SKILLEMALL.ai
GitHub ↗ RU Sign in

BB agent-queue-to-reviewed-pr

Turn a ticket queue into reviewed draft PRs with an agent that never merges. Use when wiring an agent to a ticket board or issue tracker. Trigger on "auto PR agent", "agent queue".

ClawHub Agent Skills author: Alexandre Bloch v1.1.0 MIT-0 9 files body ≈ 7 000 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Turn a ticket queue into reviewed draft PRs with an agent that never merges.

As a process B 75/100 · Nearly there — weak spots: inputs and preconditions

AnalyzerJiraGitHubAI and agentstype 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
B
78/100
safety, quality, tests
Safety 60%
70
Quality 40%
89
Run on models
none yet
Process rating
B
75/100
Nearly there
Inputs and preconditions w 11
0
Failures and branches w 10
50
Tools and files w 18
60
the three weakest of ten parameters · all ten

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. 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 · 6

✓ No critical or high findings

Medium and low: 6
  • medium Exfiltration net-credential-use references/brief-staleness.md:110
    Credential used in a network call (verify the destination is the intended service)
    curl -fsS -X POST "${STALE_ACK_URL}" -H "Authorization: Bearer ${AGENT_API_TOKEN}" \
  • medium Exfiltration net-credential-use references/queue-contract.md:119
    Credential used in a network call (verify the destination is the intended service)
    curl -fsS --max-time 30 "${QUEUE_URL}" -H "Authorization: Bearer ${AGENT_API_TOKEN}"
  • medium Exfiltration net-credential-use references/queue-contract.md:131
    Credential used in a network call (verify the destination is the intended service)
    curl -s -o /dev/null -w "%{http_code}\n" "${QUEUE_URL}" -H "Authorization: Bearer ${AGENT_API_TOKEN}"  # Output: 200
  • medium Exfiltration net-credential-use SKILL.md:65
    Credential used in a network call (verify the destination is the intended service)
    curl -fsS --max-time 30 "${QUEUE_URL}" -H "Authorization: Bearer ${AGENT_API_TOKEN}"
  • medium Exfiltration net-credential-use SKILL.md:101
    Credential used in a network call (verify the destination is the intended service)
    curl -fsS -X POST "${GROUNDING_ACK_URL}" -H "Authorization: Bearer ${AGENT_API_TOKEN}" \
  • medium Exfiltration net-credential-use SKILL.md:126
    Credential used in a network call (verify the destination is the intended service)
    curl -fsS -X POST "${ACK_URL}" -H "Authorization: Bearer ${AGENT_API_TOKEN}" \

Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7000 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 75/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web, git, python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 7000 tokens
  • 100Steps. 25 steps
  • 100Result and completion. Output format and completion criterion are stated
  • 100When it triggers. States when to use and when not to
  • 100Consistency. Name and required fields are in place
  • 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 17 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (13 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 180: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 25 items
  • +3Output format is stated explicitly
  • +4Has examples (7 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

External checks

ClawHub: clean
The skill is a well-scoped workflow for turning prioritized tickets into human-reviewed draft PRs, with its external writes and local retention behavior disclosed.
LLM: benign (high) · VirusTotal: · 16 Jul 2026