ccpe-system/model-index/extraction-log.md

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:

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:

## YYYY-MM-DD — {Source Title}

### Source

```text
title:
path:
author:
source_type:
date_written:

Extraction Context

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

-
-
-

Skill Conversion Candidates

-
-
-

Agent Conversion Candidates

-
-
-

Runtime Usage Candidates

-
-
-

Open Questions

1.
2.
3.

Next Action

3. Confidence Levels

Use:

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:

candidate
draft
active
rejected
merged
deprecated
archived

Default for implicit extractions:

candidate

Default for structurally clear but unconfirmed extractions:

draft

Use active only after user confirmation.

5. Initial Extraction Notes

2026-05-31 — Initial Known Model Seed

Source

title: User-provided examples in CCPE System construction discussion
path: conversation context
author: Wantsong
source_type: discussion
date_written: 2026-05-31

Extraction Context

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	认知显影术 / Cognitive Imaging	intermediate; workflow-model	high	draft	model-cards/intermediate/cognitive-imaging-model.md	Model content partially provided in Cognitive Imaging Specialist example. Needs formal Model Card.
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

- 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

- cognitive-imaging.skill.md
- prediction-error-capture.skill.md
- do-operator-test.skill.md
- model-mining.skill.md

Agent Conversion Candidates

- cognitive-imaging-specialist.agent.md
- review-committee-chair.agent.md

Runtime Usage Candidates

- review-committee.runtime.md
- modeling-committee.runtime.md
- article-to-model-extraction.runtime.md

Open Questions

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

Create full Model Card for Cognitive Imaging from source material.
Then run Model Mining Mode on source articles for Giant Cognition and Cognitive Prism.

6. Maintenance Rules

When a new extraction is performed:

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.