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AC chrome-extension-maintainer

Release-gate and maintenance workflow for Chrome extensions covering Chrome Web Store policy, MV3, privacy, permissions, SEO/GEO, analytics triage, browser E2E evidence, i18n, packaging, and green/yellow/red publish decisions. Use when Codex needs to review an extension repo, diagnose growth or analytics issues, align listing/landing/support/privacy pages, choose tests to run, review data flow, or decide whether an extension is ready to publish or promote.

ClawHub Agent Skills author: Zakhar Pashkin v1.0.0 MIT-0 4 files body ≈ 1 491 tokens Open the sourceclawhub.ai↗ analyzed 18 h ago

Release-gate and maintenance workflow for Chrome extensions covering Chrome Web Store policy, MV3, privacy, permissions, SEO/GEO, analytics triage, browser…

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedurePlaywrightData and analyticsSoftware developmentMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
56/100
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: 4. 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 56/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 9 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Steps. 72 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1491 tokens

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 460: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 72 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: clean
    This is a documentation-only Chrome extension release-review workflow with clear limits around publishing and no evidence of hidden execution or data misuse.
    LLM: benign (high) · VirusTotal: · 11 Jun 2026