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BB understand-issue

Investigate a GitHub issue or reported bug the way a seasoned maintainer would — form an independent model before reading the thread's theories, trace the mechanism and history, verify the diagnosis, and return a concise briefing with a verdict and fix direction. Use for triage, root-cause analysis, or re-investigation.

mastra-ai/mastra Agent Skills author: mastra-ai NOASSERTION 3 files body ≈ 3 042 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Investigate a GitHub issue or reported bug the way a seasoned maintainer would — form an independent model before reading the thread's theories, trace the…

As a process B 65/100 · Nearly there — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubData 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
When it triggers w 12
20
Inputs and preconditions w 11
30
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 65/100

    • 0Result and completion. Does not say what the result is
    • 20When it triggers. No condition that starts the skill
    • 30Inputs and preconditions. Does not say what the process needs to start
    • 60Failures and branches. 2 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 54 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3042 tokens
    • 100Running it twice. Mutating operations check current state
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 11 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 321: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 54 items
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.