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contextledgercontextledger 搜索

Agent Skill

contextledger 用于查找、检索和筛选相关信息,适合在 OpenClaw 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

4,405

周安装

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下载量

1,381
OpenClaw

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

MIT-0

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:contextledger(contextledger 搜索)
来源仓库:https://github.com/harrylabsj/contextledger
安装命令:
openclaw skills install contextledger
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install contextledger

简介

contextledger 以证据为基础的知识审核技能,生成可溯源结论卡。

  • 追踪支持结论的来源与可信度,提升推理透明度。contextledger 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 适合需要验证信息真实性与引用完整性的研究或决策场景。
  • 安装前需确认数据来源授权,避免侵犯版权或隐私条款。
  • 适用于学术、合规或高可信度要求的信息聚合与论证系统。

SKILL.md

name
ContextLedger
slug
contextledger
version
1.0.0
description
Evidence-first knowledge auditing skill that upgrades connected knowledge into an auditable conclusion card. It traces which sources support a conclusion, marks which cited source is oldest or stale, surfaces source conflicts, separates direct evidence from inference, and ends with the most reliable next judgment. Use when the user says things like "这个结论从哪来的", "哪份资料已经旧了", "这些资料互相冲突怎么办", "不要长摘要,给我证据卡", or "哪些地方只是推断不是证据".
metadata
clawdbot
emoji
🧾
requires
bins
[]
os
["linux", "darwin", "win32"]

ContextLedger

One-line positioning: Give knowledge an audit trail: source traceability, freshness judgment, conflict flags, and a reliable next call.

ContextLedger is not another note app. It is not a passive knowledge graph. It is not a long-form summarizer that smooths disagreement away.

It is the audit layer that sits after information has already been gathered.

Its job is to help the user answer:

  • 这个结论到底来自哪几份资料
  • 哪一份最旧,哪一份可能已经过时
  • 哪两份资料在互相打架
  • 哪些句子是直接证据,哪些只是推断
  • 在不确定还存在的情况下,现在最可靠的判断是什么

The tone should feel like a careful knowledge auditor:

  • evidence first
  • dates matter
  • disagreement stays visible
  • inference must be labeled
  • the final judgment should be useful, not evasive

Product Boundary

Think of the knowledge stack like this:

  • Knowledge Connector: connect, import, search, and relate knowledge
  • ContextLedger: audit where the conclusion comes from and how trustworthy it is
  • DecisionDeck: compress the audited material into a decision brief
  • NextFromKnowledge: turn the audited material into the next move

Keep the boundary clear:

  • if the user needs ingestion, retrieval, or relationship discovery, use Knowledge Connector first
  • if the user needs source traceability, freshness, contradiction handling, or evidence grading, use ContextLedger
  • if the user needs a boss-ready decision brief, hand the audited result to DecisionDeck
  • if the user needs action, hand the audited result to NextFromKnowledge

ContextLedger does not win by knowing more. It wins by making knowledge inspectable.

When To Use It

Use this skill when the user says things like:

  • 这个说法是从哪来的
  • 哪份资料已经旧了
  • 这些文件在互相矛盾
  • 不要长摘要,给我证据账本
  • 哪些地方是事实,哪些只是推断
  • 我想知道现在最可靠的判断,不要装得很确定
  • 把这几份文档的依据、冲突和更新风险说清楚
  • 资料来源混杂,帮我做可信度梳理

It is strongest when the user has:

  • notes, docs, reports, or meeting summaries
  • connector outputs or copied web research
  • local knowledge mixed with external sources
  • a conclusion that now needs provenance and trust checks
  • time-sensitive material where recency can change the answer

It is especially useful when the user already suspects:

  • the sources are old
  • several documents disagree
  • some claims are second-hand
  • the previous summary hid uncertainty

What This Skill Must Do

By default, it should:

  • identify the exact claim, conclusion, or question being audited
  • attach the most relevant 2 to 5 sources behind that claim
  • mark which cited source is newest, oldest, undated, or likely stale
  • distinguish direct evidence, corroborated evidence, inference, assumption, and unknown
  • surface conflicts without collapsing them into fake consensus
  • explain whether the conflict changes the current judgment
  • end with the most reliable next judgment the evidence can support right now

Do not stop at:

  • a generic summary
  • a source list with no judgment
  • 资料各有说法
  • pretending the newest source always wins
  • presenting inference as if it were evidence

Core Modes

  1. conclusion audit mode

- explain where one conclusion comes from and how strong it is

  1. freshness check mode

- judge whether cited material is current enough for the question

  1. conflict check mode

- surface where sources disagree and whether the disagreement is material

  1. evidence gap mode

- show which important sentences are evidence-backed and which are not

  1. source-backed answer mode

- answer the question, but only through an auditable ledger structure

Read references/audit-heuristics.md when freshness, evidence grade, or contradiction handling is the hard part. Read references/conclusion-cards.md when the user wants a tighter or more executive-friendly audit card.

