Claude
Skill
ci-test-optimization
Use this skill when you need evidence-bounded pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries; triggers include CI 测试流水线 and CI test pipeline.
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Download
naodeng-awesome-qa-skills-skills_en_testing-types_ci-test-optimization-c44b892.zip · 7 KB
Install
skills CLI
npx skills add https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ci-test-optimization
Claude Code
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install naodeng-awesome-qa-skills@llmmart
Git
git clone https://github.com/naodeng/awesome-qa-skills.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole naodeng/awesome-qa-skills collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
CI Test Pipeline Optimization
When to Use
- Use this skill when you need evidence-bounded analysis, design, or validation preparation for pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries.
- Use it to review a plan, result, or metric and turn the review into executable improvements.
- Use it when input is incomplete but a bounded first pass with assumptions, gaps, and human-decision boundaries is still useful.
Output Format Options
- Default to Markdown organized by domain risk, evidence state, priority, and boundary.
- When the user requests tables, CSV, JSON, or ticket fields, preserve the same finding fields, evidence, and decision boundaries.
- Before machine consumption, confirm the schema, enums, required fields, and evidence sources.
How to Use
- Read and follow
prompts/ci-test-optimization.md, including its input audit, domain coverage, and output order. - Extract scope, environment, version, time window, constraints, success criteria, and available evidence, with attention to stage dependency, feedback latency, resource use, flakiness, caching and sharding.
- Separate confirmed facts, evidence-backed inferences, candidate recommendations, and Human decisions before ranking by risk and evidence strength.
- Turn high-risk items into preconditions, steps, expected behavior or decision criteria, required evidence, and a validation method.
- When input is incomplete, deliver a bounded first pass, state unsupported conclusions, and never present static material as execution evidence.
Reference Files
- Always read
prompts/ci-test-optimization.md; it is the complete execution specification for this skill. - For evaluation, read
evals/eval.yamland the matching cases underevals/cases/. - Load
references/,examples/,scripts/, oroutput-formats.mdonly when those directories exist and the task needs them.
Core Constraints
- Keep the analysis focused on pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries; do not replace business owners or Human risk acceptance, exception approval, or release decisions.
- Never invent system behavior, fields, metrics, thresholds, data, root causes, execution records, or pass claims.
- Static design, plans, file presence, or a dry run retain their evidence state and cannot become proof of real execution.
- When evidence is insufficient, use pending confirmation, blocked, unassessed, or NOT_SCORED and give the smallest validation method.
- For production, privacy, or security work, use least privilege, masked data, mocks, dry runs, or isolation.
Delivery Checklist
- Covered stage dependency, feedback latency, resource use, flakiness, caching and sharding, with source, evidence state, and validation method for each.
- Separated facts, inferences, candidate recommendations, gaps, and Human decisions.
- Gave high-risk items P0/P1/P2/P3 or an equivalent priority, owner role, and close condition.
- Did not turn plans, static checks, or dry runs into test execution, all-passed, or release-approved claims.
- Stated residual risk, stop/escalation conditions, and next actions.
Common Pitfalls
- Listing checks without triggers, expected concerns, owner roles, close conditions, and evidence.
- Treating adjacent metrics or tool names as a complete CI test pipeline judgment.
- Using unexplained numbers for false precision or writing correlation as causation.
- Refusing incomplete input, or pretending that incomplete evidence is conclusive.
Best Practices
- Start with paths most likely to cause business loss, quality regression, or decision blockage.
- Use the smallest verifiable experiment to reduce uncertainty and record conditions, versions, sources, and evidence.
- Make the Skill independently installable, executable, and reviewable by another engineer.
