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honouralexwill

memory-referee

by honouralexwill · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install memory-referee
Description
Memory hygiene and adjudication layer for OpenClaw agent workflows. Deduplicates entities, resolves naming conflicts, separates facts from goals from specula...
Usage Guidance
This skill appears coherent and self-contained: it adjudicates an input array of memory records in-process and returns a report and structured JSON. Before installing, consider: 1) Review the included source (you already have it) or run the tests to verify behavior matches your expectations — classification, similarity threshold (0.8), and staleness TTL (30 days) are heuristic and may need tuning. 2) It does not persist data or contact external services by default, so secrets are not requested or used. 3) The repository contains developer governance notes (CLAUDE.md referencing a 'saturnday' tool) — these are developer workflow instructions and do not execute during normal runtime, but follow them only if you intend to modify the code. 4) For very large record sets (>100k), the README warns this is in-process only and not optimized for streaming; consider batching or an external pipeline for scale. If you want higher assurance, run the test suite (npm test), inspect the compiled dist files that will be executed, and optionally run a static scan for any changes before use.
Capability Analysis
Type: OpenClaw Skill Name: memory-referee Version: 0.1.0 The memory-referee skill is a utility for processing and cleaning agent memory records through deduplication, keyword-based classification (fact vs. goal vs. speculation), and heuristic conflict detection. The code is well-structured, includes a comprehensive test suite, and operates entirely in-memory without any network or sensitive file system access. No evidence of malicious intent, data exfiltration, or prompt injection was found; the 'Saturnday' branding and governance files (CLAUDE.md, saturnday-state.json) appear to be part of the AI-assisted development workflow used to generate the skill.
Capability Assessment
Purpose & Capability
The name/description (deduplication, classification, staleness, conflict detection, provenance) align with the included TypeScript modules (dedupe, classify, conflicts, staleness, schema, render, index). No unexpected external services, binaries, or credentials are requested.
Instruction Scope
SKILL.md and README describe in-memory adjudication of provided record arrays and a simple CLI/library API. The runtime instructions do not tell the agent to read unrelated system files, call external endpoints, or exfiltrate secrets. Note: the repository includes a CLAUDE.md with governance commands referring to a 'saturnday' tool and rules like 'Do NOT edit files directly' — these are developer/workflow instructions and are not invoked automatically by the skill at runtime; they do not change runtime behavior but are worth reviewing if you will modify the code.
Install Mechanism
There is no install spec for downloading or executing remote archives. The package is a normal node project (package.json, tsc build) with typical dev dependencies and a small runtime dependency (tsx). Nothing in the manifest points to fetching code from untrusted URLs or running opaque installers.
Credentials
The skill declares no required environment variables, no primary credential, and no config paths. The code does not reference process.env or other secret-bearing sources in the provided files. Credential and environment access are proportionate to the stated purpose (none required).
Persistence & Privilege
The skill does not request permanent inclusion (always: false) and contains no code that persists to external stores or modifies other skills. It runs in-process and returns adjudication output; the README explicitly states 'No persistence'. Autonomous invocation is allowed by platform default but is not combined here with broad privileges or credential access.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install memory-referee
  3. After installation, invoke the skill by name or use /memory-referee
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Initial release of memory-referee: a robust memory adjudication layer for OpenClaw agent workflows. - Deduplicates memory entities and resolves naming conflicts - Separates facts, goals, and speculation; assigns clear classification - Detects contradictions, archives stale records, and enforces schema consistency - Preserves full provenance for each record after deduplication/merging - Produces a human-readable adjudication report and structured JSON output
Metadata
Slug memory-referee
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is memory-referee?

Memory hygiene and adjudication layer for OpenClaw agent workflows. Deduplicates entities, resolves naming conflicts, separates facts from goals from specula... It is an AI Agent Skill for Claude Code / OpenClaw, with 137 downloads so far.

How do I install memory-referee?

Run "/install memory-referee" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is memory-referee free?

Yes, memory-referee is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does memory-referee support?

memory-referee is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created memory-referee?

It is built and maintained by honouralexwill (@honouralexwill); the current version is v0.1.0.

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