Requirements Analyzer

SkillDocs & knowledge

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-testcase-generator), constructing test data (codexqa-testdata-generator), or reviewing code (codexqa-defect-analyzer / codexqa-code-reviewer). Former skill name: requirements-analyzer.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Requirements Analyzer skill

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

  1. Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar).
  2. 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.
  3. 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.
  4. Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable.
  5. If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps.
  6. 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_role item 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, and RiskClass. Every P0 needs FailureMode.
  • Items with no observable failure condition are untestable and 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.md and 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, or references/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 aligned when 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
60
Forks
2
Last commit
Sep 2026
Advanced
Item type
skill
Key
codexqa-requirement-analyzer
Source
github.com/openqa-cn/codexqa