agentleFS
lucastosettolucastosetto/dev-skills/skills/deploy/SKILL.md

deploy

Skill0 starsChanged 25 days agoMITView on GitHub

Deploy to staging or production the way this project does it. staging: ship the current branch to the shared/dev environment. prod: build the changelog (what ships, dependencies, migrations, deploy order, post-deploy tasks), deploy after confirmation, smoke test read-only, monitor, and roll back (flag off, platform rollback, revert PR) if the deploy caused a problem. Use on "deploy to staging", "ship to dev", "what will ship?", "release to production", "deploy prod", "roll back", "/deploy staging", "/deploy prod".

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

6 rules. 0 are common, 3 are distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 139 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

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

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

    Follow ../../shared/policy.md.

    line 15
  2. Distinctive:

    Make sure the branch is committed and pushed (push goes on the confirmation list).

    line 22
  3. Distinctive:

    After approval: follow the pipeline (CI status via the host's CLI/MCP) and report the result and URL.

    line 27
  4. Very specific:

    Monitor: run dev-skills:observe comparing after vs the hour before the deploy, focused on the changed areas plus global error rate and …

    line 58
  5. Distinctive:

    When verification shows a problem caused by this deploy (evidence: started at deploy time, in changed code, not system-wide before):

    line 65
  6. Very specific:

    Not caused by the deploy: say so with the evidence and suggest dev-skills:investigate.

    line 71

“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

Follow ../../shared/policy.md. Run dev-skills:project-profile first. Methods, flags and rollback options: ../../shared/references/deploy.md. Apply the profile's ### deploy notes (caveats, order, post-deploy tasks) and show the relevant ones in each confirmation line. If the profile names a project skill for a step, run it for that step.

staging

  1. Method from the profile (deploy.staging). Unknown: discover it (deploy.md), else report the gap.
  2. Make sure the branch is committed and pushed (push goes on the confirmation list).
  3. Prepare locally what can be prepared: e.g. for a shared staging branch, merge the feature branch into an up-to-date local copy of it; conflicts via dev-skills:resolve-conflicts. Never reset or rewrite a shared staging branch.
  4. Confirmation list: the push/trigger that deploys.
  5. After approval: follow the pipeline (CI status via the host's CLI/MCP) and report the result and URL.

prod

1. Changelog (/deploy prod changelog stops here)

  • Find what is in production now (deploy.md "What is in production") and the ref to ship (default branch head unless given).
  • git log <prod ref>..<ship ref>: group into PRs and tickets; one line each with author.
  • Flag for attention: migrations (and whether they are backward compatible with the running code), new env vars/config, new dependencies, feature flags to create or flip, data backfills/rake/ management tasks, cron/job changes, API contract changes that other apps consume.
  • Cross-app dependencies: tickets or PRs in related_repos that must ship first or together; the profile's deploy.order and notes.
  • Post-deploy tasks: from PR descriptions, migration notes and profile notes.
Shipping <ship ref> over <prod ref> (<n> PRs)
- <PR link> <title> — <ticket> — <author>
Attention:
- Migration <name>: <safe | needs X first>
- Env var <NAME> must exist in prod
Order: <app A → app B | n/a>
After deploy: <task commands>

2. Deploy

Confirmation list (always, every mode): the exact trigger (command, workflow run, promote, merge), plus env/config changes and pre-deploy tasks in order. After approval, run it and follow the pipeline until it finishes; report failures with the decisive log line.

3. Verify

  • Smoke test, read-only: load the main pages and the changed flows with the browser and GET the changed/critical endpoints. Never submit forms, create, update or delete anything in production.
  • Monitor: run dev-skills:observe comparing after vs the hour before the deploy, focused on the changed areas plus global error rate and latency. Repeat at about 5, 15 and 30 minutes after the deploy (or the profile's schedule), using scheduled wakeups if the session supports them.
  • Post-deploy tasks: run the ones that are local/read-only; queue the rest (data tasks in prod are handed to the user or confirmation-listed).

4. Rollback (also /deploy prod rollback)

When verification shows a problem caused by this deploy (evidence: started at deploy time, in changed code, not system-wide before):

  • Pick the fastest safe option from deploy.md "Rollback options" (flag off first when available; check migration compatibility before a platform rollback).
  • Confirmation list, every mode, with the evidence in one line. Execute after approval, then observe again to confirm recovery. Not caused by the deploy: say so with the evidence and suggest dev-skills:investigate.

Report

<env> — <ship ref> — <status>
Changelog: <n PRs> (above)
Smoke: <pass | failures>
Monitor (+5/+15/+30m): <verdicts>
Rollback: <not needed | proposed | done — how>
<confirmation list>

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.