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AA devtool-answer-monitor

Use when the user wants to monitor how ChatGPT, Claude, Gemini, and other LLMs describe a developer tool, API, SDK, or open-source project. DevTool Answer Monitor is the companion skill for the devtool-answer-monitor repo and covers query pool design, four-metric monitoring, model-specific content placement, content checks, negative-answer repair, activation analysis, and T+7 or T+14 regression validation.

ClawHub Agent Skills author: veeicwgy v0.3.0 MIT-0 80 files · 2 scripts body ≈ 1 726 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process A 81/100 · Runs to the end — weak spots: progress reporting

IntegrationGitHubAI and agentsData and analyticstype 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
A
81/100
Runs to the end
Progress reporting w 2
0
Failures and branches w 10
55
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: 80. 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 81/100

    • 0Progress reporting. Says nothing while it works
    • 55Failures and branches. 1 branches
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 37 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1726 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 13 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)
    • +4No input/output examples
    • +2Single-language instructions
    • +3Description length 409: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 37 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (2 of 4)
    • +1License stated

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

    External checks

    ClawHub: clean
    This skill is a disclosed LLM answer-monitoring workflow with optional API calls and no hidden or unrelated behavior found.
    LLM: benign (high) · VirusTotal: · 29 May 2026