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Get Started Free →Workflow for authoring requirements and specifications: drafting, review, validation, etc.
.claude/skills/griddynamics-requirements-authoring-flow/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-15 | ✗→✓ | ▲ Improved | 49% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 79% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 116% | 0% |
<requirements-flow>
<description_and_purpose> Prevents premature drafting by enforcing HITL gates where every <req> unit receives explicit user approval before proceeding. Each phase produces traceable artifacts (Final Requirements Set, Validation Pack, Traceability Matrix). Input: user request for new requirements, edits, review, refactor, or validation; USE SKILL requirements-authoring and PROACTIVELY REQUIRE its use in all phases and subagents. </description_and_purpose>
<workflow_phases>
<prerequisites phase="0", applies="ALL">
load-project-context, hitlrequirements-authoring-flow-state.md in FEATURE TEMP with: phase name, status, artifact produced, and open questions.requirements-authoring.reverse-engineering./goal is set repeat phases 5-6 until goal is met, then continue with the rest of phases.IMPORTANT! If the task is to reverse engineer requirements, spawn MULTIPLE subagents with each handling one unit of analysis (one screen, one page, one controller, one endpoint, etc) to effectively prevent hallucinations by narrow scoping for phases intent_capture, outline, draft, validate.
</prerequisites>
<discovery phase="1" priority="must" subagent="discoverer" role="Context analyst collecting project and scope signals" subagent_required_model="claude-sonnet-5, gpt-5.4-medium, gemini-3.1-pro, grok-4.5, gpt-5.6-terra">
Artifact: Discovery Summary (context, existing requirements, constraints, affected files). Done when: scope boundaries and relevant requirement files are identified.
requirements-authoringreverse-engineering</discovery>
<research phase="2" priority="should" subagent="requirements-engineer" role="Researcher collecting standards and prior decisions" subagent_required_model="claude-opus-4-8, gpt-5.5-high, gemini-3.1-pro-high, gpt-5.6-sol">
Artifact: Research Notes (sources, constraints, prior art, reusable requirement patterns). Done when: relevant references are gathered OR no additional sources are needed. Skip when: local context is complete and no external standards are needed.
requirements-authoring</research>
<intent_capture phase="3" priority="must" subagent="requirements-engineer" role="Requirements analyst capturing intent and assumptions" subagent_required_model="claude-opus-4-8, gpt-5.5-high, gemini-3.1-pro-high, gpt-5.6-sol">
Artifact: Intent Capture. Done when: intent is restated, scope and goals confirmed, assumptions listed, and questions resolved.
requirements-authoring</intent_capture>
<outline phase="4" priority="must" subagent="requirements-engineer" role="Information architect proposing MECE requirement layout" subagent_required_model="claude-opus-4-8, gpt-5.5-high, gemini-3.1-pro-high, gpt-5.6-sol">
Artifact: Requirement Outline (areas, file mapping, ID strategy, traceability plan). Done when: user approves structure and requirement batching strategy.
requirements-authoring</outline>
<draft phase="5" priority="must" subagent="requirements-engineer" role="Author drafting atomic requirement units" subagent_required_model="claude-opus-4-8, gpt-5.5-high, gemini-3.1-pro-high, gpt-5.6-sol">
Artifact: Draft Requirement Units (per the requirements-authoring skill's requirement-unit asset). Done when: every in-scope requirement has schema-complete draft and explicit user decision.
<req> schemaDraft<req> schema if older/missing fieldsrequirements-authoring</draft>
<validate phase="6" priority="must" subagent="reviewer" role="Quality reviewer checking correctness, conflicts, and gaps" subagent_required_model="gpt-5.4-medium, gemini-3.1-pro-preview, claude-sonnet-5, grok-4.5, gpt-5.6-terra" must-be-subagent>
Artifact: Validation Report (rubric results, conflict checks, gap checks, risks). Done when: checklist passes and unresolved issues are either fixed or explicitly deferred.
requirements-authoring skill's validation rubricrequirements-authoringreverse-engineering</validate>
<user_review phase="7" priority="must" subagent="HITL" role="HITL">
</user_review>
<finalization phase="8" priority="must" subagent="requirements-engineer" role="Business analyst finalizing requirement artifacts" subagent_required_model="claude-opus-4-8, gpt-5.5-high, gemini-3.1-pro-high, gpt-5.6-sol">
Artifact: Final Requirements Set, Validation Pack, Traceability Matrix, Change Log. Done when: artifacts are stored in target location and state file is complete.
