SKILLEMALL.ai

BB ai-tooling-inventory

Build the inventory of AI capability an organization is actually running, across five ways it enters. Finds tools that left no transaction behind, including in-house builds, vendor features switched on inside approved products, third-party integrations attached to approved platforms, and free tools staff signed up for or created. Classifies entry path, evaluates ePHI contact and third-party disclosure, and produces structured findings on ownership, agreements, verification, and risk-analysis scope.

ClawHub Claude Code author: Dangsllc v2.0.0 MIT-0 4 files body ≈ 8 379 tokens Open the sourceclawhub.ai analyzed 2 d ago

Build the inventory of AI capability an organization is actually running, across five ways it enters.

As a process B 78/100 · Nearly there — weak spots: execution cost, running it twice, progress reporting

GeneratorInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
B
78/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Execution cost w 6
40
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.
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

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 8379 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 78/100

  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 6 mutating operations with no state check
  • 40Execution cost. Instruction body is 8379 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 35 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 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 10 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 503: enough signal without eating the budget
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 35 items
  • +3Output format is stated explicitly
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +1License stated

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

External checks

ClawHub: clean
This skill is a disclosed healthcare compliance interview and artifact-review workflow for finding AI tools, with sensitive self-inspection steps that are purpose-aligned and bounded.
LLM: benign (high) · VirusTotal: · 25 Aug 2026