How do I install the Ce Test Pruning Expert skill?

Run npx skillmds add majiayu000/ce-test-pruning-expert in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ce Test Pruning Expert safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ce Test Pruning Expert?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ce Test Pruning Expert free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ce Test Pruning Expert?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ce Test Pruning Expert by majiayu000

Name: Ce Test Pruning Expert
Rating: 4.2 (5 reviews)
Author: majiayu000

File contents

CE Test Pruning Expert

This skill implements the Pruner role from ADR-030 and mirrors the prompt in docs/improvement/test-quality-method/pruner.md.

Required references

docs/improvement/test-quality-method/README.md (canonical method + options)
docs/improvement/test-quality-method/pruner.md (full role prompt)

Use this skill when

Triaging over-testing reports.
Reducing redundant generated tests.
Consolidating near-duplicate tests into parameterized suites.

Focus option handling

Option A (Test-Focused): run the full pruning workflow.
Option B (Code-Focused): skip pruning by default; only process explicit test-removal requests.
Option C (Full Cycle): run Option A and pass risk-marked removals to code-quality and dead-code reviewers before implementation.

Core principles

Zero-unique-line tests are candidates, not automatic removals.
Identical coverage fingerprints indicate redundancy risk.
Behavioral uniqueness can justify retention even with low line uniqueness.

Workflow

Confirm the selected focus option (A, B, or C).
Gather evidence:

python scripts/over_testing/detect_redundant_tests.py
python scripts/over_testing/estimator.py --recommend

Review primary artifacts:

reports/over_testing/per_test_summary.csv
reports/over_testing/redundant_tests.csv

Classify each test:

Remove: redundant and behaviorally duplicative.
Refactor: preserve behavior but merge/parameterize.
Keep: unique behavioral guardrail.

Validate removal safety:

no sole-provider line coverage loss
no regression-specific or issue-tagged protection removed

Output contract

For each candidate include:

test id/path
evidence (unique lines, overlap fingerprint, assertion profile)
recommendation (Remove, Refactor, Keep)
risk note and required follow-up
selected focus option (A, B, or C) and why pruning is in-scope

Constraints

Never remove without evidence-backed safety analysis.
Coordinate with ce-deadcode-hunter if only tests touch a code path.
Submit final pruning batches to ce-devils-advocate.

---name: ce-test-pruning-expertdescription: Identify redundant or low-value tests using unique-lines and overlap evidence while preserving behavior coverage, with Option A/B/C routing from the Test Quality Method.--- # CE Test Pruning Expert This skill implements the **Pruner** role from ADR-030 and mirrors the prompt in `docs/improvement/test-quality-method/pruner.md`. ## Required references - `docs/improvement/test-quality-method/README.md` (canonical method + options) - `docs/improvement/test-quality-method/pruner.md` (full role prompt) ## Use this skill when - Triaging over-testing reports. - Reducing redundant generated tests. - Consolidating near-duplicate tests into parameterized suites. ## Focus option handling - **Option A (Test-Focused):** run the full pruning workflow. - **Option B (Code-Focused):** skip pruning by default; only process explicit test-removal requests. - **Option C (Full Cycle):** run Option A and pass risk-marked removals to code-quality and dead-code reviewers before implementation. ## Core principles 1. Zero-unique-line tests are candidates, not automatic removals. 2. Identical coverage fingerprints indicate redundancy risk. 3. Behavioral uniqueness can justify retention even with low line uniqueness. ## Workflow 1. Confirm the selected focus option (`A`, `B`, or `C`). 2. Gather evidence: ```bash python scripts/over_testing/detect_redundant_tests.py python scripts/over_testing/estimator.py --recommend ``` 3. Review primary artifacts: - `reports/over_testing/per_test_summary.csv` - `reports/over_testing/redundant_tests.csv` 4. Classify each test: - `Remove`: redundant and behaviorally duplicative. - `Refactor`: preserve behavior but merge/parameterize. - `Keep`: unique behavioral guardrail. 5. Validate removal safety: - no sole-provider line coverage loss - no regression-specific or issue-tagged protection removed ## Output contract For each candidate include: - test id/path - evidence (unique lines, overlap fingerprint, assertion profile) - recommendation (`Remove`, `Refactor`, `Keep`) - risk note and required follow-up - selected focus option (`A`, `B`, or `C`) and why pruning is in-scope ## Constraints - Never remove without evidence-backed safety analysis. - Coordinate with `ce-deadcode-hunter` if only tests touch a code path. - Submit final pruning batches to `ce-devils-advocate`.

Frequently asked questions