# Generated Scene Runtime Semantics Gap Analysis Plan

> Status: Superseded by `docs/superpowers/plans/2026-04-20-generated-scene-source-first-runtime-semantics-hardening-plan.md`

## Parent

- Parent design: [2026-04-20-generated-scene-runtime-semantics-gap-analysis-design.md](/D:/data/ideaSpace/rust/sgClaw/claw-new/docs/superpowers/specs/2026-04-20-generated-scene-runtime-semantics-gap-analysis-design.md)

## Goal

Analyze the 102 final generated scene skills for runtime-semantics divergence, using `sweep-030-scene` as the anchor case and systematizing the five gap classes exposed during inner-network validation.

This plan is analysis-only.

## Fixed Inputs

- `examples/scene_skill_102_final_materialization_2026-04-19/skills`
- `tests/fixtures/generated_scene/scene_skill_102_deterministic_invocation_readiness_after_keyword_refinement_2026-04-20.json`
- `tests/fixtures/generated_scene/scene_skill_102_natural_language_parameter_readiness_2026-04-20.json`
- `tests/fixtures/generated_scene/scene_skill_102_parameter_dictionary_template_normalization_2026-04-20.json`
- Anchor source:
  - `D:/desk/智能体资料/全量业务场景/一平台场景/台区线损大数据-月_周累计线损率统计分析`

## Boundaries

Allowed:

- Read skill manifests, reports, references, and selected source-scene evidence
- Produce JSON inventory and report

Forbidden:

- No edits in `src/`
- No edits to generated skills
- No rerun materialization
- No execution board updates
- No pseudo-production execution
- No implementation patch for any scene

## Phase 0: Freeze Gap Taxonomy

Tasks:

1. Fix the five runtime-semantics gap classes from the anchor case
2. Define high / medium / low risk buckets
3. Lock analysis outputs and stop rule

Acceptance:

1. The five gap classes are explicit and stable
2. The plan remains analysis-only

## Phase 1: Anchor-Case Evidence Extraction

Tasks:

1. Read `sweep-030-scene` generated assets:
   - `scene.toml`
   - `references/generation-report.json`
   - `references/org-dictionary.json`
   - generated script
2. Read source-scene evidence from the original `台区线损大数据-月_周累计线损率统计分析`
3. Record direct evidence for:
   - alias gap
   - dictionary recovery gap
   - parameter default semantics gap
   - resolver-to-request mapping gap
   - runtime URL semantics gap

Acceptance:

1. `sweep-030-scene` has explicit evidence for each applicable gap class

## Phase 2: 102-Scene Inventory Scan

Tasks:

1. Scan all 102 final skills
2. Extract:
   - deterministic keywords
   - params presence
   - dictionary reference presence
   - bootstrap target presence
   - generation-report URL evidence
3. Tag scenes with likely gap classes using bounded heuristics

Acceptance:

1. Every scene gets a runtime-semantics record
2. Every scene has `riskLevel` and `gaps`

## Phase 3: Family / Archetype Grouping

Tasks:

1. Group findings by archetype / family
2. Count gap incidence by bucket
3. Separate:
   - generator-level fix candidates
   - runtime-only residuals

Acceptance:

1. Summary counts exist per gap type and per archetype
2. Report can distinguish generator vs runtime responsibilities

## Phase 4: Publish Analysis Assets

Deliverables:

1. `tests/fixtures/generated_scene/generated_scene_runtime_semantics_gap_analysis_2026-04-20.json`
2. `docs/superpowers/reports/2026-04-20-generated-scene-runtime-semantics-gap-analysis-report.md`

Acceptance:

1. All 102 scenes are represented
2. `sweep-030-scene` is explicitly called out as anchor evidence
3. The report recommends next implementation routes, but does not execute them

## Stop Statement

Stop after publishing the JSON inventory and report.