generative-state-construction
Convert fuzzy keypoint text into concrete short interaction data for executable verification.
Skill by NetX-lab
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About
Use cases: data, productivity
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/generative-state-construction
What's inside
- Skill name
generative-state-construction- Description
- Convert fuzzy keypoint text into concrete short interaction data for executable verification. Use as the normal-mode `keypoint-tester` pre-step, not as a standalone top-level workflow.
- License
- MIT
- Instructions
- 154 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
## Overview
Subagent pre-step for ONE keypoint in normal mode.
This skill converts fuzzy keypoint text (`prestate/instruction/expected`) into concrete, machine-usable test data and writes the canonical artifacts consumed by `short-interaction-verification`.
The goal is not only to produce a target end state, but also to make the verdict defensible against nearby false positives. Build a compact verification contract that states what must be observed and what must not be mistaken for success.
This skill is preloaded into `keypoint-tester` and should run before `short-interaction-verification`.
## Contract Alignment
Follow `docs/inter_skills_structure.md` strictly:
- Input contract: `GenerativeStateConstructionInput = NormalSubagentInfo`
- Output data contract: `ShortInteractionInfo`
- Output artifacts:
- `{output_dir}/short_interaction_info.json`
- `{output_dir}/test_config.json`
Naming and graph reference:
- Canonical API contract: `docs/inter_skills_structure.md`
- Workflow graph and orchestration semantics: `docs/agentic_workflow_graph_api.md` (non-canonical)
## Input (Task Prompt)
Parse parameters from Task prompt (`key=value`, one per line):
- `game_name`
- `keypoint_id`
- `category`
- `description`
- `output_dir`
- `game_url`
- `project_root`
- `analysis_path` (optional)
- `prestate` (fuzzy, from game description)
- `instruction` (fuzzy, from game description)
- `expected` (fuzzy, from game description)
Important:
- These three text fields are semantic descriptions from the game description layer, not implementation-ready values.
- Preserve their original meaning in `traceability.*_from_game_description`.
## Read Files
Read the following files:
- `games/{game_name}/data.md`
- `games/{game_name}/state_injection_api.md`
- `{analysis_path}` (optional, if exists)
## Workflow
### 1. Parse and Normalize Input
- Parse all prompt fields.
- Keep exact raw strings of `prestate`, `instruction`, `expected` for traceability.
- Validate required fields are non-empty (`game_name`, `keypoint_id`, `category`, `description`, `output_dir`, `game_url`).
### 2. Build Concrete `ShortInteractionInfo`
Translate fuzzy text into concrete structures based on `data.md` schema and implementation notes in `state_injection_api.md` (and `analysis_path` if provided):
```json
{
"keypoint_id": "1",
"category": "State Transition",
"description": "Player collects mushroom and becomes big",
"prestate": {},
"instruction": { "actions": [] },
"expected_result": {},
"verification_contract": {
"precondition_checks": [],
"required_observations": [],
"forbidden_observations": [],
"decision_rule": "all required, none forbidden"
},
"traceability": {
"prestate_from_game_description": "...",
"instruction_from_game_description": "...",
"expected_result_from_game_description": "..."
}
}
```
Rules:
- `prestate` must be data-level and injectable.
- `instruction.actions` must be executable and ordered (`keypress`, `keydown`, `keyup`, `wait`, `type`).
- `expected_result` must be checkable from `window.getGameState()`.
- `verification_contract` should make the keypoint harder to game:
- `precondition_checks`: how to tell the localized scenario was actually established
- `required_observations`: positive evidence required for PASS
- `forbidden_observations`: nearby wrong behaviors or confounders that must remain absent
- `decision_rule`: usually `all required, none forbidden`
- If fuzzy text is ambiguous, choose one reasonable mapping and keep the original fuzzy text in `traceability`.
- Prefer fields that exist in `data.md` and are injectable via `state_injection_api.md`.
