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paragraph-curator段落策展人

Agent Skill

paragraph-curator 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

465

周安装

19

GitHub Stars

422

下载量

149
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:paragraph-curator(段落策展人)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/paragraph-curator
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill paragraph-curator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill paragraph-curator

简介

用于查找、检索和筛选相关信息,支持基于关键词或任务场景快速定位候选结果。

  • 适用于需要信息收集、线索追踪或研究支持的场景,帮助 Agent 高效获取目标内容。
  • 通过 npx skills add 命令从指定仓库安装,建议结合原始 README 核验具体用法。
  • 使用前需确认权限范围、维护状态,并评估是否涉及联网、命令执行或文件读写操作。
  • paragraph-curator 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Paragraph Curator (select -> evaluate -> subset -> fuse)

Purpose: turn “keep rewriting and getting longer” into a controlled convergence step.

This skill adds a decision layer between “draft paragraphs” and “polish voice”:

  • keep the best paragraphs
  • merge redundant ones
  • rewrite for clearer argument moves
  • expand only when coverage is missing (using existing evidence cards)

This is a content-structure pass (not a style pass). Run style-harmonizer and opener-variator after curation.

Inputs

Required:

  • sections/ (especially H3 bodies: sections/S<sub_id>.md)
  • outline/writer_context_packs.jsonl (what each H3 must cover + allowed citations)
  • output/ARGUMENT_SKELETON.md (single source of truth for terminology + premises)

Recommended:

  • output/SECTION_ARGUMENT_SUMMARIES.jsonl (paragraph moves + outputs)
  • output/SECTION_LOGIC_REPORT.md (paragraph linkage risks)
  • output/WRITER_SELFLOOP_TODO.md (style smells / scope/citation warnings)

Outputs

  • Updated sections/*.md (same filenames; body-only; no headings)
  • output/PARAGRAPH_CURATION_REPORT.md (short; PASS/FAIL + what changed)
  • Create sections/paragraphs_curated.refined.ok when done (empty file; pipeline contract signal)

What this skill optimizes (rubric)

You are not trying to “shorten”. You are trying to increase information density while keeping the section verifiable.

Score each paragraph on a simple 0-2 rubric:

Criterion0 (bad)1 (ok)2 (good)
Coveragedoes not match any required axis/cardmatches one axis, thindirectly executes a must-use card/comparison
Noveltyrepeats nearby contentpartially redundantadds a distinct comparison/insight
Move clarityunclear what it doesmove exists, weak outputclear move + reusable output
Consistencypremise/term drift vs skeletonminor mismatchfully aligned with Consistency Contract
Citation hygieneuncited when it should be; cite-dump vibeacceptablecitations are local and anchored (not just tail)
Fusion readinesscannot merge; tangledmergeable with editsclean unit that can be fused or kept

Decision labels:

  • KEEP: keep mostly as-is
  • REWRITE: keep content, rewrite for clearer move/output
  • FUSE: merge with neighbor(s) and rewrite into one stronger paragraph
  • REPLACE: keep the slot, but rewrite using existing evidence cards (when coverage is missing)

Paragraph budget (profile-aware)

Default per-H3 target:

  • draft_profile=survey: 10-12 paragraphs
  • draft_profile=deep: 11-13 paragraphs

If you exceed the budget, do not delete content blindly. Prefer FUSE (merge redundancy) and make the fused paragraph denser.

Must-have coverage checklist (per H3)

Each H3 must contain at least:

  • 1x Definition/Setup (only if this H3 introduces a new term/protocol field)
  • 2x concrete Contrast paragraphs (A-vs-B comparisons; not just “many papers do...”)
  • 1x Evaluation anchor paragraph (task + metric + constraint/budget/tool access; cite-backed)
  • 1x cross-paper Synthesis paragraph (what generalizes, what does not; cite-backed)
  • 1x Boundary/Failure paragraph (limitations; threats to validity; cite-backed when possible)
  • 1x Local conclusion (a reusable takeaway used downstream)

If any item is missing, use REPLACE to write that paragraph from the writer context pack (do not invent new facts).

