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CC gh-issues

Fetch GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5] [--notify-channel -1002381931352]

ClawHub Agent Skills author: JackHua6 v1.0.0 MIT-0 2 files body ≈ 8 398 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubAI and agentsInfrastructuretype 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
C
72/100
safety, quality, tests
Safety 60%
79
Quality 40%
62
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

The same skill appears in 1 more place: ClawHub

What is at stake

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

Exfiltration 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 5

✓ No critical or high findings

Medium and low: 5
  • medium Exfiltration net-credential-use SKILL.md:32
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "Authorization: Bearer $GH_TOKEN" -H "Accept: application/vnd.github+json" ...
  • medium Exfiltration net-credential-use SKILL.md:116
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "Authorization: Bearer $GH_TOKEN" -H "Accept: application/vnd.github+json" \
  • medium Exfiltration net-credential-use SKILL.md:237
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "Authorization: Bearer $GH_TOKEN" -H "Accept: application/vnd.github+json" \
  • medium Exfiltration net-credential-use SKILL.md:368
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "Authorization: Bearer $GH_TOKEN" -H "Accept: application/vnd.github+json" ...
  • low Exfiltration net-credential-use SKILL.md:226
    Credential used in a network call (verify the destination is the intended service) (destination is a well-known publishing service)
    curl -s -o /dev/null -w "%{http_code}" -H "Authorization: Bearer $GH_TOKEN" https://api.github.com/user
    known service

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning body-long SKILL.md body ≈ 8398 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 55/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 40Execution cost. Instruction body is 8398 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (web, node) that frontmatter does not declare
  • 100Steps. 125 steps
  • 100Failures and branches. 36 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 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

  • +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 340: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 125 items
  • +4Has examples (34 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
This GitHub automation skill has a coherent purpose, but it handles GitHub tokens unsafely and can keep acting on repositories through sub-agents and background modes.
LLM: suspicious (high) · VirusTotal: · 29 May 2026