SKILLEMALL.ai

CD saas-security-posture

Audit and harden your SaaS tool stack

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 1 file body ≈ 3 132 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Audit and harden your SaaS tool stack

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerSlackGitHubAWSInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
97
Quality 40%
71
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
When it triggers w 12
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: saas-security-posture (sickn33/agentic-awesome-skills)

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • low Exfiltration net-credential-use SKILL.md:52
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -H "Authorization: Bearer ${SLACK_ADMIN_TOKEN}" \
      security skill
    • low Exfiltration net-credential-use SKILL.md:54
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -H "Authorization: Bearer ${SLACK_ADMIN_TOKEN}" \
      security skill
    • low Exfiltration net-credential-use SKILL.md:212
      Credential used in a network call (verify the destination is the intended service) (documentation of a security skill)
      curl -s -X POST -H "Authorization: Bearer ${SLACK_ADMIN_TOKEN}" \
      security skill

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning description-short description under 40 chars: too little signal for triggering
    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "source_repo"
    • note frontmatter-key unknown frontmatter key "source_type"
    • note frontmatter-key unknown frontmatter key "date_added"
    • note frontmatter-key unknown frontmatter key "license_source"

    Process rating: all ten parameters 43/100

    • 0Result and completion. Does not say what the result is
    • 0When it triggers. No condition that starts the skill
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 30Running it twice. 7 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 11 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3132 tokens
    • 100Progress reporting. Reports progress
    • low 13 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)
    • +3Description length 37: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Structure: 27 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (16 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.