123 lines
3.6 KiB
Markdown
123 lines
3.6 KiB
Markdown
# Generated Scene Runtime Semantics Gap Analysis Plan
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> Status: Superseded by `docs/superpowers/plans/2026-04-20-generated-scene-source-first-runtime-semantics-hardening-plan.md`
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## Parent
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- 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)
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## Goal
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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.
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This plan is analysis-only.
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## Fixed Inputs
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- `examples/scene_skill_102_final_materialization_2026-04-19/skills`
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- `tests/fixtures/generated_scene/scene_skill_102_deterministic_invocation_readiness_after_keyword_refinement_2026-04-20.json`
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- `tests/fixtures/generated_scene/scene_skill_102_natural_language_parameter_readiness_2026-04-20.json`
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- `tests/fixtures/generated_scene/scene_skill_102_parameter_dictionary_template_normalization_2026-04-20.json`
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- Anchor source:
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- `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`
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## Boundaries
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Allowed:
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- Read skill manifests, reports, references, and selected source-scene evidence
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- Produce JSON inventory and report
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Forbidden:
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- No edits in `src/`
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- No edits to generated skills
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- No rerun materialization
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- No execution board updates
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- No pseudo-production execution
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- No implementation patch for any scene
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## Phase 0: Freeze Gap Taxonomy
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Tasks:
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1. Fix the five runtime-semantics gap classes from the anchor case
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2. Define high / medium / low risk buckets
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3. Lock analysis outputs and stop rule
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Acceptance:
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1. The five gap classes are explicit and stable
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2. The plan remains analysis-only
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## Phase 1: Anchor-Case Evidence Extraction
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Tasks:
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1. Read `sweep-030-scene` generated assets:
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- `scene.toml`
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- `references/generation-report.json`
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- `references/org-dictionary.json`
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- generated script
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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`
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3. Record direct evidence for:
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- alias gap
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- dictionary recovery gap
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- parameter default semantics gap
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- resolver-to-request mapping gap
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- runtime URL semantics gap
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Acceptance:
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1. `sweep-030-scene` has explicit evidence for each applicable gap class
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## Phase 2: 102-Scene Inventory Scan
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Tasks:
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1. Scan all 102 final skills
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2. Extract:
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- deterministic keywords
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- params presence
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- dictionary reference presence
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- bootstrap target presence
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- generation-report URL evidence
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3. Tag scenes with likely gap classes using bounded heuristics
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Acceptance:
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1. Every scene gets a runtime-semantics record
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2. Every scene has `riskLevel` and `gaps`
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## Phase 3: Family / Archetype Grouping
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Tasks:
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1. Group findings by archetype / family
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2. Count gap incidence by bucket
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3. Separate:
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- generator-level fix candidates
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- runtime-only residuals
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Acceptance:
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1. Summary counts exist per gap type and per archetype
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2. Report can distinguish generator vs runtime responsibilities
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## Phase 4: Publish Analysis Assets
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Deliverables:
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1. `tests/fixtures/generated_scene/generated_scene_runtime_semantics_gap_analysis_2026-04-20.json`
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2. `docs/superpowers/reports/2026-04-20-generated-scene-runtime-semantics-gap-analysis-report.md`
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Acceptance:
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1. All 102 scenes are represented
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2. `sweep-030-scene` is explicitly called out as anchor evidence
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3. The report recommends next implementation routes, but does not execute them
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## Stop Statement
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Stop after publishing the JSON inventory and report.
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