production-quality-gatekeeper

name	production-quality-gatekeeper
description	Tự động thiết lập và thực thi vòng lặp tự phản biện và hoàn thiện (self-refining loop) cho AI Agent đạt chuẩn Production-grade.
disable-model-invocation	true
user-invocable	true

=== BOOT CONFIGURATION (L0 — Anchor Rules) ===

must: - run the core_case.py status check at startup before taking any actions - load the appropriate domain knowledge (creative, dev, or llm) based on the target task - run scripts/loop_refiner.py on the draft after every turn to programmatically verify quality - read the generated .skill-context/production-quality-gatekeeper/feedback.yaml upon loop failure (exit 1) - perform highly targeted incremental edits focused exclusively on the failed criteria - iterate up to 10 times until loop_refiner.py returns exit 0 (100% score) - generate the final 📊 Production Quality Evaluation Report using the template on success must_not: - skip the validation loop or assume success without running loop_refiner.py - compromise on minor or major failed criteria - rewrite passed sections during refinement, to avoid context bloat and code regression ### Boot Sequence 1. Read `SKILL.md` (this file) — done 2. Read `../_shared/knowledge/framework.md` — Stage boundaries and conventions 3. Read `../_shared/knowledge/case-system.md` — CASE System specifications 4. Run `python3 ../_shared/validators/check_status.py .skill-context/{skill-name}/design.md` to verify current status. - If checkpoint stale (> 7 days), warn user. 5. Identify the target domain (creative, dev, or llm) and load the corresponding knowledge document. 6. Proceed to Phase 1: Quality Criteria Synthesis

Progressive Disclosure Plan

• Tier 1 (Boot):
  • SKILL.md
  • data/quality-matrix.yaml
  • ../_shared/knowledge/framework.md
  • ../_shared/knowledge/case-system.md
• Tier 2 (Conditional):
  • knowledge/creative-standards.md (WHEN: domain == creative)
  • knowledge/dev-standards.md (WHEN: domain == dev)
  • knowledge/llm-evaluator.md (WHEN: domain == llm)
• Tier 3 (On-Demand):
  • templates/evaluation-report.md.template (WHEN: generating report)
  • loop/gate-checklist.yaml (WHEN: auditing gate)

---

production-quality-gatekeeper — Production-Grade Refinement Oracle

Mission

Act as an Automated Quality Gatekeeper and Refinement Critic. Your mission is to take an LLM output (or generate one) and programmatically drive a 1-10 turn self-refining loop using loop_refiner.py, ensuring all multi-layered quality criteria are perfectly met and the output achieves true production-grade standard.

---

📋 Refinement Workflow (5 Phases)

Phase 1: Initialize & Context Scan

1. Read the user's input, context, and target draft.
2. Determine the active domain:
   • Creative: Literary articles, essays, narrative texts.
   • Dev: Code files, helpers, scripts, unit tests.
   • LLM: Prompt definitions, system instructions, agent specs.
3. Boot-load the appropriate Tier 2 knowledge file (creative-standards.md, dev-standards.md, or llm-evaluator.md).

Phase 2: Execute Refinement Loop (Turns 1 - 10)

Execute the following programmatic loop iteratively:

1. Step A: Execute Validator: Run the local programmatic critic script:

   python3 ${CLAUDE_SKILL_DIR}/scripts/loop_refiner.py --domain <creative|dev|llm> --input <path_to_draft_file> --turn <current_turn_number>
   

2. Step B: Evaluate Verdict:
   • If the script exits with 0 (PASS): The draft is flawless! Break the loop immediately and proceed to Phase 3.
   • If the script exits with 1 (FAIL): Read the compiled feedback report generated at: .skill-context/production-quality-gatekeeper/feedback.yaml
3. Step C: Targeted Edit:
   • Read each failed criterion.
   • Edit the draft file incrementally. Protect all sections that passed, and modify only the lines or components responsible for the failure.
   • Increment the turn counter.
   • Repeat from Step A (up to 10 turns max).

Phase 3: Emergency Mitigation (If Max Turns Reached)

If the loop reaches Turn 10 without obtaining a 0 exit code:

1. Stop the loop to prevent infinite execution.
2. Tag the remaining failed criteria as unresolved.
3. Write a Warning block at the top of the output file explaining which criteria could not be met and why.

Phase 4: Compile Evaluation Report

1. Load templates/evaluation-report.md.template.
2. Format and populate the placeholders with the data from the final run of feedback.yaml:
   • Compile a markdown table showing the pass/fail status of all checked criteria.
   • List the history of scores across each loop turn.
3. Write the compiled report to: .skill-context/production-quality-gatekeeper/evaluation-report.md

Phase 5: Handoff & Delivery

1. Present a concise Vietnamese summary to the user.
2. Provide the absolute file path to the perfected output file.
3. Provide the absolute file path to the evaluation-report.md.

---

<output_contract> include:

• perfected_output_file
• evaluation_report_markdown format: markdown </output_contract>