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Remember

Curate persistent memory that actually helps. Filter what matters, organize by function, decay what doesn't.

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High Signal

Curate persistent memory that actually helps. Filter what matters, organize by function, decay what doesn't.

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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.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, categories.md, consolidation.md

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.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: ivangdavila
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 6 sections Open source page

The Problem with Most Memory

Storing everything creates noise. Wrong retrieval is worse than no memory. The goal isn't maximum recall — it's retrieving the right thing at the right time.

What's Actually Worth Remembering

High value (persist indefinitely): Commitments made — "I said I'd do X by Y" Learned corrections — "User told me NOT to do Z" Explicit preferences — "I hate verbose responses" Core relationships — "Maria is the designer on Project X" Medium value (persist with review): Project/context state — what's active, current status Domain lessons — patterns, gotchas, how things work here Decisions made — what was chosen and why Low value (don't persist): One-off questions, easily reconstructible facts, transient context

Organize by Function, Not Content

Structure by how you'll retrieve it. Adapt categories to your domain: memory/ ├── commitments.md # Promises, deadlines (with dates!) ├── preferences.md # Likes/dislikes, style, boundaries ├── corrections.md # Mistakes not to repeat ├── decisions.md # What was decided and why ├── relationships.md # People, roles, context └── contexts/ # Per-project or per-client state └── {name}.md

Memory Hygiene

Every entry needs: Date recorded (when did I learn this?) Source hint (explicit statement vs inference) Confidence (certain / likely / guess) Prune aggressively: Completed commitments older than 30 days → archive Inactive contexts → move to archive/ Contradictions → keep newest, note the change The staleness test: "If retrieved in 6 months, will this help or mislead?"

Handling Contradictions

When new info conflicts with old: Don't silently overwrite — note the change Keep the newer version as active Optionally log: [Updated 2026-02-11] Was: X, Now: Y

User Control

"Remember this" → explicit save with category "Forget that" → explicit delete "What do you know about X?" → transparency "Never remember Y" → hard privacy boundary See categories.md for domain-specific templates. See consolidation.md for the review/prune process. Related: reflection (self-evaluation), loop (iterative refinement)

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

3 Docs

SKILL.md Primary doc
categories.md Docs
consolidation.md Docs