Workflow (minimal)

  1. Pick the target set
  • Start with the H3 bodies listed in output/SECTION_LOGIC_REPORT.md, plus any H3 flagged in output/WRITER_SELFLOOP_TODO.md as repetitive/template-y, plus any H3 that keeps growing across edits.
  • Work file-by-file: each target is a concrete sections/S<sub_id>.md.
  1. Build a paragraph inventory (scratch only; do not paste into the paper)
  • If output/SECTION_ARGUMENT_SUMMARIES.jsonl exists, use its per-paragraph moves/output as the first draft of your inventory, then reconcile with the actual text. For each paragraph, write one line:
  • P<i>:: move(s) -> output (1 sentence):: citations (keys)
  1. Apply the rubric and label each paragraph
  • Mark KEEP/REWRITE/FUSE/REPLACE.
  • If two adjacent paragraphs repeat the same axis, FUSE.
  • For any paragraph you plan to change (REWRITE/REPLACE/FUSE), draft 2-3 candidate rewrites in parallel (different angles: contrast-first / protocol-first / synthesis-first).

- Score candidates quickly with the rubric; keep one winner (or fuse two if they cover complementary axes). - Keep citation keys unchanged while sampling; you are choosing surface form + structure, not changing the evidence set.

  1. Construct the curated set
  • Use outline/writer_context_packs.jsonl to enforce must-have coverage (paragraph_plan/must_use/comparison_cards/limitation_hooks) without inventing new content.
  • Enforce the must-have coverage checklist.
  • Enforce the paragraph budget by fusing redundancy rather than deleting substance.
  1. Fuse + rewrite (keep citation keys fixed) Rules that keep the pipeline stable:
  • Do not add/remove citation keys; when fusing, carry citations forward and re-anchor them to the right sentence.
  • Do not move citations across subsections.
  • Avoid adjacent citation blocks (e.g., [@a] [@b]) and duplicate keys in one block (e.g., [@a; @a]).
  • When fusing, it is often faster to write two fused candidates (one contrast-heavy, one synthesis-heavy) and pick the better one.
  1. Write the report + marker
  • output/PARAGRAPH_CURATION_REPORT.md should be short and actionable:

- - Status: PASS|FAIL - per H3: paragraph count before/after; what was fused; any remaining gaps - (minimal) how many candidates you tried for the main rewrites (e.g., 2-3), so future passes can see whether this was a real selection step

  • Create sections/paragraphs_curated.refined.ok.

Routing rules

  • If you cannot fill a missing must-have paragraph without new evidence: stop and route upstream (evidence-selfloop / C3-C4). Do not pad.
  • If you feel forced to change a definition or evaluation premise: update output/ARGUMENT_SKELETON.md# Consistency Contract first, then rerun argument-selfloop.
  • If the only issue is surface cadence/openers: do not overwork curation; run style-harmonizer / opener-variator.

Done checklist

  • Each targeted H3 stays within its paragraph budget (survey 10-12; deep 11-13) without losing required moves.
  • Redundant paragraphs are fused into denser, clearer ones (not just deleted).
  • No citation keys were added/removed; citation shape is reader-facing (no adjacent blocks, no dup keys).
  • output/PARAGRAPH_CURATION_REPORT.md exists and is understandable.
  • sections/paragraphs_curated.refined.ok exists.

Script

Quick Start

  • python.codex/skills/paragraph-curator/scripts/run.py --workspace workspaces/<ws>

All Options

  • --workspace <dir> (required)
  • --unit-id <U###>
  • --inputs <semicolon-separated>
  • --outputs <semicolon-separated>
  • --checkpoint <C#>

Examples

  • Curate paragraphs in a survey workspace:

- python.codex/skills/paragraph-curator/scripts/run.py --workspace workspaces/survey-llm-agents

适合场景

01

研究助手

02

事实核查

03

知识库问答

04

带来源的搜索总结

能力概览

能力 1

组合搜索和大模型调用

能力 2

支持多来源检索和总结

能力 3

强调引用来源和事实核查

能力 4

适合研究型 Agent 流程

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.25%
按下载量换算54

Claude

28.75%
按下载量换算43

Cursor

17.65%
按下载量换算26

Gemini CLI

8.44%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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