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AB product-data-audit

Use when auditing a product, business, or project ecosystem — analyzing data sources, decision loops, bottlenecks, and implementation contours. Triggers on "аудит продукта", "product audit", "data audit", "аудит данных", "аудит бизнеса", "проанализируй экосистему", "аудит систем".

serejaris/personal-corp-os Agent Skills author: serejaris MIT 7 files body ≈ 1 648 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Triggers on "аудит продукта", "product audit", "data audit", "аудит данных", "аудит бизнеса", "проанализируй экосистему", "аудит систем".

As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions

AnalyzerData and analyticstype 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
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
95
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
70
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: 7. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 46, 91): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 68/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 4 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 19 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1648 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress

    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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +5Description quotes 7 example trigger phrases
    • +3Description length 281: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +2Bilingual instructions (RU + EN)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.