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citation-injector引文注入器

Agent Skill

citation-injector 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

689

周安装

29

GitHub Stars

422

下载量

241
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

3

许可证

MIT

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:citation-injector(引文注入器)
来源仓库:https://github.com/willoscar/research-units-pipeline-skills
仓库路径:skills/citation-injector
安装命令:
npx skills add https://github.com/willoscar/research-units-pipeline-skills --skill citation-injector
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

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

简介

citation-injector 在满足预算约束的前提下向指定段落注入合规引文。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中平衡局部密集与全局多样性需求时使用。
  • 依据 diversifier 生成的预算报告进行确定性编辑,不引入新事实。
  • 需确保所用 bibkey 已在授权范围内,避免越权引用。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Citation Injector (deterministic baseline edits; budget-as-constraints)

Purpose: make the pipeline converge when the draft is:

  • locally citation-dense but globally under-cited (too few unique keys), or
  • overly reusing the same citations across many subsections.

This skill is intentionally conservative and scriptable:

  • the script edits output/DRAFT.md directly using the budget report as constraints
  • injections stay evidence-neutral (NO NEW FACTS) and use only in-scope keys already listed for each H3

Inputs

  • output/DRAFT.md
  • output/CITATION_BUDGET_REPORT.md (from citation-diversifier)
  • outline/outline.yml (H3 id/title mapping)
  • citations/ref.bib (must contain every injected key)

Outputs

  • output/DRAFT.md (updated in place)
  • output/CITATION_INJECTION_REPORT.md (PASS/FAIL + what you changed)

Non-negotiables (NO NEW FACTS)

  • Only inject keys listed for that H3 in the budget report.
  • Do not introduce new numbers, new benchmarks, or superiority claims.
  • Do not add narration templates (This subsection..., Next, we...).
  • Do not produce cite dumps like [@a; @b; @c] as the only citations in a paragraph.

Paper-voice injection patterns (safe sentence shapes)

Use these as *sentence intentions* (paraphrase; do not copy verbatim).

  1. Axis-anchored exemplars (preferred)
  • Systems such as X [@a] and Y [@b] instantiate <axis/design point>, whereas Z [@c] explores a contrasting point under a different protocol.
  1. Parenthetical grounding (short, low-risk)
  • ... (e.g., X [@a], Y [@b], Z [@c]).
  1. Cluster pointer + contrast hint
  • Representative implementations span both <cluster A> (X [@a], Y [@b]) and <cluster B> (Z [@c]), suggesting that the trade-off hinges on <lens>.
  1. Decision-lens pointer
  • For builders choosing between <A> and <B>, prior systems provide concrete instantiations on both sides (X [@a]; Y [@b]; Z [@c]).
  1. Evaluation-lens pointer (still evidence-neutral)
  • Across commonly used agent evaluations, systems such as X [@a] and Y [@b] illustrate how <lens> is operationalized, while Z [@c] highlights a different constraint.
  1. Contrast without list voice
  • While many works operationalize <topic> via <mechanism> (X [@a]; Y [@b]), others treat it as <alternative> (Z [@c]), which changes the failure modes discussed later.

Anti-patterns (high-signal “budget dump” voice)

Avoid these stems (they read like automated injection):

  • A few representative references include...
  • Notable lines of work include...
  • Concrete examples include...

If your draft contains these, rewrite them immediately using the patterns above (keep citation keys unchanged).

Placement guidance

  • Prefer inserting citations where the subsection already states a concrete contrast or decision lens.
  • If you must add a new sentence/mini-paragraph, place it early (often after paragraph 1) so it reads as positioning, not as an afterthought.
  • Keep injections subsection-specific: mention the subsection lens (H3 title / contrast_hook) so the same sentence cannot be copy-pasted into every H3.

Workflow

  1. Read the budget report (output/CITATION_BUDGET_REPORT.md)
  • Treat Global target (policy; blocking) as the PASS line for the pipeline gate (derived from queries.md:citation_target; A150++ default: recommended).
  • If Gap: 0, do nothing: write a short PASS report and move on.
  • Otherwise, for each H3 with suggested keys, pick enough keys to close the gap to target:

- small gaps: 3-6 keys / H3 - A150++ gaps: often 6-12 keys / H3 Prefer keys that are unused globally and avoid repeating the same new keys across many H3s.

  1. Inject in the right subsection
  • Use outline/outline.yml to confirm H3 ordering and ensure the injected sentence lands inside the correct ### subsection.
  1. Inject with paper voice
  • Prefer one short, axis-anchored sentence over a long enumerator sentence.
  • Keep injections evidence-neutral (NO NEW FACTS) and avoid new numbers.
  • Before you commit an injected key, confirm it exists in citations/ref.bib.
  1. Write output/CITATION_INJECTION_REPORT.md
  • Record which H3s you touched and which keys were added.
  • Mark - Status: PASS only when the global target is met.
  1. Verify
  • Rerun the validator script (below) to recheck the global target.
  • Then run draft-polisher to smooth any residual injection voice (citation keys must remain unchanged).

Done criteria

  • output/CITATION_INJECTION_REPORT.md exists and is - Status: PASS.
  • pipeline-auditor no longer FAILs on “unique citations too low”.

Script (optional; deterministic injector + validator)

You usually do not run this manually; it exists so a pipeline runner can deterministically apply a baseline injection and validate the target.

Quick Start

  • python.codex/skills/citation-injector/scripts/run.py --workspace workspaces/<ws>

All Options

  • --workspace <dir>
  • --unit-id <U###> (optional; for logs)
  • --inputs <semicolon-separated> (rare override; prefer defaults)
  • --outputs <semicolon-separated> (rare override; default validates output/CITATION_INJECTION_REPORT.md)
  • --checkpoint <C#> (optional)

Examples

  • After you generate the budget report and want the script to apply the baseline injection:

- python.codex/skills/citation-injector/scripts/run.py --workspace workspaces/<ws>

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

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

Claude Code

28.08%
按下载量换算68

Gemini CLI

22.85%
按下载量换算55

Cursor

17.6%
按下载量换算42

Codex

11.92%
按下载量换算29

OpenCode

7.26%
按下载量换算17

Antigravity

3.04%
按下载量换算7

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

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来源信息

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