Files (awesome-qa-skills)
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agents
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openai.yaml 317 B
version: 1 metadata: key: ci-test-optimization interface: display_name: "CI Test Pipeline Optimization" short_description: "Produce evidence-bounded CI test pipeline analysis" default_prompt: "Use the ci-test-optimization skill to perform CI test pipeline analysis." policy: allow_implicit_invocation: true
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evals
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cases
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basic-success.yaml 1.4 KB
id: basic-success title: "CI Test Pipeline Optimization: domain-complete scenario" description: | Checks that the skill produces domain-specific CI test pipeline analysis that is evidence-based, prioritized, and executable. input: prompt: | Use the ci-test-optimization skill for this scenario: a CI test pipeline queues during peak hours and retries increase cost, so the team needs evidence-based tradeoffs among feedback speed, stability, and resources. Provide CI test pipeline priorities, core execution items, expected behavior or decision criteria, evidence, and open questions. expect: must_contain: - "object/rule" - "source" - "trigger or applicability" - "expected concern/rationale" - "evidence state" - "impact/priority" - "owner role" - "close condition" - "validation method" - "CTO-" - "Task Understanding" - "Input Audit" - "CI test pipeline" - "expected" must_not_contain: - "I cannot" - "unable to help" judge: type: rule_based success: - output_contains: all: - "object/rule" - "source" - "trigger or applicability" - "expected concern/rationale" - "evidence state" - "impact/priority" - "owner role" - "close condition" - "validation method" - "CTO-" - "Task Understanding" - "Input Audit" - "CI test pipeline" -
edge-incomplete-input.yaml 946 B
id: edge-incomplete-input title: "CI Test Pipeline Optimization: bounded first pass with incomplete input" description: | Checks that missing context leads to a bounded first pass with assumptions and gaps, not refusal or invention. input: prompt: | Use the ci-test-optimization skill. The only known fact is: a CI test pipeline queues during peak hours and retries increase cost, so the team needs evidence-based tradeoffs among feedback speed, stability, and resources. No more environment, version, definitions, or supporting evidence are available. Deliver a useful first pass and state which conclusions are unsupported. expect: must_contain: - "Working Assumptions" - "Open Questions" - "evidence" - "Next Actions" must_not_contain: - "I cannot" - "unable to help" judge: type: rule_based success: - output_contains: all: - "Working Assumptions" - "Open Questions" -
edge-scope-boundary.yaml 989 B
id: edge-scope-boundary title: "CI Test Pipeline Optimization: execution and release boundary" description: | Checks that missing runtime records do not become execution, pass, or release claims and that the domain validation boundary remains explicit. input: prompt: | Use the ci-test-optimization skill to review this scenario: a CI test pipeline queues during peak hours and retries increase cost, so the team needs evidence-based tradeoffs among feedback speed, stability, and resources. Only static material is available and no runtime record is supplied. State what cannot be written as "tests were executed", "all tests passed", or "release approved", then give stop conditions and next validation. expect: must_contain: - "CI test pipeline" - "cannot" - "Open Questions" must_not_contain: - "I cannot" - "unable to help" judge: type: rule_based success: - output_contains: all: - "CI test pipeline" - "cannot"
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eval.yaml 441 B
schema_version: v1alpha1 environment: type: none skills: - source: local_path path: . engine: name: claude_code cases: files: - evals/cases/basic-success.yaml - evals/cases/edge-incomplete-input.yaml - evals/cases/edge-scope-boundary.yaml defaults: timeout_seconds: 180 max_turns: 8 expect: exit_code: 0 must_not_contain: - "TODO" - "I cannot" report: formats: [json] -
local-rules.json 139 B
{ "skill": "ci-test-optimization", "max_commands": 20, "max_total_tokens": 100000, "permissions": { "max_escalations": 0 } } -
trigger-prompts.csv 597 B · in bundle
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prompts
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ci-test-optimization.md 4.6 KB
# CI Test Pipeline Optimization Prompt Build an executable, reviewable, and traceable evidence boundary for pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries. ## Input Start with an input audit and record known, missing, conflicting, stale, out_of_scope, and assumptions: - known: scope, CI test pipeline material, constraints, or results directly supported by a source. - missing: material needed to assess pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries that has not been supplied. - conflicting: inconsistent goals, definitions, conditions, or behaviors across sources. - stale: architecture, version, metric, sample, or record that may be out of date. - out_of_scope: actions outside this Skill, unauthorized actions, or actions requiring a real environment. - assumptions: temporary assumptions for a bounded first pass, each with a validation method. ## What to do 1. Restate the objective, subject, scope, and success criteria in one sentence. 2. Audit completeness, credibility, recency, and comparability, focusing on stage dependency, feedback latency, resource use, flakiness, caching and sharding. 3. Build a risk or failure model for CI test pipeline, including triggers, expected concerns, impact, and evidence needs. 4. Convert the analysis into concrete scenarios, assertions, validation steps, decision gates, or improvement experiments. 5. Report residual risk, evidence gaps, and next actions without presenting assumptions as facts. ## Execution Rules - Give a source, evidence state, and validation method for every important conclusion. - Each scenario must include preconditions, action or stimulus, expected behavior or decision criterion, required evidence, and a stop condition. - Use P0/P1/P2/P3 or an equivalent scale and explain business impact, likelihood, detectability, or decision cost. - Make pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries concrete; do not substitute adjacent test types, tool names, or one example for domain reasoning. - Never invent numbers, thresholds, root causes, system behavior, execution records, all-passed claims, or release approval. - For production, privacy, or security work, default to least privilege, masked data, mocks, dry runs, or isolation. - Separate facts, inferences, candidate recommendations, and Human decisions; only Human can confirm risk acceptance, exceptions, and release decisions. ## Minimum Coverage Checklist - stage dependency, feedback latency, resource use, flakiness, caching and sharding - confirmed facts, working assumptions, open questions, evidence quality, and recency - high-risk paths, blockers, stop/escalation/rollback or human-handoff conditions - smallest validation action, owner role, close condition, residual risk, and Human decision - facts, evidence-backed inferences, candidate recommendations, Human decisions ## Output ### 1. Task Understanding and Scope State the objective, subject, inclusions, exclusions, success criteria, and unauthorized actions. ### 2. Input Audit List known, missing, conflicting, stale, out_of_scope, and assumptions with source, recency, and evidence quality. ### 3. CI test pipeline Analysis and Priorities Describe scenarios, triggers, expected behavior, impact, priority, and evidence gaps for pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries. ### CTO-## Finding Contract Each finding must include: - object/rule - source - trigger or applicability - expected concern/rationale - evidence state - impact/priority - owner role - close condition - validation method ### 4. Candidate Validation and Residual Risk Separate candidate recommendations from actual execution and state the smallest validation action, stop/escalation conditions, residual risk, and open questions. ### 5. Human Decisions List only items requiring Human confirmation, risk acceptance, exception authorization, rollback, or release judgment. The output must keep these sections separate: facts, evidence-backed inferences, candidate recommendations, Human decisions. ## Quality Bar - Tailor the content to CI test pipeline; do not merely rename a generic template. - Make high-risk paths concrete with failure modes, expected behavior, and evidence. - Never infer numbers, root causes, or system behavior without support. - Static analysis, plans, or dry runs are not proof of real execution, all tests passing, or release approval. - Let an executor act without guessing and a reviewer trace the boundary between facts, inferences, recommendations, and Human decisions.
