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Agent skill

Woodpecker CI

by magnus919 in magnus919/agent-skills

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…

MITAuto-check: notesDevOps & Cloud

Install Woodpecker CI

skills CLI
$ npx skills add magnus919/agent-skills --skill woodpecker-ci -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install magnus919/agent-skills woodpecker-ci --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/magnus919/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/woodpecker-ci .claude/skills/woodpecker-ci && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
woodpecker-ci
GitHub stars
115
Token cost
~2.5k tokens
SKILL.md length
1,062 words
Files
17 (incl. scripts, references, assets)
Skills in repo
131
Repo updated
First seen
Licence
MIT

At a glance

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…

  • Works in 6 steps: Identify the Woodpecker major version,… → Read the matching reference below before… → Render and lint configuration before… → …
  • Debugging Woodpecker CI
  • SKILL.md covers Operating loop, Choose the entry point, Quick command card and Core defaults, plus 7 more sections
  • Runs Python scripts from its folder; calls docker, openssl and python3; needs WOODPECKER_AGENT_SECRET

What it does

Woodpecker CI is an agent skill from magnus919/agent-skills. 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.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 21 other files, including scripts, reference files and assets (for example `README.md`, `assets/troubleshooting-checklist.md` and `evals/evals.json`). Compatibility notes: Requires access to a Woodpecker instance for administration; Docker Compose, Kubernetes, or woodpecker-cli are optional depending on the backend.

It sits in DevOps & Cloud, covering Container orchestration. It works with Docker and Kubernetes. The repository describes itself as: Curated collection of AI agent skills for Hermes and other agent frameworks. The licence is MIT.

When your agent uses it

  • Debugging Woodpecker CI
  • Unrelated requests
  • Route to the nearest named specialist

Example prompts

  • “/woodpecker-ci”

Requirements

  • Python 3
  • Docker
  • A credential in WOODPECKER_AGENT_SECRET
  • Compatibility (from SKILL.md): Requires access to a Woodpecker instance for administration; Docker Compose, Kubernetes, or woodpecker-cli are optional depending on the backend.

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Identify the Woodpecker major version, forge, backend, deployment files, database, public URL, and whether the task is a setup, pipeline…
  2. Read the matching reference below before changing configuration.
  3. Render and lint configuration before starting the server or agent.
  4. Make the smallest change at the layer that owns the problem: forge/OAuth, server, agent/backend, or repository workflow.
  5. Verify the result at the next boundary: server health, agent connected state, repository webhook, pipeline scheduling, step logs, and…
  6. Record the exact version and relevant environment variables without recording secret values.

What it can do on your machine

Read from SKILL.md and the folder at commit 22b4723. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • docker
    • openssl
    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use docker, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • WOODPECKER_AGENT_SECRET

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires access to a Woodpecker instance for administration; Docker Compose, Kubernetes, or woodpecker-cli are optional depending on the backend.

    From compatibility in the SKILL.md frontmatter.

Context cost

Woodpecker CI loads about 2.5k tokens when it runs, and up to ~10k if it reads all its reference files. Until then it costs about 111 tokens; SKILL.md has 1,062 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~111
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~10k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:52
    cp templates/forgejo.env.example .env
  • NoteMentions a .env fileSKILL.md:53
    # edit .env, then:

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from magnus919/agent-skills at commit 22b4723, republished under its MIT licence (© magnus919). 1,062 words, ~2,464 tokens.

Download SKILL.mdSave it as .claude/skills/woodpecker-ci/SKILL.md (or your agent's skills folder). This skill also uses 16 other files; get the full folder from GitHub.
name
woodpecker-ci
description
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.
compatibility
Requires access to a Woodpecker instance for administration; Docker Compose, Kubernetes, or woodpecker-cli are optional depending on the backend.
license
MIT
metadata.source
https://woodpecker-ci.org/docs
metadata.research
GroktoCrawl plus official Woodpecker documentation and repository sources

Woodpecker CI

Use this skill as an operating playbook, not as a substitute for checking the documentation for the installed major version. Prefer explicit SemVer image tags, verify the resolved configuration before starting services, and verify runtime health after every change.

Scope boundary: This skill configures and operates Woodpecker CI (the server, agents, workflows, and integrations). It does not install or administer Forgejo/Gitea itself, and it does not configure Forgejo Actions runners. When a task mentions Forgejo, treat it as the forge Woodpecker connects to unless forge administration is explicitly requested.

Operating loop

  1. Identify the Woodpecker major version, forge, backend, deployment files, database, public URL, and whether the task is a setup, pipeline, or incident.
  2. Read the matching reference below before changing configuration.
  3. Render and lint configuration before starting the server or agent.
  4. Make the smallest change at the layer that owns the problem: forge/OAuth, server, agent/backend, or repository workflow.
  5. Verify the result at the next boundary: server health, agent connected state, repository webhook, pipeline scheduling, step logs, and external deployment endpoint.
  6. Record the exact version and relevant environment variables without recording secret values.