requirements-authoring skill's change-log assetrequirements-authoringcoding-flow (ask, recommend, switch), on switch do not load requirements-use skill, as requirements-authoring is superior already.</finalization>
</workflow_phases>
<validation_checklist>
<req> explicitly user-approved</validation_checklist>
<pitfalls>
</pitfalls>
</requirements-flow>
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 31,998 | 6,796 | -79% | 1 | 1 | 0% | 6,226 | 2,544 | -59% | 0 | 0 | — |
case-02 | fail→fail | 10,363 | 5,785 | -44% | 1 | 1 | 0% | 1,622 | 2,592 | +60% | 0 | 0 | — |
case-03 | fail→fail | 23,135 | 3,192 | -86% | 1 | 1 | 0% | 3,580 | 2,678 | -25% | 0 | 0 | — |
case-04 | pass→fail | 15,771 | 3,778 | -76% | 1 | 1 | 0% | 3,096 | 2,769 | -11% | 0 | 0 | — |
case-15 | fail→pass | 12,942 | 5,805 | -55% | 1 | 1 | 0% | 1,756 | 2,622 | +49% | 0 | 0 | — |
case-05 | pass→fail | 14,432 | 5,361 | -63% | 1 | 1 | 0% | 2,540 | 2,950 | +16% | 0 | 0 | — |
case-06 | pass→fail | 13,168 | 11,578 | -12% | 1 | 1 | 0% | 3,051 | 4,126 | +35% | 0 | 0 | — |
case-07 | pass→pass | 16,763 | 8,657 | -48% | 1 | 1 | 0% | 2,668 | 3,561 | +33% | 0 | 0 | — |
case-08 | fail→pass | 9,360 | 3,260 | -65% | 1 | 1 | 0% | 1,454 | 2,609 | +79% | 0 | 0 | — |
case-09 | pass→pass | 13,732 | 1,855 | -86% | 1 | 1 | 0% | 1,950 | 2,380 | +22% | 0 | 0 | — |
case-10 | pass→pass | 6,024 | 3,912 | -35% | 1 | 1 | 0% | 871 | 2,782 | +219% | 0 | 0 | — |
case-11 | fail→pass | 13,869 | 5,747 | -59% | 1 | 1 | 0% | 2,049 | 2,979 | +45% | 0 | 0 | — |
case-12 | fail→fail | 18,551 | 10,767 | -42% | 1 | 1 | 0% | 3,090 | 3,843 | +24% | 0 | 0 | — |
case-13 | fail→pass | 11,830 | 3,641 | -69% | 1 | 1 | 0% | 1,871 | 2,647 | +41% | 0 | 0 | — |
case-14 | pass→pass | 12,014 | 3,735 | -69% | 1 | 1 | 0% | 1,738 | 2,622 | +51% | 0 | 0 | — |
case-16 | fail→fail | 16,267 | 9,572 | -41% | 1 | 1 | 0% | 2,378 | 3,652 | +54% | 0 | 0 | — |
case-17 | pass→pass | 13,086 | 4,585 | -65% | 1 | 1 | 0% | 1,931 | 2,742 | +42% | 0 | 0 | — |
case-18 | fail→pass | 9,032 | 4,536 | -50% | 1 | 1 | 0% | 1,372 | 2,962 | +116% | 0 | 0 | — |
case-19 | fail→pass | 16,930 | 4,220 | -75% | 1 | 1 | 0% | 2,612 | 2,825 | +8% | 0 | 0 | — |
case-20 | fail→fail | 17,714 | 9,881 | -44% | 1 | 1 | 0% | 2,689 | 3,785 | +41% | 0 | 0 | — |
case-21 | fail→pass | 8,085 | 2,603 | -68% | 1 | 1 | 0% | 1,121 | 2,546 | +127% | 0 | 0 | — |
case-22 | pass→pass | 7,758 | 3,418 | -56% | 1 | 1 | 0% | 1,167 | 2,692 | +131% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 20 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +18 percentage points is the difference between those two pass rates over the 20 comparable cases. 4 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.