ContextLedger is an evidence-first knowledge auditing skill that transforms connected information into an auditable conclusion card with full source traceability.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install contextledger
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install contextledger using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is ContextLedger?
ContextLedger provides an essential audit layer for information gathered by AI agents. Unlike traditional summarizers that smooth over discrepancies, this tool highlights source conflicts, evaluates data freshness, and distinguishes direct evidence from mere inference. It ensures that every conclusion has a clear provenance, allowing users to trust the integrity of their knowledge base.
By integrating ContextLedger into Openclaw Skills workflows, developers and researchers can maintain a high standard of information accountability. It wins by making knowledge inspectable rather than just voluminous, providing a rigorous check on the reliability of any judgment. It is the perfect tool for when the truth matters more than a simple summary.
ContextLedger Use Cases
- Identifying the exact origin of a specific claim or conclusion within a large dataset.
- Detecting stale or outdated documents within a research pool to maintain accuracy.
- Resolving contradictions between multiple information sources without losing visibility of the conflict.
- Distinguishing between factual evidence and logical inference in complex reports.
- Creating source-backed answer ledgers for executive-level decision-making.
How ContextLedger Works
- Define the audit target by identifying the specific claim, conclusion, or question under review.
- Build a source ledger capturing what each document says and its primary or derivative nature.
- Grade the support by categorizing information into direct evidence, corroborated evidence, inference, or assumption.
- Judge freshness by identifying the newest and oldest sources to determine if recency impacts the current conclusion.
- Surface conflicts by explaining exactly where sources disagree and whether the disagreement is material to the judgment.
- Make the smallest honest call, providing the best-supported judgment and identifying what facts could change that call.
ContextLedger Setup
To use ContextLedger within your Openclaw Skills environment, ensure you have the necessary documentation in your references directory.
# Install the skill to your local environment
openclaw install contextledger
# Reference the audit heuristics and conclusion cards
# [references/audit-heuristics.md]
# [references/conclusion-cards.md]
ContextLedger Data Schema & Taxonomy
The skill organizes data using a structured audit trail and metadata taxonomy to ensure transparency.
| Data Component | Description |
|---|---|
| Audit Target | The exact claim or question being scrutinized. |
| Source Ledger | A list of 2-5 key sources with support/weakening flags and freshness markers. |
| Freshness Signal | Identification of the oldest or most likely stale source. |
| Evidence vs Inference | Classification of content as direct evidence, inference, or assumption. |
| Final Judgment | The most reliable call based strictly on the available evidence. |
ContextLedger Advanced Features
- Multi-mode auditing including conclusion audit, freshness check, conflict check, and evidence gap modes.
- Claim-specific recency checks that prevent global staleness errors by evaluating dates per individual point.
- Seamless integration with Knowledge Connector for ingestion and DecisionDeck for executive reporting.
- Automated identification of the "single fact" or source update most likely to change a conclusion's validity.
SKILL.md
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