agentleFS
ianshankianshank/Agents/skills/deploy/SKILL.md

deploy

Skill1 starsChanged 58 days agoApache-2.0View on GitHub

Generate a safety-railed deployment shell script (scripts/deploy.sh) with bash strict mode, a --dry-run mode, a confirmation gate before irreversible steps, rollback, and a health-check retry loop. Use this whenever the user wants a deploy script, a release/rollback workflow, deployment automation with a dry-run or confirmation prompt, a health-check-after-deploy step, or a safe repeatable deployment procedure. Config is passed as flags (app, artifact, health URL, environment) and lands as environment-overridable defaults — secrets are never inlined.

155 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use pm-discovery, qa-phase and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • ww-w-aiSkill

    pm-discovery

    PM Agent Team - Automated product discovery, strategy, and PRD generation. Runs PM analysis workflow to produce comprehensive PRD before PDCA Plan phase.

    Also in 3 projects that use this

  • ww-w-aiSkill

    qa-phase

    Run QA tests at various levels (L1-L5) for features under PDCA management. Integrates with MCP tools to execute structured test plans.

    Also in 3 projects that use this

  • +10 more from ww-w-ai

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 139 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    No secret or credential is written into the script — all config is ${VAR:-default} and overridable from the environment (ADR-0009 baseline).

    line 47

“Common” is measured: kept by at least 6 in 10 of the 139 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

deploy — deployment-script writer

Emit a scripts/deploy.sh whose value is its safety structure, not platform-specific deploy logic: set -euo pipefail, a --dry-run mode that prints instead of acting, a confirmation gate before irreversible steps, a health-check retry loop, a rollback path, and structured logging. You fill in the per-step commands (clearly marked TODO); the scaffold guarantees the rails. Output is byte-stable and ShellCheck-clean.

1. Preconditions (input contract)

  • A target project directory exists.
  • The deployment target is known well enough to pass as flags: app name, artifact/image reference, health-check URL, environment. Unspecified critical values default to <...> placeholders that the script refuses to run against (fail fast).
  • Python 3.10+ (no third-party dependency — config is flag-driven, not detected).

2. Procedure (the E2E steps)

python scripts/gen_deploy.py --app <name> --artifact <ref> \
    --health-url <url> --environment <env> [--out <path>] [--stdout] [--check]
  1. Collect the deployment config as flags. Do not put secrets on the command line or in the script — the script reads them from the environment at run time.
  2. Emit scripts/deploy.sh with build | release | rollback | health-check subcommands, a run wrapper honouring --dry-run, a confirm gate (skipped by --yes/--dry-run), a require guard that aborts on unfilled <placeholders>, and a health-check retry loop.
  3. Fill in each TODO step (replace the true placeholder with your platform's command).
  4. Review and commit. Verify with ./scripts/deploy.sh --dry-run --yes release — it should print the intended actions and change nothing.

3. Output contract (postconditions — what "done" means)

  • scripts/deploy.sh is written (mode +x), starts with #!/usr/bin/env bash, sets -euo pipefail, ends in exactly one newline (LF), and re-renders byte-identically.
  • --dry-run performs no side effects and exits 0; the confirmation gate aborts non-zero when declined; an unfilled placeholder value aborts non-zero; an unknown subcommand exits 2.
  • No secret or credential is written into the script — all config is ${VAR:-default} and overridable from the environment (ADR-0009 baseline).

4. Failure handling

  • The require guard fails fast on any <placeholder> still present, so a half-configured deploy aborts instead of doing something surprising.
  • --check is advisory (see §5), never a hard gate.

5. --check is advisory, not a gate

--check exits 1 when the committed script differs from a fresh render — an optional drift signal. Because you extend the script with real deploy commands, do not wire --check into CI as a blocking gate.

6. Validation gate (before declaring success)

You are not done until this exits 0:

python scripts/validate_skill.py --skill . --tier structural,behavioral

7. Examples

Example 1 — dry run is side-effect-free ./scripts/deploy.sh --dry-run --yes release prints DRY-RUN: ... lines for the release and health-check steps and exits 0 without touching anything.

Example 2 — fail fast on missing config Running release with ARTIFACT left at its <artifact> placeholder aborts non-zero with unconfigured value — the deploy never proceeds half-configured.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.