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Reproducing Ci Locally

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kajisho5
reproducing-ci-locally

Run the CI gate on your machine so it agrees with the runner — deriving the exact command, paths, markers, and env from the workflow file instead of the Makefile, unblocking gate steps that short-circuit and hide the next failure, pinning the linter version CI resolves, building the interpreter/toolchain environment the runner builds, and confirming the run is green instead of explaining a red job away. Use when a check passes locally but fails in CI (or the reverse), when a lint/format job goes red on an untouched file, when setting up a local dev loop for an unfamiliar repo, or before pushing a branch you expect to merge.

Overview

Publisherkajisho5
Repositoryffmpeg-skill
Skill namereproducing-ci-locally
Stars
1.8K
Forks
144
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by kajisho5 on GitHub. Read the source before you install it.

Installation

Install the Reproducing Ci Locally AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/kajisho5/ffmpeg-skill/tree/main/.claude/skills/reproducing-ci-locally
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kajisho5/ffmpeg-skill.git /tmp/ffmpeg-skill
mkdir -p .claude/skills
cp -r /tmp/ffmpeg-skill/.claude/skills/reproducing-ci-locally .claude/skills/reproducing-ci-locally
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reproducing Ci Locally in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Reproducing Ci Locally on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Reproducing Ci Locally is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Reproducing CI Locally

A local check is only useful if it runs the same thing the runner runs. Most "green locally, red in CI" failures are not bugs in the code — they are a difference between two commands: different paths, different test markers, different env, a different linter version, or a different interpreter.

The fix is mechanical: derive the local command from the workflow file, not from the Makefile, not from habit, not from what the last repo used.

Read the workflow before you run anything

The workflow is the contract. The Makefile is a convenience that drifts from it.

bash
# What the gate actually is, in order
sed -n '/jobs:/,$p' .github/workflows/ci.yml

# Every command CI runs, across all workflows
grep -rn "run:" .github/workflows/

Copy out four things, verbatim:

  1. The commands and their order.
  2. The paths each command is scoped to (ruff check app tests scripts is not ruff check .).
  3. Test selection — marker expressions, -k filters, which suites are excluded.
  4. The env: block, and the runtime/toolchain versions in setup-* steps.

Each of those four is a distinct way to get a wrong answer locally.

Paths. If CI lints app tests scripts and you run ruff check ., you get findings from directories CI never looks at — a red that isn't a merge blocker and shouldn't be "fixed" in an unrelated PR. Run it the narrow way to reproduce the gate; run it the wide way only when you're deliberately auditing.

Markers. A suite-wide make test that excludes one marker is not the CI gate if CI excludes six. Live-credential integration tests deselected in CI will run locally, hit a fake key, and fail in a way that looks like a regression:

bash
# Wrong: local shorthand — pulls in suites CI never runs
pytest -m "not browser"

# Right: the full expression, copied from the workflow
pytest -m "not browser and not slow and not load and not integration"

Env. Config objects instantiated at import time (a settings singleton at module scope, an engine built when the module loads) make collection fail without the workflow's variables — a wall of "Field required" errors that looks like a broken suite. Mirror the env: block, including the shape of values: if CI passes a Postgres URL and the module builds a pooled engine, a local SQLite URL raises on arguments that dialect rejects before a single test runs.

Keep those values in a gitignored .env.ci copied from the workflow's env: block, so the local command is the workflow command plus one set -a:

bash
set -a; . ./.env.ci; set +a
pytest -m "not browser and not slow and not load and not integration"

A short-circuiting gate hides the next failure

Gate steps run in order and the job stops at the first red. So the CI log shows you one failure even when three are waiting:

yaml
- run: ruff check .          # fails here …
- run: ruff format --check . # … so this never runs, and you never see it

You fix the lint error, push, and get an immediate second red for formatting. Same shape everywhere: cargo fmt --all -- --check before cargo clippy --all-targets -- -D warnings before cargo test means a formatting failure tells you nothing about whether clippy or the tests pass.

Run every gate step locally, even after one fails. Don't &&-chain them while diagnosing — run them separately and collect the whole set:

bash
ruff check app tests scripts;  echo "lint:   $?"
ruff format --check app tests; echo "format: $?"
pytest -m "not integration";   echo "tests:  $?"

The corollary: after a red job, never report "only X is broken." Everything downstream of X is unmeasured until you run it.

Pin what gates the build, and reproduce the version CI resolves

An unpinned gating tool means the gate changes without a commit. A range like ruff>=0.4.0 resolves to whatever shipped this morning, and a release that widens file coverage — a formatter that starts formatting code blocks inside Markdown, a linter that promotes a rule to default — turns every open PR red on files nobody touched.

Two habits:

  • Pin the linter, formatter, and toolchain in the manifest, and bump them in a dedicated PR where the reformat is the whole diff.
  • Reproduce with the version CI resolves, not the one you happen to have:
bash
uvx ruff@0.16.4 format --check .      # exactly what the runner would install

# Node: CI does `npm ci` then `npx prettier --check web` — that's the LOCKFILE's
# prettier. A bare `npx prettier` fetches the latest and flags files CI is fine
# with. Read the pinned version, then ask for it.
grep -m1 -A2 '"node_modules/prettier"' package-lock.json
npx -y prettier@3.8.3 --check web

Formatting a file CI never complained about is not a fix — it's an unrelated diff caused by using a different tool than the gate.

Build the environment the runner builds

Package managers will happily invent an environment for you, and the one they invent is not CI's.

