Requirements Analyzer
SkillDocs & knowledgeActs as a requirements analyzer that reviews requirement docs and reports gaps, conflicts, and priority checks.
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Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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About this skill
Full quality-and-risk analysis of requirement documents (single- or multi-source), producing one gap/conflict register with executable P0/P1 verification. Triggers: requirements analyzer, requirement quality, gap register, 需求分析, 需求分析器, 需求评审, 需求缺口. Not for writing a case library (that is codexqa-test
What this skill tells your AI
The instructions your AI receives, as published by openqa-cn/codexqa in skills/codexqa-requirement-analyzer/SKILL.md and read by Ahel’s review.
Human-facing install and method docs: README.md, HOW_IT_WORKS.md, KNOWN_LIMITATIONS.md (and .zh-CN.md). Do not load those at runtime.
When to Use
- Need a complete quality and risk analysis of requirement documents, not just a scope scan or a gap list.
- Materials may be a single PRD or a multi-source pack (stories, APIs, plans, role reports).
- Need one reviewable, assignable, verifiable gap register that can hand off to strategy or case writing.
Workflow
- Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar).
- Run the fixed pipeline: intake → short-input gate → structure inventory → input audit → quality/smell/NFR → success metrics → behavior plus UX states → trace and goal-feature alignment → dependency grid → single register → risk algorithm → testability and oracles → blockers and next actions. Do not write risks or a “TOP 3” list first.
- Direct requirement materials remain sufficient for standalone use. If the user supplies role reports with a declared
source_role, treat them as optional composition inputs and never require installing a role Skill. - Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable.
- If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps.
- Default to Markdown; switch formats only when the user asks.
Core Constraints
- Produce exactly one gap and conflict register; do not also write “Gaps and Ambiguities”, “Cross-Source Conflicts”, or “Core Analysis Items”.
- Audit credibility and comparability first; never mark stale or incomparable sources as
aligned. - Prioritize by risk / business impact — do not treat everything equally.
- Separate confirmed facts, role-report content, working assumptions, and hypotheses to verify.
- Do not invent endpoints, fields, SLAs, environments, or root causes the user did not provide; unknowns become questions.
- Do not fill business decisions for stakeholders, and do not judge whether the ask is worth building; keep both views and a suggested decider on conflicts. If an omission looks intentional, ask first.
- When using a role report, preserve
source_roleitem by item; never flatten into anonymous consensus or present a role view as a PRD fact. - Every P0/P1 item needs executable verification: preconditions, stimulus, expected result, required evidence,
VerifyMethod, andRiskClass. Every P0 needsFailureMode. - Items with no observable failure condition are
untestableand must not be executable P0. Do not emit a fake RPN, a 0–100 score, or a six-dimension traffic-light scorecard.
Progressive Disclosure
- Before producing output, read and follow
prompts/codexqa-requirement-analyzer.md(minimum coverage, output structure, quality bar). - When Excel/CSV/JSON/Word is requested: read
output-formats.mdand honor the format. - When a ready-made template fits: use matching files under
output-templates/. - When the user wants examples or alignment with existing assets: read relevant
examples/. - For register fields, troubleshooting, or FAQ: read
references/register-fields.md,references/troubleshooting.md, orreferences/FAQ.md. - For quality-characteristic judgments: read
references/quality-attributes.md. - For vague/optional/loophole wording: read
references/requirement-smells.md. - For NFR completeness: read
references/nfr-quality-grid.md. - For the structure inventory (present/thin/absent): read
references/structure-inventory.md. - For success metrics / KPI: read
references/success-metrics.md. - For upstream/downstream owner and ready dates: read
references/dependency-grid.md. - For missing states/decisions/time/failure paths/UX states: read
references/behavioral-completeness.md. - For P0–P3 ranking: read
references/risk-scoring.md. - Do not load the whole
references/directory. - For format conversion or helper pre-parse: prefer existing TypeScript scripts in
scripts/(npx --yes tsx scripts/run_analysis.ts --input <file>); do not rewrite them in Python. - For evaluating/regressing this skill: use
evals/with skill-up. - For the shortest path: read
quick-start.md.
Pre-delivery Checklist
- Followed the main prompt's 7-section output structure; section 6 was not omitted
- Produced one register only; questions were not restated in section 7
- Covered the minimum checklist, or explained omissions
- High-risk items have P0–P3 with impact / likelihood / detectability
- Section 2 includes quality-characteristic hits, smell hits, the eight-cell NFR grid, structure inventory, success-metrics grid, and dependency grid
- P0/P1 items include preconditions, stimulus, expected, evidence, VerifyMethod, and RiskClass; P0 includes FailureMode
- Items without an oracle were not written as executable P0
- Did not invent details the user did not provide
- Assumptions and gaps are marked
- Role-report findings retain source roles; no role Skill internal file was linked
Common Pitfalls
- Concatenating three old analysis outlines and repeating gaps, risks, questions, and next steps.
- Marking
alignedwhen sources are stale or not comparable. - Pretending completeness when scope or context is missing.
- Treating every item as equally important, or using filler (“improve communication”) instead of closable questions.
- Listing checkpoints for P0 items without verification steps.
- Writing a weak AC with no oracle as an executable P0, or using a score or traffic-light scorecard for false precision.
- Labeling a missing KPI as NFR, or treating “out of this iteration” as
missing.
Signals
- GitHub stars
- 101
- Forks
- 2
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
codexqa-requirement-analyzer- Source
- github.com/openqa-cn/codexqa
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