SKILLEMALL.ai

AB gogetajob

Open-source contribution workflow — find GitHub issues, implement fixes, submit PRs, track results. Use when: (1) starting a work loop or contribution cycle (打工, contribute, work on issues), (2) scanning repos for available work, (3) submitting or following up on PRs, (4) syncing PR statuses and handling review feedback, (5) checking work stats or history. Triggers on: 打工, 干活, find work, contribute, open source, scan issues, submit PR, work loop, gogetajob. NOT for: simple one-off code edits, reading code, or tasks unrelated to open-source contribution.

ClawHub Agent Skills author: kagura-agent v1.0.2 MIT-0 3 files body ≈ 1 310 tokens Open the sourceclawhub.ai analyzed 5 d ago

As a process B 72/100 · Nearly there — weak spots: result and completion

ProcedureGitHubSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
B
72/100
Nearly there
Result and completion w 14
0
Tools and files w 18
60
Failures and branches w 10
65
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 72/100

    • 0Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 65Failures and branches. 3 branches
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 28 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1310 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 The response is described with custom markup (7 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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 559: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    This markdown-only skill is coherent for open-source contribution automation, but it grants broad automated code-writing and GitHub publishing authority without clear human approval gates.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026