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BB github-pr-review-session

Human-reviewer co-pilot for ZeroClaw PR reviews. Use this skill when the user wants to review a specific PR as themselves, re-review a PR after author changes, work through a queue of PRs, check what's still open on a PR, or post a formal review verdict. Trigger on: 'review 1234', 'can you look at PR #1234', 're-review 1234', 'check 1234', 'what's still open on 1234', 'go through the queue', 'next PR', 'review the open PRs'. This skill posts reviews in the voice of the active `gh` account holder using gh CLI.

zeroclaw-labs/zeroclaw Agent Skills author: zeroclaw-labs Apache-2.0 1 file body ≈ 4 434 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Human-reviewer co-pilot for ZeroClaw PR reviews.

As a process B 71/100 · Nearly there — weak spots: result and completion, progress reporting

AnalyzerGitHubAI and agentsWriting and documentsSoftware 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%
100
Quality 40%
84
Run on models
none yet
Process rating
B
71/100
Nearly there
Progress reporting w 2
0
Result and completion w 14
40
Tools and files w 18
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: 1. 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 71/100

    • 0Progress reporting. Says nothing while it works
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4434 tokens
    • 85Steps. 60 steps, 1 vague phrases
    • 100Failures and branches. 5 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 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 The response is described with custom markup (8 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 514: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 60 items
    • +4Has examples (9 code blocks)

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