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SKILL.md 4.1 KB
--- name: ci-test-optimization description: Use this skill when you need evidence-bounded pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries; triggers include CI 测试流水线 and CI test pipeline. --- # CI Test Pipeline Optimization ## When to Use - Use this skill when you need evidence-bounded analysis, design, or validation preparation for pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries. - Use it to review a plan, result, or metric and turn the review into executable improvements. - Use it when input is incomplete but a bounded first pass with assumptions, gaps, and human-decision boundaries is still useful. ## Output Format Options - Default to Markdown organized by domain risk, evidence state, priority, and boundary. - When the user requests tables, CSV, JSON, or ticket fields, preserve the same finding fields, evidence, and decision boundaries. - Before machine consumption, confirm the schema, enums, required fields, and evidence sources. ## How to Use 1. Read and follow `prompts/ci-test-optimization.md`, including its input audit, domain coverage, and output order. 2. Extract scope, environment, version, time window, constraints, success criteria, and available evidence, with attention to stage dependency, feedback latency, resource use, flakiness, caching and sharding. 3. Separate confirmed facts, evidence-backed inferences, candidate recommendations, and Human decisions before ranking by risk and evidence strength. 4. Turn high-risk items into preconditions, steps, expected behavior or decision criteria, required evidence, and a validation method. 5. When input is incomplete, deliver a bounded first pass, state unsupported conclusions, and never present static material as execution evidence. ## Reference Files - Always read `prompts/ci-test-optimization.md`; it is the complete execution specification for this skill. - For evaluation, read `evals/eval.yaml` and the matching cases under `evals/cases/`. - Load `references/`, `examples/`, `scripts/`, or `output-formats.md` only when those directories exist and the task needs them. ## Core Constraints - Keep the analysis focused on pipeline stages, feedback latency, resources, flakiness, caching/sharding, and rollback boundaries; do not replace business owners or Human risk acceptance, exception approval, or release decisions. - Never invent system behavior, fields, metrics, thresholds, data, root causes, execution records, or pass claims. - Static design, plans, file presence, or a dry run retain their evidence state and cannot become proof of real execution. - When evidence is insufficient, use pending confirmation, blocked, unassessed, or NOT_SCORED and give the smallest validation method. - For production, privacy, or security work, use least privilege, masked data, mocks, dry runs, or isolation. ## Delivery Checklist - [ ] Covered stage dependency, feedback latency, resource use, flakiness, caching and sharding, with source, evidence state, and validation method for each. - [ ] Separated facts, inferences, candidate recommendations, gaps, and Human decisions. - [ ] Gave high-risk items P0/P1/P2/P3 or an equivalent priority, owner role, and close condition. - [ ] Did not turn plans, static checks, or dry runs into test execution, all-passed, or release-approved claims. - [ ] Stated residual risk, stop/escalation conditions, and next actions. ## Common Pitfalls - Listing checks without triggers, expected concerns, owner roles, close conditions, and evidence. - Treating adjacent metrics or tool names as a complete CI test pipeline judgment. - Using unexplained numbers for false precision or writing correlation as causation. - Refusing incomplete input, or pretending that incomplete evidence is conclusive. ## Best Practices - Start with paths most likely to cause business loss, quality regression, or decision blockage. - Use the smallest verifiable experiment to reduce uncertainty and record conditions, versions, sources, and evidence. - Make the Skill independently installable, executable, and reviewable by another engineer.
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