citorigin
Run, inspect, and explain CitOrigin evidence-to-claim audit workflows.
معلومات المصدر
- المستودع
- e10nMa2k/cc-mini
- آخر نشاط في المصدر
- ١٨ مايو ٢٠٢٦ في ١٠:٠٢
- لغة SKILL.md المكتشفة
- الإنجليزية
- النجوم
- ٩٦٤
- التفرعات
- ٣٥٦
خيارات التثبيت
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
مراجعة ملفات المصدر
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
مستكشف الملفات
2 ملفاتعرض SKILL.md
SKILL.md
تعليمات المصدر · معاينة للقراءة فقط- name
- citorigin
- description
- Run, inspect, and explain CitOrigin evidence-to-claim audit workflows.
- context
- inline
- allowed-tools
- Bash, Read, Grep, Glob
- arguments
- task
# Execute CitOrigin Task
User request:
```text
${task}
```
Execute the user request now. This is not a request to describe the skill.
Use this skill for CitOrigin run, inspect, debug, visualize, and explain workflows.
CitOrigin is an audit tool, not a generation tool. The main workflow is:
1. Input externally provided evidence blocks or source documents plus claims.
2. Choose an audit method:
- `shapley`: exact full-subset attribution
- `drop_hold_per_unit`: SelfCite-style diagnostics with `drop`, `hold`, and `selfcite_reward`
- `shapley,drop_hold_per_unit`: output both
3. Generate a front-end HTML result page for inspection.
Do not answer with a generic readiness message. If the request already names a workflow, file, example id, or output path, complete that task in this turn.
## Repository Assumptions
Assume the current working directory is the CitOrigin project root.
Use the active Python environment. Prefer:
```bash
PYTHONPATH=src python -m citorigin.cli ...
```
If the project is already installed in the environment, `python -m citorigin.cli ...`
also works.
Use relative paths from the repository root. Do not assume any machine-specific absolute
paths.
## Interpret Arguments
Read `$ARGUMENTS` and choose the closest workflow:
- Payload scoring: user provides one JSON payload path or says `input-path`, `documents`, `claims`, `evidence blocks`.
- File-based scoring: user provides one claim plus PDF/TXT/JSON evidence files.
- Example-dir scoring: user points to an example directory with PDFs or `documents.json` plus a claims file.
- Visualization: user asks for HTML, front-end display, result page, or `生成可视化 html`.
- Output inspection: user asks to explain a result JSON, compare Shapley vs SelfCite-style scores, or summarize top evidence blocks.
- Project status: user asks for current scope, workflow summary, or handoff context.
If the requested workflow would overwrite an existing output file, choose a new timestamped or descriptive output path unless the user explicitly asked to overwrite.
## Main Product Workflow
When the user asks for the main CitOrigin workflow, interpret it as:
### A. Prepare input
Use one of these three inputs:
1. One payload JSON for `score-claim`
2. One claim text plus evidence files for `score-claim-from-files`
3. One example directory plus claims JSON for `score-claims-from-example`
### B. Choose audit method
- `shapley`
- Exact full-subset attribution
- Main output: `attribution.shapley_values`, `support_strength`, `weakly_grounded`
- `drop_hold_per_unit`
- SelfCite-style per-evidence diagnostics
- Main output: `drop_from_full`, `hold_vs_empty`, `selfcite_reward`
- `shapley,drop_hold_per_unit`
- Output both exact Shapley and SelfCite-style diagnostics
If the user does not specify, default to:
```text
shapley
```
### C. Front-end display
After scoring, use:
```text
scripts/build_attribution_reader_demo.py
```
This HTML builder supports:
- single-claim result JSON from `score-claim`
- batch result JSON from `score-claims-from-example`
- metric switching when `audit_outputs.drop_hold_per_unit` is present:
- `Shapley`
- `SelfCite-style`
- `Hold`
- `Drop`
## Payload Scoring Workflow
Use this when the user provides a JSON payload or wants to score externally provided evidence blocks and claims.
