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BB issue-create

Interview the user to gather all required context, then open a high-quality GitHub issue (`gh issue create`) on the codex-proxy repo with the right template, labels, and structured body. TRIGGER when: user says "open an issue", "file an issue", "create issue", "report a bug", "feature request", "提个 issue", "报 bug", "提需求". DO NOT TRIGGER when: user wants to view, search, comment on, or close existing issues (use `gh issue list/view/comment/close` directly); user is writing a PR description (use `pr-push`).

icebear0828/codex-proxy Agent Skills author: icebear0828 7 files body ≈ 1 689 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Interview the user to gather all required context, then open a high-quality GitHub issue (gh issue create) on the codex-proxy repo with the right template…

As a process B 66/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorGitHubSoftware developmenttype 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%
95
Quality 40%
93
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: codex-proxy

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Bash Read Write

    Files scanned: 7. 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 66/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 6 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 70Failures and branches. 6 branches
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 53 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1689 tokens
    • 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
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +3Description length 510: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 53 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.