Choose the entry point

If the task is...Start here
New server/agent or forge connectionreferences/setup.md
Workflow YAML, services, conditions, secrets, or pluginsreferences/pipeline-syntax.md
Upgrade, backup, metrics, CLI, or capacityreferences/operations.md and references/advanced-patterns.md
A failed login, queued pipeline, clone, step, or backendreferences/troubleshooting.md and references/failure-signatures.md
A trust or multi-tenant decisionreferences/security.md and the backend section of references/setup.md
Local lint/exec or CLI installationreferences/cli.md

Then load references/source-index.md when a version-sensitive command or variable needs confirmation.

Quick command card

bash
# Generate a shared server/agent secret
openssl rand -hex 32

# Compose Woodpecker server + agent (not Forgejo itself)
cp templates/docker-compose.yml compose.yaml
cp templates/forgejo.env.example .env
# edit .env, then:
docker compose config --quiet
docker compose up -d
docker compose ps
docker compose logs -f --tail=100 woodpecker-server woodpecker-agent

# Local workflow checks
woodpecker-cli lint .woodpecker.yml
woodpecker-cli exec .woodpecker.yml

# Pipeline and secret administration
woodpecker-cli repo info --repository OWNER/REPO
woodpecker-cli repo secret add --repository OWNER/REPO --name NAME --value @/path/to/value

woodpecker-cli exec is useful for local command and metadata checks, but it is not a complete substitute for a server run: server-managed secrets and forge events may not be available locally.

Core defaults

  • Use the Docker backend for isolated container steps. The Docker socket is powerful: treat an agent host as a CI trust boundary.
  • Use the Kubernetes backend for pod-per-step isolation and cluster scheduling; review service accounts, namespace boundaries, pull secrets, PVC/storage, and resource requests before allowing repository authors to set backend options.

Multi-tenant default: For untrusted repositories, prefer Kubernetes with namespace/RBAC/ServiceAccount controls when the cluster is already operated as a security boundary. Docker is a viable simpler default only with dedicated agents and trust-tier separation because the Docker socket controls the host daemon. Never use the Local backend for untrusted repositories.

  • The agent needs WOODPECKER_SERVER and the same WOODPECKER_AGENT_SECRET as the server. The server registers an agent on first contact; persist the agent config file so its generated identity survives restarts.
  • Keep WOODPECKER_OPEN=false unless open registration is intentional. Grant admin access explicitly and protect the OAuth client secret and agent secret.
  • Do not expose secrets to untrusted pull requests by default. If a secret must be available there, restrict its events and plugin images and document the threat model.
  • depends_on is for workflow ordering and parallelism; it is not a readiness check for service containers. Add a real wait/backoff or health probe for databases and caches.
  • A step's when list is OR across entries and AND within one entry. Branch filters also affect pull-request target branches; combine event and branch when you mean pushes to a branch only.
  • A passing docker compose config or CLI lint proves syntax/model validity, not that the agent can reach the forge, the image can pull, or the pipeline is safe.

Reference routing

Load whenReference
Installing with Docker Compose, configuring Forgejo/Gitea, or choosing a backendreferences/setup.md
Writing workflow YAML, events, conditions, matrices, services, plugins, or multi-workflow projectsreferences/pipeline-syntax.md
Designing parallel workflows, concurrency, caching, registries, reusable YAML, or autoscalingreferences/advanced-patterns.md
Managing secrets, registries, CLI operations, upgrades, backups, and metricsreferences/operations.md
A pipeline, clone, agent, OAuth, Docker, or Kubernetes run is failingreferences/troubleshooting.md
You need a compact symptom-to-evidence map during an incidentreferences/failure-signatures.md
Reviewing trust boundaries, pull requests, plugins, local backend, or Kubernetes permissionsreferences/security.md
Using or installing woodpecker-clireferences/cli.md
Checking source URLs and version-sensitive claimsreferences/source-index.md
Show full SKILL.md (402 more words)Show less

Included artifacts

  • templates/docker-compose.yml — minimal server plus Docker agent deployment.
  • templates/woodpecker.yml — conservative build/test/deploy workflow skeleton.
  • templates/forgejo.env.example — placeholder environment contract for Forgejo.
  • assets/troubleshooting-checklist.md — incident handoff checklist.
  • scripts/woodpecker-doctor.py — dependency-free connectivity/configuration probe with text or JSON output.

Available Scripts

ScriptPurposeInvocation
scripts/woodpecker-doctor.pyDependency-free connectivity/configuration probe for a Woodpecker deployment: checks the server HTTP URL and the agent gRPC endpoint, with text or JSON output. Run it as step one of any "is it the CI or is it me" diagnosis, after deployment changes to verify health, and before deeper troubleshooting.python3 scripts/woodpecker-doctor.py --url https://ci.example.com --server agent-host:9000 --json

Prerequisites

  • Network reachability from where you run the probe to the Woodpecker server URL and, when checking agents, the agent's gRPC host:port.
  • Python 3 only — the probe uses the standard library and no third-party packages; it never mutates the Woodpecker instance.
  • For actual administration beyond probing (per compatibility): access to the Woodpecker instance, and Docker Compose, Kubernetes, or woodpecker-cli depending on the backend in use — see references/setup.md and references/cli.md.

