", (3) the agent determines that a specialized ClawHub skill would handle the task better than general-purpose reasoning or existing tools, (4) the user asks "what skills are available for X" or "can OpenClaw do X". Triggers on phrases like: "find a skill", "search skills", "install a skill", "search ClawHub", "is there a plugin for", "can you do X", "I need a tool for", "有没有能做X的技能", "帮我找个技能", "搜一下技能", "装个技能". Also activates when no available_skills entry matches the user's intent and the task is clearly skill-shaped (domain-specific workflow, specialized integration, or multi-step procedure).

ClawHub Agent Skills author: lpb123 v1.0.0 MIT-0 2 files body ≈ 515 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 61/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

IntegrationAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

How to improve

  1. Shorten the description to 1024 characters.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1233 chars, limit 1024

Process rating: all ten parameters 61/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 1 mutating operations with no state check
  • 40Consistency. Frontmatter name (skill-discovery) differs from the folder (clawhub-skill-finder)
  • 70When it triggers. States when to use, but not when not to
  • 70Failures and branches. 4 branches
  • 100Tools and files. No external tools needed
  • 100Steps. 18 steps
  • 100Execution cost. Instruction body is 515 tokens
  • 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)
  • +3Description length 1232: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 17 example trigger phrases
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 18 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: clean
This is an instruction-only helper for finding ClawHub skills, with clear confirmation required before any install.
LLM: benign (high) · VirusTotal: · 29 May 2026