本文へ移動
cccskills
無料GitHub で公開

regression-test

Manage golden dataset regression tests for LLM prompts using Promptfoo. Commands — init, add, run, report. Use when user says "regression", "golden dataset", "prompt regression", "eval run", "test my prompts", or after shipping prompt changes.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md5.8 KB
  • skill-report.json40.5 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Regression Test: Golden Dataset Management

Manage golden test datasets for LLM prompt regression testing using Promptfoo. Ensures prompt changes don't degrade output quality.

One eval tells you today's score. A kept set tells you what your change did. There is a recorded three-run walkthrough of exactly that, runnable with no API key, at https://github.com/7alexhale5-rgb/alexhale-skills/tree/main/example

Prerequisite: npm install -g promptfoo (check with which promptfoo)


Commands

/regression-test init

Scaffold regression testing infrastructure in the current project.

  1. Create directory structure:

    mkdir -p .promptfoo/golden
    
  2. Generate .promptfoo/promptfooconfig.yaml (the path matters: tests: "golden/*.yaml" below resolves relative to this file, and /regression-test run looks for it here):

    description: "Golden dataset regression tests"
    # Without this, promptfoo has no prompt to send and every case scores an empty string.
    # The vars come from the golden files that `/regression-test add` writes.
    prompts:
      - |
        [{"role":"system","content":"{{system_prompt}}"},{"role":"user","content":"{{user_input}}"}]
    providers:
      - id: anthropic:messages:claude-sonnet-5
        config:
          temperature: 0
    tests: "golden/*.yaml"
    defaultTest:
      assert:
        - type: llm-rubric
          value: "Output should match the expected behavior described in the test case"
    
  3. Add .promptfoo/results/ to .gitignore if not already present. Ignore the results, commit the goldens: a golden dataset nobody else can see is not a shared baseline, and the whole point is that your teammates and your CI run the same cases you do. If a prompt in a golden case is genuinely sensitive, keep that one case out of the repo rather than hiding the dataset.

  4. Report: "Initialized .promptfoo/golden/ — run /regression-test add <name> to capture test cases."


/regression-test add <name>

Capture a prompt+response pair as a golden test case.

  1. Parse <name> from user input (slug format, e.g., auth-flow, error-handling)

  2. Prompt user for:

    • System prompt (or detect from current project's CLAUDE.md/skill)
    • User input (the test prompt)
    • Expected behavior (natural language description of good output)
    • Vars (optional template variables)
  3. Write golden test case:

    # .promptfoo/golden/{name}.yaml
    - description: "{name}"
      vars:
        system_prompt: |
          {system prompt content}
        user_input: |
          {user input}
      assert:
        - type: llm-rubric
          value: |
            {expected behavior description}
        - type: not-contains
          value: "error"
    
  4. Report: "Added golden test {name} — {N} total test cases."


/regression-test run

Execute Promptfoo evaluation against all golden datasets.

  1. Check prerequisites:

    which promptfoo 2>&1  # must return path
    ls .promptfoo/golden/*.yaml 2>/dev/null | wc -l  # must be > 0
    
  2. Run evaluation:

    cd {project_root}
    promptfoo eval --config .promptfoo/promptfooconfig.yaml \
      --output .promptfoo/results/eval-$(date +%Y%m%d-%H%M%S).json \
      --no-cache 2>&1
    
  3. Parse results JSON:

    • Count PASS/FAIL per test case
    • Extract failure details (expected vs actual)
  4. Report:

    Regression Test Results
    ├─ Total: {N} test cases
    ├─ Pass: {N} ({%})
    ├─ Fail: {N} ({%})
    └─ Duration: {N}s
    
    Failures:
    - {test_name}: {assertion_type} — expected "{expected}", got "{actual}"
    
  5. If all pass: "All golden tests pass — safe to ship." If failures: "Regressions detected — review before shipping."


/regression-test report

Show regression trends over the last 5 evaluations.