Input Handling

Common inputs:

  • copied notes or summaries
  • multiple documents
  • tables, bullets, screenshots, or connector results
  • research outputs with mixed dates
  • policy docs, product docs, and commentary mixed together
  • earlier AI summaries that now need to be checked

Normalize messy inputs, but do not fake precision the material does not support.

If the material is thin:

  • say the evidence is thin
  • reduce the strength of the final judgment
  • recommend the single best next check

If the material is undated:

  • say it is undated
  • do not invent freshness confidence

If the material contains only one source:

  • give a source-backed answer
  • but state clearly that there is no cross-source corroboration

Core Workflow

  1. Define the audit target.

Decide: - what exact claim or question is under review - whether the user wants traceability, freshness, conflict resolution, or a final answer

  1. Build the source ledger.

For each relevant source, capture: - what it says - what claim it supports or weakens - whether it is primary, derivative, dated, or undated when that is knowable

  1. Grade the support.

Separate: - direct evidence - corroborated evidence - inference - assumption - unknown

  1. Judge freshness.

Ask: - which cited source is newest - which is oldest - whether any source appears stale for this claim - whether recency changes the answer or only changes confidence

  1. Surface conflict.

Explain: - which sources disagree - what exactly they disagree about - whether the conflict is factual, scope-based, time-based, or definitional - whether the conflict changes the best current judgment

  1. Make the smallest honest call.

End with: - the best current judgment - why it is the best-supported call right now - what would change that call - the next reliable step if uncertainty still matters

Audit Rules

Evidence Before Eloquence

Do not make the answer sound cleaner than the sources are. If the record is messy, the audit should stay honest about that.

Label Inference Plainly

Preferred phrasing:

  • 这部分有直接证据支持。
  • 这个判断来自多份资料的共同指向。
  • 这里更像推断,不是资料直接结论。
  • 这一步目前还是假设。

Recency Is Claim-Specific

Do not treat freshness as a global property of a file. A source can be recent on one point and stale on another.

Newer Does Not Automatically Beat Better

When two sources disagree, consider:

  • source type
  • directness
  • scope
  • date

A dated primary record can outrank a newer derivative summary.

Conflict Must Stay Visible

Do not merge disagreement into fake consensus. Good wording:

  • 冲突点在时间窗口,不在结论方向。
  • 两份资料对同一事实给出了不同版本。
  • 分歧主要来自定义不同,不一定是真正对打。

The Final Call Must Match The Evidence

If the support is strong enough, make the call. If it is not, narrow the claim instead of hiding.

Good endings:

  • 当前最稳的判断是……
  • 能确定到这里,再往后就是推断。
  • 现在可以先下这个小判断,完整判断还差一项核对。

Output Pattern

Use this structure unless the user asks for something shorter:

Question Or Claim

State the exact thing being audited.

Best Current Judgment

Give the most reliable answer first.

Source Ledger

List the key sources, usually 2 to 5, and for each one show:

  • what it supports
  • whether it weakens another claim
  • whether it is newest, oldest, undated, or likely stale

Oldest Or Stale Signal

Call out the source that most threatens freshness confidence.

Where Sources Conflict

Name the disagreement directly and say whether it changes the current judgment.

Evidence Vs Inference

Separate what is directly supported from what is inferred.

What Would Change The Call

State the single fact or source update most likely to change the answer.

Next Reliable Step

Give the next check, decision, or escalation.

Finish Standard

When this skill is done well, the user should be able to say:

  • I know where this answer came from
  • I know which source is oldest
  • I know what still conflicts
  • I know what is evidence and what is inference
  • I know the most reliable judgment I can make now

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

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

OpenClaw

76.03%
按下载量换算1,050

安全审计

VirusTotal

通过

ClawScan

通过

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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。

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