SKILLEMALL.ai

AC aegis

AI Development Quality Guardian — contract-driven, design-first quality guardrails for AI-assisted full-stack development. Five-layer defense: Design → Contract → Implementation → Verification → PM. Prevents project chaos at scale. Activate when: starting a feature, setting up a project, dispatching coding tasks, reviewing PRs, or managing multi-agent workflows. Also triggers on projects with a contracts/ directory (implementation guardrails auto-activate).

ClawHub Agent Skills author: PeterHiroshi v1.4.0 MIT-0 22 files · 7 scripts body ≈ 2 622 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

ProcedureAI and agentsSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
54/100
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

    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: 22. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 54/100

    • 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
    • 40Consistency. Frontmatter name (aegis) differs from the folder (aegis-quality-guardian)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 4 branches
    • 100Steps. 71 steps
    • 100Execution cost. Instruction body is 2622 tokens
    • 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 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)
    • +3Output format is not stated: the model decides each time
    • -31 of 7 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 461: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 71 items
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (3 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: clean
    Aegis is a coherent development-quality skill that writes project guardrail files and can run setup scripts, with no evidence of hidden data access, exfiltration, or destructive behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026