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Flaker Storage Cache On Ci

Community
mizchi
flaker-storage-cache-on-ci

Persist flaker's DuckDB storage across GitHub Actions runs and feed it from multiple sources (vitest reports, custom adapter reports, etc.). Use when wiring `@mizchi/flaker` into a new repo's CI, adding a new ingest source to an existing flaker setup, or debugging why `flaker apply` / `flaker run --gate ...` "lost its history" between runs. Encodes the cache key shape, fetch-depth requirements, `--changed` derivation, and the import-step placement that internal flaker users converged on.

Overview

Publishermizchi
Repositoryskills
Skill nameflaker-storage-cache-on-ci
Stars
333
Forks
4
Bundled files
Instructions only
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 mizchi on GitHub. Read the source before you install it.

Installation

Install the Flaker Storage Cache On Ci 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.
  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/mizchi/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/flaker-storage-cache-on-ci .claude/skills/flaker-storage-cache-on-ci
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Flaker Storage Cache On Ci 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 Flaker Storage Cache On Ci 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 Flaker Storage Cache On Ci 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.

flaker storage cache on GitHub Actions

flaker keeps its data in a DuckDB file at the path declared by flaker.toml [storage] path (default .flaker/data). For flaky detection / KPI / quarantine to work, that file must persist between CI runs. GitHub Actions has no first-class runtime storage, so the convention is actions/cache@v6 with a sliding key.

When this skill applies

  • "新しい repo に flaker を CI で動かす"
  • "flaker の履歴が CI で消えてる / 毎回ゼロからになる"
  • "flaker に を import するワークフロー追加して"
  • "VRT / playwright / custom-adapter report を flaker に流したい"

Cache key shape

yaml
- name: Cache flaker data
  if: always()
  uses: actions/cache@v6
  with:
    path: .flaker/data
    key: flaker-data-${{ github.run_id }}
    restore-keys: |
      flaker-data-
  • key uses github.run_id so each run writes a new cache entry on save (GH Actions saves at end of job).
  • restore-keys: flaker-data- matches anything written previously, falling back to the most recent — so the next run sees the latest accumulated state regardless of which earlier run produced it.
  • if: always() so a failed earlier step still triggers the save (history-on-failure is fine and often more useful than history-only-on-success).

The path MUST equal flaker.toml's [storage] path. Default is .flaker/data (a file, not a directory — pre-creating it as a dir breaks the DuckDB open).

In a pnpm workspace monorepo: prefix the cache path with the package directory, e.g. packages/<pkg>/.flaker/data. flaker does NOT walk up to find flaker.toml — it must live next to where flaker is invoked. Mismatched cache path vs flaker.toml location surfaces as Config file not found (when running from repo root) or "history vanished every run" (when cache and flaker disagree on where storage is).

Triggering writes

actions/cache@v6 saves automatically in its post-step. Callers don't cache save explicitly. The save key is the run-id, so duplicate writes never collide.

fetch-depth for --changed derivation

Any flaker invocation that uses the hybrid / affected strategy (flaker run --gate merge in CI profile, by default) needs --changed <files,...>. Without it: Error: hybrid mode requires resolver and changedFiles.

yaml
- uses: actions/checkout@v7
  with:
    fetch-depth: 0  # need history for `git diff` against the PR base
yaml
- name: flaker run --gate merge
  env:
    BASE_REF: ${{ github.event.pull_request.base.ref }}
  run: |
    changed=$(git diff --name-only "origin/${BASE_REF}...HEAD" | tr '\n' ',' | sed 's/,$//')
    pnpm exec flaker run --gate merge --changed "$changed"

Empty diff (config-only PR, etc.) is fine — hybrid falls back to the configured fallback strategy.

Workflows: which one persists, which one reads

Reference layout for splitting workflows by trigger:

WorkflowTriggerReads cacheWrites cacheNotes
flaker-nightly.ymlcron + workflow_dispatchyesyesRuns flaker apply. The canonical writer of vitest history.
<source>-baseline.ymlpush to main + cronyesyesImports a custom-adapter report via flaker import --adapter <name>. Same cache key.
flaker-pr.ymlpull_requestyes(yes implicitly, harmless)Advisory only, continue-on-error: true.
<source>-pr-gate.ymlpull_requestNONOPR-scoped runs would distort the population — keep them ephemeral.

Rule of thumb: anything that touches main / scheduled writes; PR-scoped checks read-only or no cache.

Adding a new ingest source

Common pattern (custom adapter, see flaker#79 for the adapter contract):

  1. Produce the report file (e.g. <source>-report.json, vitest-report.json).
  2. Restore the cache (or rely on the same job's earlier restore step).
  3. Import:
    yaml
    - name: Import <source> into flaker
      if: always()
      run: |
        pnpm exec flaker import <report-file> \
          --adapter <name> \
          --commit "${{ github.sha }}" \
          --branch "${{ github.ref_name }}" \
          --source ci
  4. The cache save at end-of-job picks up the new rows automatically.

if: always() ensures partial-failure runs still record what they saw before the failure.

Don't do this

  • Don't pre-create .flaker/data as a directory before flaker runs. DuckDB expects to open it as a file; IO Error: Could not read from file ... Is a directory is the symptom. The cache path: .flaker/data referencing a not-yet-existing file is fine — actions/cache restores it if present, and flaker creates it if not.
  • Don't add flaker import to PR-only workflows without thinking. PR runs happen on every commit-to-PR and would dominate the population. Keep PR jobs read-only against the cache.
  • Don't use NODE_OPTIONS=--preserve-symlinks-main with flaker ≥ 0.10.7. It was a workaround for a pnpm symlink bug fixed in 0.10.7; under 0.11.x the env var silently turns the CLI into a no-op, exit 0 (no output, no work done). Surfaced via downstream usage, removed in flaker upstream.
  • Don't try to write the cache from a PR fork. GH Actions disallows writes from forks for security reasons; the restore still works, the save silently no-ops.

Diagnostics

If history "vanishes":

  1. Check the cache hit log line in the workflow run — if it says Cache not found for input keys, the restore-keys prefix doesn't match what was saved.
  2. Check flaker.toml [storage] path matches the cached path: exactly.
  3. Run flaker doctor in the same job — confirms the file is open-able.
  4. flaker status --markdown exposes row counts so you can see what was actually loaded.

Reference

  • flaker itself (docs/contributing.md) for the storage path convention.
  • The --adapter system docs for writing a custom report importer.

Frequently asked questions

What does the Flaker Storage Cache On Ci AI skill do?

Persist flaker's DuckDB storage across GitHub Actions runs and feed it from multiple sources (vitest reports, custom adapter reports, etc.). Use when wiring `@mizchi/flaker` into a new repo's CI, adding a new ingest source to an existing flaker setup, or debugging why `flaker apply` / `flaker run --gate ...` "lost its history" between runs. Encodes the cache key shape, fetch-depth requirements, `--changed` derivation, and the import-step placement that internal flaker users converged on.

Why use Flaker Storage Cache On Ci on TypingMind?

Because you install it once and use it with any model. Flaker Storage Cache On Ci 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 Flaker Storage Cache On Ci in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mizchi/skills/tree/main/flaker-storage-cache-on-ci. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Flaker Storage Cache On Ci?

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 Flaker Storage Cache On Ci?

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

Is the Flaker Storage Cache On Ci AI skill free?

It is published on GitHub by mizchi. Check the repository for licensing terms. 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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