SKILLEMALL.ai
GitHub ↗ RU Sign in

AC cron-worker-guardrails

Use when: hardening OpenClaw cron/background workers (POSIX shells: bash/sh) against brittle quoting, cwd/env drift, and false pipeline failures (SIGPIPE, pipefail + head). Don't use when: the issue is application logic rather than execution-wrapper reliability. Output: a scripts-first hardening checklist + safe patterns (silent-on-success, deterministic cwd/env, rollback-friendly).

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 842 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Use when: hardening OpenClaw cron/background workers (POSIX shells: bash/sh) against brittle quoting, cwd/env drift, and false pipeline failures (SIGPIPE…

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureSoftware developmentInfrastructuretype 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
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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

    • note frontmatter-key unknown frontmatter key "slug"

    Process rating: all ten parameters 57/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 6 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, web, git, python) that frontmatter does not declare
    • 100Steps. 34 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 842 tokens
    • low The response is described with custom markup (3 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
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 385: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 34 items
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +1License stated

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