Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
Memory Bench Pioneer
Be one of the first to benchmark your agent's memory — and help shape how AI remembers. Runs a peer-review-grade evaluation suite (LLM-as-judge, nDCG/MAP/MRR...
skill openclawclawhub Free
Be one of the first to benchmark your agent's memory — and help shape how AI remembers. Runs a peer-review-grade evaluation suite (LLM-as-judge, nDCG/MAP/MRR...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, scripts/collect.py, scripts/rate.py, scripts/submit.sh, scripts/test_metrics.py, scripts/testset.json
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.0.0
Provenance
- Publisher
- globalcaos
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Memory Bench
Collect, assess, and submit anonymized memory system statistics for the ENGRAM and CORTEX research papers.
1. Assess Retrieval Quality
Run the standard test set (30 queries across 4 types × 3 difficulty levels) with LLM-as-judge: # Full assessment with GPT-4o-mini judge + ablation (recommended) python3 scripts/rate.py --queries 30 --judge openai --ablation # Without OpenAI key: local embedding judge (weaker, marked in output) python3 scripts/rate.py --queries 30 --judge local --ablation # Custom test set python3 scripts/rate.py --testset path/to/queries.json --judge openai What it measures: RAR (Recall Accuracy Ratio), MRR (Mean Reciprocal Rank) nDCG@5, MAP@5, Precision@5, Hit Rate All metrics include 95% bootstrap confidence intervals Ablation: runs with AND without spreading activation to isolate its contribution Judge methods: openai — GPT-4o-mini rates each (query, result) pair 1-5. Independent from retrieval system. ~$0.01 per run. local — Embedding cosine similarity. Weaker, marked as such in output. Zero cost. Standard test set (scripts/testset.json): 30 queries stratified across semantic/episodic/procedural/strategic types and easy/medium/hard difficulty. No lexical overlap with stored memories. All deployments run the same queries for cross-site comparability.
2. Collect Statistics
python3 scripts/collect.py --contributor GITHUB_USER --days 14 --output /tmp/memory-bench-report.json Collected (anonymized): Memory counts/types/ages, strength/importance histograms, association graph size, hierarchy levels, consolidation history, retrieval metrics (RAR/MRR/nDCG/MAP with CIs), ablation results, judge method, algorithm version, embedding coverage. Instance ID is a random UUID (not reversible). Never collected: Memory content, queries, file paths, usernames, hostnames.
3. Submit as PR
scripts/submit.sh /tmp/memory-bench-report.json GITHUB_USERNAME Forks, branches, places report, updates INDEX.json, opens PR. Requires gh CLI.
Validation Protocol
For peer-review-ready data, contributors should: Run rate.py --ablation --judge openai (minimum N=30 queries) Collect at least 2 reports from the same instance, ≥7 days apart (longitudinal) Report the algorithm version (auto-captured from git)
Test Set Format
Custom test sets are JSON arrays: [ { "id": "T01", "query": "...", "category": "semantic|episodic|procedural|strategic", "difficulty": "easy|medium|hard" } ]
Agent Workflow
When asked to submit benchmarks: run rate.py --ablation --judge openai, then collect.py, review summary, then submit.sh. Share the PR link.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Scripts1 Docs1 Config
- SKILL.md
- scripts/collect.py
- scripts/rate.py
- scripts/submit.sh
- scripts/test_metrics.py
- scripts/testset.json