NODEDC_1C/.codex/skills/domain-case-loop/SKILL.md

name: domain-case-loop description: Use this skill when a user wants to iteratively refine one NDC_1C domain case through a multi-agent loop: automated baseline capture, JSON analysis, minimal domain patch, rerun, and before/after verdict.

Domain case loop

This skill packages the standard workflow for iterating on one concrete domain case in NDC_1C.

Use this skill when

• the user wants to improve one domain question end-to-end;
• the answer exists but is noisy, heuristic, partial, or business-useless;
• the route is wrong even if the wording looks better;
• there is a gap between exact compute intent and actual fallback output;
• there are follow-up / continuation bugs that corrupt business context.

Do not use this skill when

• the user is asking for a broad architecture rewrite;
• there is no concrete domain case or no reproducible input;
• the task is only prose editing with no technical/domain component;
• the task is a generic repo cleanup unrelated to domain capability behavior.

Repo-specific runtime map

Read references/repo_runtime_map.md before the first real cycle.

Use these repo-native capture paths:

• automated capture: python scripts/domain_case_loop.py run-case ...
• import existing technical export: python scripts/domain_case_loop.py import-export ...

Workflow

Step 1 - Normalize the case

Create artifacts/domain_runs/<case_id>/case_brief.md with:

• domain name
• raw user question
• expected business meaning
• expected exact capability
• expected result mode
• known constraints
• acceptance criteria draft

Use references/case_brief_template.md.

Step 2 - Capture baseline

Preferred path:

• run python scripts/domain_case_loop.py run-case ...

Fallback path:

• if the user already has a copied technical export markdown, run python scripts/domain_case_loop.py import-export ...

Required artifacts:

• baseline_output.md
• baseline_debug.json
• baseline_turn.json

Step 3 - Analyst verdict

Spawn domain_analyst and provide:

• case_brief.md
• baseline_turn.json
• baseline_output.md
• baseline_debug.json
• optional relevant code excerpts or file paths

Require a full verdict using references/verdict_template.md.

Step 4 - Domain patch

Spawn domain_coder with:

• the case brief
• the analyst verdict
• baseline artifacts

Require:

• a minimal patch
• zero architecture drift
• rerun after changes

Step 5 - Rerun

Capture:

• rerun_output.md
• rerun_debug.json
• rerun_turn.json
• patch_summary.md

Step 6 - Before/after analysis

Spawn domain_analyst again for:

• before/after comparison
• final status recommendation
• quality score from 0 to 100

Step 7 - Final status

Write final_status.md with one of:

• accepted
• partial
• blocked
• needs_exact_capability

Accepted requires:

• quality score >= 80
• no unresolved P0 defects
• no silent heuristic masking

Hard rules

• Do not count heuristic candidates as confirmed business answers.
• If exact data should exist in 1C/MCP, prefer exact route work over prompt cosmetics.
• If exact data does not exist yet in the reachable contour, return a technical insufficiency with a crisp blocker.
• Never fabricate 1C data.
• Keep domain fixes minimal and localized.
• Preserve successful baseline scenarios.
• Treat follow-up continuity as a state-machine problem, not a wording problem.

Domain-specific framing

For this repository:

• architecture must remain unchanged;
• 1C/MCP is the primary source of truth;
• analyst output must be detailed and business-readable;
• answers should be suitable for product hardening, not just debugging notes;
• machine-readable turn artifacts are first-class inputs for analysis.

Recommended artifact set

Use the artifact layout from references/artifact_layout.md.