SKILLEMALL.ai

AA agent-architecture-analysis

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

ClawHub Agent Skills author: Kevin Anderson v1.0.3 MIT-0 3 files body ≈ 1 358 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process A 85/100 · Runs to the end — weak spots: progress reporting

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
100/100
safety, quality, tests
Safety 60%
100
Quality 40%
100
Run on models
none yet
Process rating
A
85/100
Runs to the end
Progress reporting w 2
0
Tools and files w 18
60
Result and completion w 14
60
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: 3. 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 85/100

    • 0Progress reporting. Says nothing while it works
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 37 steps
    • 100When it triggers. States when to use and when not to
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1358 tokens
    • 100Running it twice. No mutating operations

    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

    • +1No license
    • +2Single-language instructions
    • +5Description quotes 4 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 449: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 37 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

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

    External checks

    ClawHub: clean
    This is a documentation-only skill for auditing a user-selected agent codebase, with no executable payload or hidden behavior found.
    LLM: benign (high) · VirusTotal: · 1 Jun 2026