Expected payload shape:
```json
{
"question": "optional string",
"claim_text": "required string",
"documents": [
{
"doc_id": "d1",
"title": "optional string",
"content": "required string"
}
]
}
```
Run:
```bash
PYTHONPATH=src python \
-m citorigin.cli score-claim \
--provider <local_or_api> \
--audit-methods <audit_methods> \
--input-path <payload_json_path> \
--output-path <result_json_path>
```
If `--provider local`, include:
```text
--model-path <model_path> --generator-backend transformers --device-map auto --torch-dtype bfloat16
```
If `--provider api`, include:
```text
--api-config-path ./api_config.env
```
## File-Based Scoring Workflow
Use this when the user gives one claim text plus evidence files.
Supported evidence inputs:
- `--pdf-path`
- `--txt-path`
- `--doc-json-path`
Run:
```bash
PYTHONPATH=src python \
-m citorigin.cli score-claim-from-files \
--provider <local_or_api> \
--audit-methods <audit_methods> \
--claim-text "<claim_text>" \
--question "<optional question>" \
--pdf-path <pdf1> \
--pdf-path <pdf2> \
--doc-json-path <json1> \
--output-path <result_json_path>
```
Use this workflow when the user talks about:
- evidence blocks
- PDF evidence
- TXT evidence
- manually prepared JSON evidence units
## Example-Dir Scoring Workflow
Use this when the user has multiple claims plus documents packaged under one folder.
Expected example directory contents:
- one or more `*.pdf`, or
- `documents.json`
Claims input:
- `claims.json`, or
- a user-specified claims JSON path
Run:
```bash
PYTHONPATH=src python \
-m citorigin.cli score-claims-from-example \
--provider <local_or_api> \
--audit-methods <audit_methods> \
--example-dir <example_dir> \
--claims-path <claims_json_path> \
--output-path <result_json_path>
```
This is the best workflow when the user says:
- input evidence blocks and claims
- run one batch
- score a set of claims
- generate one result file for later visualization
## Visualization Workflow
Use this whenever the user asks for:
- HTML
- front-end display
- result page
- `生成可视化 html`
Run:
```bash
python scripts/build_attribution_reader_demo.py \
--result-path <result_json_path> \
--claims-path <claims_json_path_if_needed> \
--output-html <output_html_path>
```
If the result JSON is from:
- `score-claim`
- `claims-path` is optional
- `score-claims-from-example`
- pass the claims JSON path when available so the page shows the intended claim text
After generation, report both paths:
- result JSON
- output HTML
## Output Inspection Workflow
Use this when the user asks to explain or compare results.
If the result contains `attribution`, summarize:
- `shapley_values`
- `normalized_shapley_values`
- `support_strength`
- `weakly_grounded`
- `dominant_segments`
If the result contains `audit_outputs.drop_hold_per_unit`, summarize:
- `drop_from_full`
- `hold_vs_empty`
- `selfcite_reward`
- top-ranked evidence blocks under each metric
When comparing methods:
- do not compare API and local raw values as if they are on the same scale
- compare rank order and evidence selection patterns
- explain that `selfcite_reward = drop + hold`
## Project Status Workflow
Use this when `$ARGUMENTS` includes `project status`, `scope`, `handoff`, or `what is this project`.
Read if present and relevant:
```text
PROJECT_STATUS.md
README.md
README_EN.md
docs/project_framing_and_evaluation_cn.md
```
Summarize:
- current project scope
- main input format
- supported audit methods
- current visualization path
- known limitations
- recommended next step
## Guardrails
- Do not print, copy, or commit `api_config.env`.
- Do not say CitOrigin generates claims or retrieves literature unless the user explicitly asks about those separate scripts.
- Treat claims and evidence as externally provided inputs.
- Do not modify source code unless the user asks for implementation changes.
عرض على GitHub