# Extraction Log ## 1. Purpose This file records model extraction events. Use it to track how models are extracted from articles, prompts, discussions, notes, and other source material. The extraction log helps preserve provenance. It answers: ```text Where did this model come from? When was it extracted? What confidence level was assigned? What needs user review? What was promoted, rejected, merged, or deferred? ``` ## 2. Entry Format Use this format for each extraction event: ```md ## YYYY-MM-DD — {Source Title} ### Source ```text title: path: author: source_type: date_written: ``` ### Extraction Context ```text reason_for_extraction: requested_by: extractor: mode: Model Mining Mode ``` ### Extracted Models | Model ID | Model Name | Type | Confidence | Status | Proposed Path | Notes | | -------- | ---------- | ---- | ---------- | ------ | ------------- | ----- | ### Non-Model Ideas ```text - - - ``` ### Skill Conversion Candidates ```text - - - ``` ### Agent Conversion Candidates ```text - - - ``` ### Runtime Usage Candidates ```text - - - ``` ### Open Questions ```text 1. 2. 3. ``` ### Next Action ```text ``` ## 3. Confidence Levels Use: ```text high medium low ``` ### high The model is explicit, named, and structurally complete. ### medium The model is strongly implied but not fully formalized. ### low The model is plausible but requires user confirmation. ## 4. Status Values Use: ```text candidate draft active rejected merged deprecated archived ``` Default for implicit extractions: ```text candidate ``` Default for structurally clear but unconfirmed extractions: ```text draft ``` Use `active` only after user confirmation. ## 5. Initial Extraction Notes ## 2026-05-31 — Initial Known Model Seed ### Source ```text title: User-provided examples in CCPE System construction discussion path: conversation context author: Wantsong source_type: discussion date_written: 2026-05-31 ``` ### Extraction Context ```text reason_for_extraction: Initialize model library with known model names mentioned by user. requested_by: Wantsong extractor: CCPE Forge planning process mode: Model Mining Mode ``` ### Extracted Models | Model ID | Model Name | Type | Confidence | Status | Proposed Path | Notes | | ----------------- | ------------------------- | ---------------------------- | ---------- | --------- | --------------------------------------------------- | -------------------------------------------------------------------------------------------------- | | cognitive-imaging | 认知显影 / Cognitive Imaging | intermediate; workflow-model | high | active | model-cards/intermediate/cognitive-imaging-model.md | Promoted after source review, Lite regression tests, and user confirmation. | | giant-cognition | 巨人认知 / Giant Cognition | intermediate | medium | candidate | TBD | Mentioned as an existing model-backed agent. Source material needed. | | cognitive-prism | 认知棱镜 / Cognitive Prism | intermediate; applied | medium | candidate | TBD | Mentioned as an existing review model. Source material needed. | ### Non-Model Ideas ```text - CCPE Forge as Creator / Auditor / Refactor / Model Mining Skill - Review Committee as possible Hybrid Runtime - Human-in-the-loop as design principle ``` ### Skill Conversion Candidates ```text - cognitive-imaging.skill.md - prediction-error-capture.skill.md - do-operator-test.skill.md - model-mining.skill.md ``` ### Agent Conversion Candidates ```text - cognitive-imaging-practitioner.agent.md (deferred) - review-committee-chair.agent.md ``` ### Runtime Usage Candidates ```text - review-committee.runtime.md - modeling-committee.runtime.md - article-to-model-extraction.runtime.md ``` ### Open Questions ```text 1. Should Cognitive Imaging be marked active after full Model Card creation? 2. Should Giant Cognition and Cognitive Prism be extracted from source articles before indexing further? 3. Should prediction-error-capture be a separate Model Card or only a Skill under Cognitive Imaging? ``` ### Next Action ```text Create full Model Card for Cognitive Imaging from source material. Then run Model Mining Mode on source articles for Giant Cognition and Cognitive Prism. ``` ## 2026-06-01 — 认知显影 Model Card Promotion ### Source ```text title: 大脑暗房：关于洞察力的显影术; 认知显影者1.1 path: workbench/raw/2026-01-06-the-darkroom-of-brain.md; workbench/raw/认知显影者1.1.md author: Wantsong source_type: article; legacy agent prompt date_written: 2026-01-06; unknown ``` ### Extraction Context ```text reason_for_extraction: Promote the stable embedded cognitive model behind 认知显影者 Lite into a canonical Model Card and update the active Model Index. requested_by: Wantsong extractor: CCPE Forge mode: Model Mining Mode ``` ### Extracted Models | Model ID | Model Name | Type | Confidence | Status | Proposed Path | Notes | | -------- | ---------- | ---- | ---------- | ------ | ------------- | ----- | | cognitive-imaging | 认知显影 / Cognitive Imaging | intermediate; workflow-model | high | active | model-cards/intermediate/cognitive-imaging-model.md | Stable named model. Active Lite prompt exists at agents/lite/cognitive-imaging-practitioner.prompt.md. | ### Non-Model Ideas ```text - 认知显影者 as a Web-style expert prompt. - Article review committee as a future orchestration pattern. - Scenario probe as a CCPE classification prerequisite. ``` ### Skill Conversion Candidates ```text - cognitive-imaging.skill.md - prediction-error-capture.skill.md - causal-exposure-test.skill.md ``` Skill conversion is deferred. It should happen only if Codex automatic invocation or cross-agent method reuse becomes necessary. ### Agent Conversion Candidates ```text - cognitive-imaging-practitioner.agent.md ``` Agent conversion is deferred. The active production artifact is currently the Lite prompt. ### Runtime Usage Candidates ```text - article-review-committee.runtime.md - modeling-committee.runtime.md ``` Runtime design is deferred until the other review or modeling agents are upgraded. ### Open Questions ```text 1. Should prediction-error-capture become a separate Skill later? 2. Should causal-exposure-test be generalized as a reusable evaluation Skill? 3. Which future committee should first consume 认知显影 as a formal Agent node? ``` ### Next Action ```text Keep 认知显影 active as Model Card + Lite Prompt. Do not create Skill, Agent Spec, or Runtime until scenario demand appears. ``` ## 6. Maintenance Rules When a new extraction is performed: ```text 1. Add an entry with date and source. 2. Record all extracted models. 3. Record confidence level. 4. Record status. 5. Record proposed Model Card path. 6. Record Skill / Agent / Runtime conversion candidates. 7. Record open questions. 8. Update model-index.md. 9. Update model-dependency-map.md if relationships are known. 10. Update model-usage-map.md if usage is known. ``` ## 7. Final Rule The extraction log should make model formation traceable. A model without provenance is harder to trust, harder to revise, and harder to integrate.