  • A fresh clone or worktree has no virtualenv. uv run <tool> silently creates a bare one without your dev extras, then fails with Failed to spawn: ruff — which reads like a missing dependency rather than a missing environment.
  • uv run re-syncs from the lockfile against your host interpreter. On a Python newer than CI's matrix, a pinned dependency with no wheel for that version gets built from source and fails on a compiler error that has nothing to do with your change.
  • Extras differ. If CI installs [dev,web] and make install installs [dev], the full suite errors at collection locally on an import CI has.

Build it explicitly, at CI's interpreter version, with CI's extras:

bash
uv venv .venv --python 3.12 --seed
uv pip install --python "$PWD/.venv/bin/python" -e ".[dev,web]"
.venv/bin/python -m ruff check app tests scripts
.venv/bin/python -m pytest -m "not integration"

Driving the tools as .venv/bin/python -m <tool> sidesteps the re-sync entirely. If you prefer uv run, pass --no-sync. And prefix with env -u VIRTUAL_ENV when a shell profile exports one — otherwise the run is silently redirected into an unrelated environment and its results mean nothing.

Fix divergence in shared config, not in the workflow

When you find a difference, ask where the fix belongs. A flag added to the workflow YAML fixes CI and leaves every local run diverging — so the next person hits the same confusion.

Prefer the file both sides read:

  • Test-runner flags → addopts in pyproject.toml, not the workflow's run:. (Import-mode is the classic one: a source directory on sys.path shadowing an installed compiled package is a config problem, and pinning --import-mode=importlib in addopts fixes local and CI together.)
  • Marker definitions, coverage thresholds, lint rules and target version → the project manifest.
  • Keep requires-python and the linter's target-version in sync; a mismatch means the linter applies rules for a runtime you don't support.

The workflow should read as make lint / make test plus the environment. When it contains flags the local target doesn't, that's the divergence.

Know which checks are actually gates

Not every command in the repo is a merge blocker, and treating them as equal wastes PRs.

bash
# Which jobs are required is a repo setting, not a file — check it
gh api repos/OWNER/REPO/branches/main/protection --jq '.required_status_checks.contexts'

If CI runs the linter but not the type checker, then a pre-existing type error in an untouched module is not blocking your PR — don't fold a speculative fix for it into an unrelated change, and don't claim CI verifies types. The inverse matters too: a helper target like make quality-check that runs more than CI will show you reds that no one is gating on.

Finish by confirming the run, not by explaining it

"Passes locally" is a prediction. Wait for the real result:

bash
gh pr checks --watch
gh run view --log-failed        # the failing step's output, not the summary

When a job is red, fix it in the same PR if the fix is feasible. If you believe it's pre-existing, prove it: check out the base commit and run the same command there. An unverified "pre-existing / out of scope" is how a base branch becomes permanently red.

Two traps in the log itself:

  • A step gated on an event (if: github.event.action == 'opened') is skipped when you re-run by pushing a commit. Green-on-rerun can mean not run.
  • A permissions failure at the last step (an HTTP 403 posting a comment) shows every build/test step green with a red X on the job — read which step failed before concluding the code is broken.

Checklist

Before running anything:
- [ ] Read .github/workflows/*.yml — commands, order, paths, markers, env, versions
- [ ] Local command uses CI's paths (not `.`) and CI's full marker expression
- [ ] Workflow env: block mirrored, including value shape (DB URL dialect, etc.)

Environment:
- [ ] venv created explicitly at CI's runtime version, with CI's extras
- [ ] Tools driven from that venv (`.venv/bin/python -m …` or `--no-sync`)
- [ ] `env -u VIRTUAL_ENV` when a shell profile exports one
- [ ] Gating linter/formatter/toolchain pinned; local run uses the pinned version

Running:
- [ ] Every gate step run separately — a first failure hides the rest
- [ ] Formatter check run even when the linter passed (they are different tools)

Fixing:
- [ ] Divergence fixed in shared config (manifest/addopts), not only in the workflow
- [ ] Checked which jobs are actually required before treating a red as blocking
- [ ] Waited for the real run; any red either fixed here or proven on the base commit

Note for this repository (ffmpeg-skill)

This repo's gate is .github/workflows/ci.yml: install ffmpeg per-OS (apt / brew install ffmpeg-full / choco install ffmpeg), then python tests/test_all.py and python tests/test_contract.py (unittest, not pytest — there is no marker expression to copy, but there IS an OS-conditional: a handful of test_contract.py tests are skipIf'd on Windows because they depend on a POSIX shell shim, not on CI's own if: gating). Read the actual workflow file before assuming a local npm test run matches — npm test runs both files with no OS-conditional skip logic layered on top, so on a non-Windows machine it is already a faithful local reproduction; the gap only shows up when debugging a Windows-specific CI failure, where the fix is to read what skipIf actually excludes before assuming a fix applies everywhere.

Source: wdm0006/python-skills (MIT).

Frequently asked questions

What does the Reproducing Ci Locally AI skill do?

Run the CI gate on your machine so it agrees with the runner — deriving the exact command, paths, markers, and env from the workflow file instead of the Makefile, unblocking gate steps that short-circuit and hide the next failure, pinning the linter version CI resolves, building the interpreter/toolchain environment the runner builds, and confirming the run is green instead of explaining a red job away. Use when a check passes locally but fails in CI (or the reverse), when a lint/format job goes red on an untouched file, when setting up a local dev loop for an unfamiliar repo, or before pus...

Why use Reproducing Ci Locally on TypingMind?

Because you install it once and use it with any model. Reproducing Ci Locally is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Reproducing Ci Locally in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kajisho5/ffmpeg-skill/tree/main/.claude/skills/reproducing-ci-locally. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Reproducing Ci Locally?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Reproducing Ci Locally?

As many as you like. As long as a model supports skills, you can use Reproducing Ci Locally with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Reproducing Ci Locally AI skill free?

Yes. It is published on GitHub by kajisho5 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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