SKILLEMALL.ai
GitHub ↗ RU Sign in

AF woodpecker-ci

Operate Woodpecker CI from installation through production troubleshooting: configure servers and agents, connect Forgejo/Gitea or another forge, write and validate pipelines, manage secrets and plugins, use Docker or Kubernetes backends, run the CLI, and diagnose failed builds. Use when setting up, administering, or debugging Woodpecker CI. Do not use this skill for unrelated requests; route to the nearest named specialist.

magnus919/agent-skills Agent Skills author: magnus919 MIT 16 files · 1 script body ≈ 2 265 tokens Open the sourcegithub.com↗ analyzed 27 h ago

Operate Woodpecker CI from installation through production troubleshooting: configure servers and agents, connect Forgejo/Gitea or another forge, write and…

As a process F 44/100 · Will not run — References files that are not bundled: templates/forgejo.env.example

ProcedureDockerKubernetesInfrastructureAI and agentsSoftware 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
A
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
90
Run on models
none yet
Process rating
F
44/100
Will not run
References files that are not bundled: templates/forgejo.env.example
Tools and files w 18
0
Result and completion w 14
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security references/setup.md:71
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    Set the namespace and backend options explicitly. For private images, place pull credentials in Kubernetes Secrets and list them through `WOOD…MES`. Add per-step resour

Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: templates/forgejo.env.example

Process rating: all ten parameters 44/100

Will not run. References files that are not bundled: templates/forgejo.env.example
  • 0Tools and files. 1 referenced file(s) missing: templates/forgejo.env.example
  • 0Result and completion. Does not say what the result is
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 8 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2265 tokens
  • low 11 top-level sections: this looks like several domains in one skill
  • medium 7 test cases, all positive: not one "should refuse" or "should ask first"
  • low No test case covers injection arriving through data

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 428: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (9 of 9)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.