SKILLEMALL.ai
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BC skill-comply

Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines. Use when checking whether agents actually follow the skills, rules, and definitions they were given, rather than assuming they do.

The skillemall take

Generates scenarios to check whether an agent actually follows its instructions — three prompt strictness levels, agent execution, behavior classification, compliance report with percentages and tool call timelines. The idea works: you see what the agent actually does instead of assuming.

Tests show: code stayed within sandbox boundaries, no side effects. Quality score 80/100, process score 53/100 — logic is there, architecture could be tighter. No critical errors, linter passes. Never ran against live models, so real-world agent behavior remains untested.

Worth installing if you need objective verification that your agent matches its definition. Test it on your specific agent before production use.

affaan-m/everything-claude-code Agent Skills author: affaan-m MIT 19 files · 13 scripts body ≈ 510 tokens Open the sourcegithub.com↗ analyzed 25 h ago

Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies…

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ECC

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "tools"

    Process rating: all ten parameters 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 17 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 510 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

    • +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
    • -310 of 10 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 371: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 17 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

    In the sandbox The scripts kept to themselves

    The skill's scripts were run in a throwaway machine: no network, fake keys in the home directory, a tracer watching. We wrote down what they did. Reaching for the network or for secrets caps the technical grade at C; a quiet run adds no points.

    Запущено 9 скриптов; каждому дали двадцать секунд, поддельный домашний каталог с ключами и сеть, в которой ничего нет.

    Из них 1 не дошёл до работы, и об их поведении мы ничего не узнали.

    scripts/classifier.pyничего за пределами своей папки
    scripts/grader.pyничего за пределами своей папки
    scripts/parser.pyничего за пределами своей папки
    scripts/report.pyничего за пределами своей папки
    scripts/run.pyне запустился: run.py: error: the following arguments are required: skill
    scripts/runner.pyничего за пределами своей папки
    scripts/scenario_generator.pyничего за пределами своей папки
    scripts/spec_generator.pyничего за пределами своей папки
    scripts/utils.pyничего за пределами своей папки

    6 Oct 2026