SKILLEMALL.ai

AC agent-see

Convert any website, SaaS product, or API into a live, discoverable, agent-executable integration. Use when the user asks to "convert a website", "turn this SaaS into a plugin", "create an agent bundle", "make my business agent-ready", "go live", "deploy the server", "publish discovery files", "connect to my database", "wire up real data", "set up maintenance", "re-sync the bundle", "package as a plugin", "verify the bundle", "generate launch artifacts", or provides a URL or OpenAPI spec to process. Also triggers proactively after any pipeline step completes to guide the user toward full go-live.

ClawHub Agent Skills author: Daniel Foo Jun Wei v1.0.0 MIT-0 2 files body ≈ 7 692 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

IntegrationAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
77
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration read-dotenv SKILL.md:487
    Reads a .env file
    cp .env.example .env

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7692 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 7692 tokens
  • 85Steps. 152 steps, 1 vague phrases
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 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
  • low The response is described with custom markup (5 tags): a typed call is more reliable

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)
  • +3Output format is not stated: the model decides each time
  • -223 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 14 example trigger phrases
  • +3Description length 603: enough signal without eating the budget
  • +4Structure: 76 headings
  • +3Step-by-step instructions: 152 items
  • +4Has examples (25 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
This skill has a coherent agent-integration purpose, but it can automatically install unpinned GitHub code and guide users into deployment, publishing, and backend credential workflows with broad triggers.
LLM: suspicious (high) · VirusTotal: · 29 May 2026