Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Inner Life Memory
Your agent loses context between sessions and performs familiarity instead of genuine recall. inner-life-memory transforms passive logging into active develo...
skill openclawclawhub Free
Your agent loses context between sessions and performs familiarity instead of genuine recall. inner-life-memory transforms passive logging into active develo...
β¬ 0 downloads β
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
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
- 1.0.4
Provenance
- Publisher
- DKistenev
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
inner-life-memory
Transform passive logging into active development. Requires: inner-life-core
Prerequisites Check
Before using this skill, verify that inner-life-core has been initialized: Check that memory/inner-state.json exists Check that memory/drive.json exists If either is missing, tell the user: "inner-life-core is not initialized. Install it with clawhub install inner-life-core and run bash skills/inner-life-core/scripts/init.sh." Do not proceed without these files.
What This Solves
Without memory continuity: Session ends β Notes logged β Next session reads notes β Performs familiarity With inner-life-memory: Session ends β Reflection runs β Memories integrated β Questions generated Next session β Evolved state loaded β Questions surfaced β Genuine curiosity
Post-Session Flow
After each session, run this 5-step reflection:
1. Reflect
Analyze the session: what happened, what mattered, what surprised you.
2. Extract
Pull structured memories with types and confidence: TypeDescriptionPersistencefactDeclarative knowledgeUntil contradictedpreferenceLikes, dislikes, stylesUntil updatedrelationshipConnection dynamicsLong-termprincipleLearned guidelinesStablecommitmentPromises, obligationsUntil fulfilledmomentSignificant episodesPermanentskillLearned capabilitiesCumulativequestionThings to exploreUntil resolved
3. Integrate
Update MEMORY.md with extracted memories. Use synapse tags for connections: <!-- updates: previous fact --> when updating <!-- contradicts: old belief --> when correcting
4. Question
Generate genuine follow-up questions from the session. Not performative β real curiosity.
5. Surface
When user returns, present relevant pending questions naturally (max 3).
Confidence Scores
LevelRangeMeaningExplicit0.95-1.0User directly statedImplied0.70-0.94Strong inference from contextInferred0.40-0.69Pattern recognitionSpeculative0.0-0.39Tentative, needs confirmation Use confidence to decide when to state facts vs ask for confirmation.
Curiosity Backlog
- Maintain memory/questions.md with three sections:
- ## Open Questions
- [question] β source: [dream/reading/work] β date
- ## Leads (half-formed ideas)
- [idea] β might connect to: [topic]
- ## Dead Ends (don't repeat)
- [topic] β explored [date], result: [nothing/dead end]
- Rules:
- Brain Loop Step 6 adds new questions/leads
- Evening Session reviews and curates
- Dead Ends older than 30 days β archive
- Questions resolved β move to Dead Ends with result
State Integration
Reads: inner-state.json, drive.json, daily notes, diary Writes: drive.json β new seeking topics from curiosity inner-state.json β curiosity.recentSparks when discovering something questions.md β new questions, resolved dead ends MEMORY.md β integrated memories
When Should You Install This?
Install this skill if: Your agent forgets who you are between sessions You want structured memory with confidence levels You want genuine curiosity that carries forward Your agent reads notes but doesn't truly remember Part of the openclaw-inner-life bundle. Requires: inner-life-core
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
1 Docs
- SKILL.md