  1. Read result files:

    ls -t .promptfoo/results/eval-*.json | head -5
    
  2. For each result file, extract:

    • Date (from filename)
    • Pass/fail counts
    • Any new failures vs previous run
  3. Report:

    Regression Trends (last 5 evals)
    ├─ 2026-03-31 14:30: 12/12 PASS ✓
    ├─ 2026-03-30 09:15: 11/12 PASS (1 fail: auth-flow)
    ├─ 2026-03-29 16:45: 12/12 PASS ✓
    ├─ 2026-03-28 11:00: 10/12 PASS (2 fail)
    └─ 2026-03-27 08:30: 12/12 PASS ✓
    
    Trend: STABLE (4/5 clean)
    

Where to wire it in

The value shows up when the run is automatic, not when you remember to type it.

MomentWhat to do
After changing any prompt/regression-test run before you commit. This is the one that catches most regressions.
Before a releaseRun it in CI and fail the build on a new failure. promptfoo eval returns non-zero when an assertion fails.
After a model or version changeRe-run the whole set. A provider upgrade is a silent prompt change.
Weekly, on a schedule/regression-test report to see whether quality is drifting rather than breaking.

If your agent harness supports hooks or a pre-commit step, put the run there. A check that depends on discipline is a check you will lose.


Graceful Degradation

ComponentIf MissingFallback
promptfoo CLINot installedPrompt user: npm install -g promptfoo
.promptfoo/ directoryNot initializedPrompt: /regression-test init
Golden datasetsNo .yaml filesSkip with note: "No golden tests — add with /regression-test add"
Results directoryMissingCreate on first run

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Trigger for: explaining current FAR, DFARS, or agency-supplement text; determining documented acquisition-policy status for an agency and FAR part; comparing codified text, RFO model text, and agency deviations; tracing acquisition rulemaking; finding procurement comment periods; analyzing public comments; refreshing prior policy analysis; or producing an Acquisition Policy Impact Brief. Use published federal sources and supplied policy documents without making legal or procurement-specific applicability determinations. Do not use for clause selection, market research execution, opportunities, pricing, grants, or cooperative agreements.

日本語の概要は準備中です。原文の説明を表示しています。

aiskillstore/marketplace4322026年10月12日 更新

Systematic framework for extracting actionable items from documents and organizing them into prioritized, trackable checklists. Use when converting reports, meeting notes, audits, or any document with embedded action items into structured TODO lists.

日本語の概要は準備中です。原文の説明を表示しています。

aiskillstore/marketplace4322026年10月12日 更新

Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, agent browsing, research. Triggers: browser, web automation, scrape, navigate, click, fill form, screenshot, browse web, playwright, headless browser, web agent, surf internet, record video

日本語の概要は準備中です。原文の説明を表示しています。

aiskillstore/marketplace4322026年10月12日 更新

This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.

日本語の概要は準備中です。原文の説明を表示しています。

aiskillstore/marketplace4322026年10月12日 更新

Run AI apps via inference.sh CLI - image generation, video creation, LLMs, search, 3D, Twitter automation. Models: FLUX, Veo, Gemini, Grok, Claude, Seedance, OmniHuman, Tavily, Exa, OpenRouter, and many more. Use when running AI apps, generating images/videos, calling LLMs, web search, or automating Twitter. Triggers: inference.sh, infsh, ai model, run ai, serverless ai, ai api, flux, veo, claude api, image generation, video generation, openrouter, tavily, exa search, twitter api, grok

日本語の概要は準備中です。原文の説明を表示しています。

aiskillstore/marketplace4322026年10月12日 更新

Agile ceremonies and sprint management specialist. Use when running standups, retrospectives, sprint planning, or tracking sprint progress.

日本語の概要は準備中です。原文の説明を表示しています。

aiskillstore/marketplace4322026年10月12日 更新

aiskillstore のスキルをすべて見る

このスキルの問題を報告する