claw/docs/superpowers/specs/2026-04-20-scene-skill-102-natural-language-parameter-readiness-design.md

Scene Skill 102 Natural-Language Parameter Readiness Design

Date: 2026-04-20 Parent: 2026-04-20-scene-skill-102-deterministic-invocation-readiness-design.md

Intent

Clarify whether the final 102 materialized scene skills can be invoked with natural-language query conditions before pseudo-production testing.

This design separates deterministic dispatch readiness from parameter readiness. A skill can be selected by an instruction ending with 。。。 while still not being able to parse query conditions such as organization, period, date, or report mode.

Current Baseline

1. 102 / 102 scene skills are final-materialized.
2. 102 / 102 scene skills are deterministic dispatch ready for 。。。 suffix invocation.
3. 102 / 102 scene skills pass full direct mock execution.
4. Only a subset currently declares explicit [[params]] in scene.toml.

Problem Statement

Internal-network validation should not use only 场景名。。。 as the invocation pattern for every skill.

Parameterized skills must be validated with representative natural-language query conditions. For example:

兰州公司 台区线损大数据 月累计线损率统计分析。。。

This should resolve:

1. 兰州公司 as organization;
2. 月累计 as period mode;
3. the scene keywords as deterministic skill selection evidence.

If a skill has required params but lacks usable resolver resources, it must be flagged before pseudo-production execution.

Scope

Allowed:

1. Analyze all final 102 skill manifests.
2. Classify parameter readiness for each scene.
3. Generate recommended natural-language invocation samples.
4. Identify resolver-resource gaps.
5. Publish readiness JSON and report.

Forbidden:

1. Do not modify src/compat/scene_platform/dispatch.rs.
2. Do not modify src/compat/scene_platform/resolvers.rs.
3. Do not modify src/generated_scene/analyzer.rs.
4. Do not modify src/generated_scene/generator.rs.
5. Do not edit final generated skill packages.
6. Do not execute browser, host bridge, localhost services, or production network.
7. Do not update scene_execution_board_2026-04-18.json.

Readiness Classes

parameter-ready

The skill declares required params and all required resolver resources are present and populated enough to support deterministic parsing.

parameter-gap

The skill declares required params, but at least one required resolver cannot currently resolve real user input because of missing, empty, or unsupported resolver configuration.

parameter-not-required

The skill has no declared required params. It may still accept descriptive natural language, but the current runtime will primarily use it for deterministic scene selection rather than structured argument extraction.

parameter-implicit-risk

The skill has no declared required params, but the scene name suggests likely query conditions such as month, week, day, company, county, report period, or business object. These scenes should be tested carefully because user wording may imply filters that current manifests do not parse.

Output Model

Each scene record should include:

1. sceneId
2. sceneName
3. archetype
4. skillDir
5. hasParams
6. requiredParams
7. resolverStatus
8. parameterReadiness
9. recommendedInvocation
10. minimalInvocation
11. parameterizedInvocation
12. gaps
13. notes

Completion Criteria

1. All 102 scenes are classified.
2. The report states how many scenes require explicit query conditions.
3. The report states how many required-param scenes are actually resolver-ready.
4. The report states which scenes should not be validated with only 场景名。。。.
5. The report does not claim production readiness.