166 lines
4.0 KiB
Markdown
166 lines
4.0 KiB
Markdown
# Generated Scene Source Evidence Cross-Scan Design
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> Date: 2026-04-20
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> Status: Draft
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> Parent roadmap:
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> - `docs/superpowers/plans/2026-04-20-generated-scene-source-first-runtime-semantics-hardening-plan.md`
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## Intent
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Execute the first bounded child step of the source-first runtime semantics hardening roadmap:
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`scan the original 102 source scenes for high-signal runtime-semantics evidence`
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This design does not change analyzer, generator, manifests, or runtime behavior. It only defines how to scan original source-scene evidence so later rule-hardening routes can be derived from source truth rather than from already-generated skills alone.
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## Objective
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For every scene in the current 102-scene set:
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1. locate the original source directory
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2. perform a bounded source evidence scan
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3. record whether source-side evidence exists for the five anchor gap classes:
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- `invocation_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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## Scope
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In scope:
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1. source directories under:
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- `D:/desk/智能体资料/全量业务场景/一平台场景`
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2. current 102-scene mapping from existing materialization / board assets
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3. bounded file-content scanning over high-signal files
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4. JSON ledger + human-readable report
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Out of scope:
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1. any code change in `src/`
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2. any generated skill change
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3. any rematerialization
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4. any execution board update
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5. any pseudo-production execution
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## Required Scan Targets
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The scan should prioritize only high-signal evidence sources.
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### 1. Invocation alias evidence
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Signals:
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1. scene name variants
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2. menu labels
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3. button labels
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4. route names
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5. report titles
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6. user-facing Chinese phrases in HTML / JS
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### 2. Dictionary recovery evidence
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Signals:
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1. `city.js`
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2. `dict.js`
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3. `enum.js`
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4. `options*.js`
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5. tree / option arrays with `label`, `value`, `code`, `children`
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### 3. Parameter default semantics evidence
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Signals:
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1. `moment(`
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2. `dayjs(`
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3. default query parameter assignment
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4. implicit month/week/date initialization
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### 4. Resolver-to-request mapping evidence
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Signals:
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1. `$.ajax`
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2. `fetch`
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3. `contentType`
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4. request `data`
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5. request body field names
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6. mode-specific request payloads
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### 5. Runtime URL semantics evidence
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Signals:
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1. app entry URLs
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2. module route URLs
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3. API endpoint URLs
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4. host runtime / bootstrap page hints
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## Scan Strategy
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This is not a full source index.
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The scan should:
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1. use bounded heuristics and targeted filename/content patterns
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2. avoid exhaustive deep parsing of every file
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3. record evidence flags and representative evidence paths
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4. be sufficient to classify scenes for later hardening routes
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## Inputs
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Primary inputs:
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1. `tests/fixtures/generated_scene/scene_skill_102_final_materialization_manifest_2026-04-19.json`
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2. `tests/fixtures/generated_scene/scene_execution_board_2026-04-18.json`
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3. source root:
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- `D:/desk/智能体资料/全量业务场景/一平台场景`
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Anchor validation source:
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1. `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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## Output Artifacts
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### JSON
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- `tests/fixtures/generated_scene/generated_scene_source_evidence_cross_scan_2026-04-20.json`
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Each scene record should include:
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1. `sceneId`
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2. `sceneName`
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3. `sourceDir`
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4. `evidenceFlags`
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5. `evidenceFiles`
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6. `riskHints`
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### Report
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- `docs/superpowers/reports/2026-04-20-generated-scene-source-evidence-cross-scan-report.md`
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The report must answer:
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1. how many scenes show dictionary evidence
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2. how many scenes show default parameter semantics
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3. how many scenes show request field aliasing
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4. how many scenes show multi-URL semantics
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5. which scenes look most similar to `sweep-030-scene`
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## Acceptance Criteria
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This design is complete when:
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1. all 102 scenes are included in the cross-scan
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2. the five evidence families are explicit
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3. the output JSON structure is defined
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4. the scan remains analysis-only
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## Stop Statement
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Stop after publishing the child design and child plan.
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Do not execute the scan inside this design.
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