SKILLEMALL.ai

BF greenhelix-agent-testing-qa-toolkit

Agent Testing & QA Toolkit: Integration, Chaos, and Contract Testing for Multi-Agent Systems. Comprehensive testing toolkit for agent commerce systems: unit vs integration vs e2e testing strategies, mock strategies, chaos testing, contract testing between agents, performance benchmarking, CI/CD for agent deployments, and canary releases.

ClawHub Agent Skills author: mirni v1.3.1 MIT-0 2 files body ≈ 26 064 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process F 49/100 · Will not run — References files that are not bundled: input_data

IntegrationAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
57
Run on models
none yet
Process rating
F
49/100
Will not run
References files that are not bundled: input_data
Tools and files w 18
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  3. The text references files that are not there: add them or drop the references.
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

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 26064 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: input_data
  • note frontmatter-key unknown frontmatter key "type"
  • note frontmatter-key unknown frontmatter key "price_usd"
  • note frontmatter-key unknown frontmatter key "content_type"
  • note frontmatter-key unknown frontmatter key "executable"
  • note frontmatter-key unknown frontmatter key "install"
  • note frontmatter-key unknown frontmatter key "credentials"

Process rating: all ten parameters 49/100

Will not run. References files that are not bundled: input_data
  • 0Tools and files. 1 referenced file(s) missing: input_data
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 10Execution cost. Instruction body is 26064 tokens: crowds the task out of the window
  • 30Running it twice. 32 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 26 steps
  • 100Failures and branches. 4 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 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)
  • +2Single-language instructions
  • +3Description length 339: enough signal without eating the budget
  • +4Structure: 48 headings
  • +3Step-by-step instructions: 26 items
  • +3Output format is stated explicitly
  • +4Has examples (23 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.

External checks

ClawHub: clean
This is a non-executing testing guide, but copied examples could affect real GreenHelix accounts or funds if run with production credentials.
LLM: benign (high) · VirusTotal: · 29 May 2026