120 lines
3.9 KiB
Markdown
120 lines
3.9 KiB
Markdown
# Workflow
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## 1. Model Extraction Workflow
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For detailed model extraction gates, see `docs/MODEL_EXTRACTION_WORKFLOW.md`.
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The project follows this flow:
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```text
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Original article / representative text
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-> source article record
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-> source evidence excerpts
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-> human-readable model card
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-> machine-readable model JSON
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-> regression cases
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-> selector examples
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-> validation report
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```
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## 2. Development Workflow
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For each task:
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1. Read `README.md` and `AGENTS.md`.
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2. Check `docs/PROJECT_BRIEF.md`.
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3. Modify the smallest necessary set of files.
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4. Keep JSON and Markdown versions consistent.
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5. Run or update validation.
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6. Update reports or handoff notes.
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7. Do not expand scope without confirmation.
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## 3. Model Addition Workflow
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When adding a new model:
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1. Create a model JSON file in `models/`.
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2. Create a human-readable card in `cards/`.
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3. Add source article records.
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4. Add source evidence excerpts.
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5. Add regression cases.
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6. Add selector examples if relevant.
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7. Run `python scripts\rebuild_indexes.py --write`.
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8. Run validation.
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9. Update documentation.
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## 4. Stabilization Workflow
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If a model is unstable:
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1. Mark `needs_stabilization: true`.
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2. Add risks in `stability_profile.main_risks`.
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3. Add boundary and misuse regression cases.
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4. Do not upgrade to stability level A until tests pass.
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## 5. Handoff Workflow
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For ChatGPT-specific handoff rules, see `docs/CHATGPT_HANDOFF_RULES.md`.
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At the end of each work session:
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1. Summarize what changed.
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2. List created and modified files.
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3. Record validation status.
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4. Separate assumptions from verified facts.
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5. List questions that require product or CCRA judgment.
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6. Suggest the smallest useful next tasks.
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If the owner is going from Codex to ChatGPT, create a `reports/ChatGPT交接文档_<topic>_<YYYY-MM-DD>.md` file before ending the work session.
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Before handoff, run:
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```powershell
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python scripts\rebuild_indexes.py --check
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python scripts\validate_model_library.py
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```
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## 6. Supplier Request Workflow
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When this repository needs a capability owned by a neighboring repository:
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1. Classify the need.
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2. Use `requirements/ccpe/` for expert-agent, runtime, model-governance, invocation, evaluation, or integration-registration needs.
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3. Use `requirements/skills-vault/` for deterministic automation, reusable scripts, validation helpers, extraction helpers, or installable Skill needs.
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4. Write one request file per missing capability.
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5. Pause the dependent model extraction or implementation step unless the project owner explicitly says to solve the need locally.
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6. Resume after the supplier artifact, installed Skill, or owner decision is available.
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## 7. Duplex Model Extraction Workflow
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Model extraction has two concurrent tracks:
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```text
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Workflow/tooling track -> request or build the process and supporting capabilities.
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Content-processing track -> use available capabilities to extract, validate, and stabilize models.
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```
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Do not let the content-processing track silently absorb missing tooling work.
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If a tool gap appears, record it as a supplier request and stop the blocked extraction step.
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## 8. Framework Adoption Workflow
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Third-party agentic frameworks such as CrewAI or LangGraph may be introduced only for concrete product implementation.
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Before adoption:
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1. Record the product reason.
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2. Identify whether CCPE needs to supply an Agent Spec, Runtime Spec, authority rule, evaluation rule, or integration registration.
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3. Identify whether skills-vault needs to supply reusable deterministic helpers.
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4. Keep framework adapters, state, deployment, and product-specific behavior in this repository.
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5. Record the boundary decision in `docs/DECISIONS.md`.
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## 9. Knowledge Asset Workflow
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For long-term reusable knowledge rules, see `docs/KNOWLEDGE_ASSET_RULES.md`.
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When a rule, model map, schema explanation, workflow summary, or product context becomes stable enough to reuse across sessions, create or update a file under `knowledge_assets/`.
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Do not leave durable knowledge only in temporary reports.
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