- For boundary-condition, warning, pause, or transient-feedback keypoints, prefer a near-threshold injectable state that starts close to the target scenario instead of requiring the verifier to play from the main menu or an unrelated baseline state.
- For verification-strengthening, do not stop at "what should happen"; also encode "what nearby incorrect outcome must not count as success".
### 3. Write `{output_dir}/short_interaction_info.json`
Write the full `ShortInteractionInfo` structure to disk.
### 4. Build Canonical `test_config.json`
Convert `ShortInteractionInfo` into executable config with canonical field names:
```json
{
"game_url": "http://localhost:3000",
"precondition_state": {},
"interaction": { "actions": [] },
"expected_state": {},
"verification_contract": {
"precondition_checks": [],
"required_observations": [],
"forbidden_observations": [],
"decision_rule": "all required, none forbidden"
},
"metadata": {From NetX-lab/GameGen-Verifier, MIT.
More from NetX-lab/GameGen-Verifier
- 0.519score
auto-eval
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Run the full game-gen-eval pipeline in normal mode (state injection + visual interaction verification) from a generated game.
SkillCFreshness: C★ 3Claude Code +9 - 0.519score
game-gen-with-data
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Generate a runnable TypeScript game from a game description file, plus data.md and a state injection API for evaluation.
SkillCFreshness: C★ 3Claude Code +9 - 0.519score
distill-verified-keypoints
NetX-lab
Extract discrete, verifiable, specification-first keypoints from a game description for subsequent evaluation of game logic correctness.
SkillCFreshness: C★ 3Claude Code +9 - 0.514score
analyze-game-implementation
NetX-lab
Analyze game source code and write a concise implementation summary for downstream testing.
SkillCFreshness: C★ 3Claude Code +9 - 0.513score
keypoint-orchestrator
NetX-lab
Orchestrate normal keypoint testing with bounded concurrency.
SkillCFreshness: C★ 3Claude Code +9 - 0.511score
short-interaction-verification
NetX-lab
Execute Playwright verification for one concrete short interaction and write result artifacts.
SkillCFreshness: C★ 3Claude Code +9 - 0.508score
cross-verification
NetX-lab
Recheck an existing normal keypoint evaluation artifact without rerunning the game.
SkillCFreshness: C★ 3Claude Code +9 - 0.508score
recheck-orchestrator
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Run cross-verification on an existing evaluation run and generate recheck_summary.md plus recheck_comparison.json.
SkillCFreshness: C★ 3Claude Code +9
Signals
- Stars
- 3
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jun 7, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/NetX-lab/GameGen-Verifier
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from NetX-lab
- 0.519score
auto-eval
NetX-lab
Run the full game-gen-eval pipeline in normal mode (state injection + visual interaction verification) from a generated game.
SkillCFreshness: C★ 3Claude Code +9 - 0.519score
game-gen-with-data
NetX-lab
Generate a runnable TypeScript game from a game description file, plus data.md and a state injection API for evaluation.
SkillCFreshness: C★ 3Claude Code +9 - 0.519score
distill-verified-keypoints
NetX-lab
Extract discrete, verifiable, specification-first keypoints from a game description for subsequent evaluation of game logic correctness.
SkillCFreshness: C★ 3Claude Code +9 - 0.514score
analyze-game-implementation
NetX-lab
Analyze game source code and write a concise implementation summary for downstream testing.
SkillCFreshness: C★ 3Claude Code +9 - 0.513score
keypoint-orchestrator
NetX-lab
Orchestrate normal keypoint testing with bounded concurrency.
SkillCFreshness: C★ 3Claude Code +9 - 0.511score
short-interaction-verification
NetX-lab
Execute Playwright verification for one concrete short interaction and write result artifacts.
SkillCFreshness: C★ 3Claude Code +9
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Cite this page
generative-state-construction skill. Agent Mart. https://agentmart.dev/skills/netx-lab-generative-state-construction (last updated 2026-10-24)