Limitations

  • The doctor probe reports connectivity and configuration signals only; a green probe does not prove forge OAuth works, pipelines schedule, or steps can pull images — verify at those boundaries separately (see Common failure boundaries).
  • It reads nothing about repository-level state (webhooks, secrets, queue depth); use the server API/UI and references/troubleshooting.md for that.
  • The skill documents operating patterns, not a pinned version: always confirm version-sensitive commands and variables against the installed major version via references/source-index.md.

Common failure boundaries

  • Server starts but repositories do not appear: inspect forge OAuth URL, callback URL, scopes, and server logs before changing pipeline YAML. For a push with no pipeline, inspect the Forgejo repository's Settings → Webhooks → Recent Deliveries first and record the delivery status.
  • Agent is connected but never picks up work: compare labels/backend, maximum workflows, repository visibility/trust, and agent logs.
  • A clone fails: first test network/DNS and credentials from an intentionally paused step; do not debug application commands until checkout works.
  • A service is running but tests fail to connect: use the service hostname and container port, then add readiness handling.
  • A secret is empty: check secret scope, event filters, plugin-image filters, and expression escaping ($${NAME} when Woodpecker must pass the variable to the shell).

When not to use

Use a forge-specific skill for installing or administering Forgejo/Gitea itself, a Kubernetes operations skill for cluster lifecycle, and a Docker security skill for host hardening. This skill covers Woodpecker's integration points and CI behavior.

© magnus919, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 16 other files (scripts, references, assets) in woodpecker-ci of magnus919/agent-skills.

  • SKILL.md
  • README.md
  • assets/troubleshooting-checklist.md
  • evals/evals.json
  • references/advanced-patterns.md
  • references/cli.md
  • references/failure-signatures.md
  • references/operations.md
  • references/pipeline-syntax.md
  • references/security.md
  • references/setup.md
  • references/source-index.md
  • references/troubleshooting.md
  • scripts/woodpecker-doctor.py
  • templates/docker-compose.yml
  • templates/forgejo.env.example
  • … and 1 more

Open the folder on GitHubat commit 22b4723

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Deepseek Harness Dockerrunzhliu/deepseek-harness-docker110—~2.7kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Woodpecker CI

What does Woodpecker CI do?

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…. Woodpecker CI is an agent skill from magnus919/agent-skills. 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.

When should I use Woodpecker CI?

Woodpecker CI fits situations like: debugging Woodpecker CI; unrelated requests; route to the nearest named specialist.

How do I install Woodpecker CI in Claude Code?

Run `npx skills add magnus919/agent-skills --skill woodpecker-ci -a claude-code`. Or copy the skill folder (woodpecker-ci in magnus919/agent-skills) into .claude/skills/woodpecker-ci in your project. Claude Code loads it when a task matches its description.

How do I install Woodpecker CI in Codex?

Run `npx skills add magnus919/agent-skills --skill woodpecker-ci -a codex`. Or copy the skill folder (woodpecker-ci in magnus919/agent-skills) into .agents/skills/woodpecker-ci in your project. Codex loads it when a task matches its description.

Can I use Woodpecker CI in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add magnus919/agent-skills --skill woodpecker-ci -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/woodpecker-ci, .gemini/skills/woodpecker-ci, .github/skills/woodpecker-ci and .opencode/skills/woodpecker-ci in your project.

What does Woodpecker CI need to run?

Going by SKILL.md and its folder, Woodpecker CI needs Python for the scripts in its folder, the command-line tools its instructions call (docker, openssl and python3) and credentials named WOODPECKER_AGENT_SECRET. Our summary lists: Python 3; Docker; A credential in WOODPECKER_AGENT_SECRET. Compatibility (from SKILL.md): Requires access to a Woodpecker instance for administration; Docker Compose, Kubernetes, or woodpecker-cli are optional depending on the backend..

Does Woodpecker CI access the network?

SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Woodpecker CI safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Woodpecker CI use?

Woodpecker CI is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Woodpecker CI use?

About 2.5k tokens (SKILL.md is roughly 9.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.7k tokens, read only when the agent opens those files.

What are the alternatives to Woodpecker CI?

Skills that share tags, products or a category with Woodpecker CI: LangBot Deployment Guide (langbot-app/LangBot, 18k stars), Build Openshell Mxc Windows (NVIDIA/OpenShell, 16k stars), Devops (nicepkg/auto-company, 195 stars) and Debug Openshell Cluster (NVIDIA/OpenShell, 16k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Woodpecker CI?

magnus919 (a GitHub user) maintains it in magnus919/agent-skills, which has 115 GitHub stars. The repository holds 131 skills in this directory. The repository was last updated on October 10, 2026.

Source: magnus919/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.