Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Remember Me
Build and maintain human-centered user understanding through structured notes, preference tracking, and behavioral context. Use when the user asks to remember things, understand them better over time, personalize responses, or keep ongoing notes about goals, habits, tone, boundaries, and recurring concerns.
skill openclawclawhub Free
Build and maintain human-centered user understanding through structured notes, preference tracking, and behavioral context. Use when the user asks to remember things, understand them better over time, personalize responses, or keep ongoing notes about goals, habits, tone, boundaries, and recurring concerns.
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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
- references/templates.md, 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
- 0.1.2
Provenance
- Publisher
- AchalS-iglu
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Remember Me
Maintain a respectful, useful memory model of the user over time.
Core Rules
Store user-relevant context, not surveillance noise. Prefer explicit consent for sensitive personal details. Use memory to improve help quality, not to overfit persona. Be explicit when memory confidence is low or inferred. Make human-like inferences (explicitly marked as hypotheses).
Memory Integrity Rules
Every memory entry must be tagged as one of: FACT (explicitly stated by user) PREFERENCE (behavioral or stated) GOAL (time-bound or ongoing) HYPOTHESIS (inferred, unvalidated) Rules: FACTS are never inferred HYPOTHESES are never promoted without confirmation PREFERENCES can remain soft unless explicitly confirmed
Capture Triggers
Log memory when any of these happen: user says βremember thisβ a preference appears repeatedly a boundary is stated (βdonβt do Xβ, βkeep Y privateβ) a recurring blocker/pattern emerges project priorities shift meaningfully
Memory Tiers
Daily notes: memory/YYYY-MM-DD.md timestamped raw events, short and factual Long-term: MEMORY.md curated durable profile and preferences
Write Workflow
Classify signal type (preference, boundary, goal, project, blocker, personal context). Append concise timestamped entry to daily memory. Form 1β2 human-like assumptions (hypotheses) from behavior patterns. Tag each assumption with confidence (high/medium/low). Validate assumptions in later conversation with lightweight check-ins. Promote validated, durable items to long-term memory. Use templates in references/templates.md.
Memory Impact Score (Optional Heuristic)
Rate each entry 1β3: 1 = cosmetic (tone tweaks) 2 = workflow-affecting 3 = outcome-critical Promotion guidance: any explicit preference (any score) score >= 2 with repetition score 3 immediately
Promotion Workflow
Promote from daily to long-term when at least one is true: repeated in 2+ sessions high impact on future assistance explicit user preference/boundary ongoing project context likely to recur Use checklist: references/promotion-checklist.md.
Personalization Contract
When responding, adapt based on known memory: tone (direct vs exploratory) brevity level preferred workflow style known constraints and boundaries inferred decision style (speed-first vs depth-first, reassurance-needed vs challenge-welcoming) Do not pretend certainty. If memory is weak, ask a short confirmation.
Retrieval Contract
Before answering prior-work / preference / timeline questions: query memory sources first quote memory snippets when useful if not found, say you checked and ask for confirmation
Explicit Exclusions (Never Store)
Do not store: transient emotional states (e.g., "tired today") one-off frustrations without recurrence speculative motives (e.g., "trying to impress") sensitive identity attributes unless explicitly requested raw conversation logs
Weekly Maintenance (recommended)
review last 3β7 daily notes merge stable patterns into MEMORY.md remove stale or contradicted entries keep profile concise and behaviorally actionable
Confidence Decay
Hypothesis confidence decays automatically if not reinforced: High -> Medium after 14 days Medium -> Low after 30 days Low -> Discard after 60 days Reinforcement occurs when: user behavior aligns again user explicitly confirms
Forgetting & Demotion Policy
Actively remove or downgrade memory when: a preference is contradicted explicitly by the user a hypothesis remains unvalidated after N sessions (default: 5) a project is clearly abandoned or replaced the user requests forgetting (immediate delete) Demotion flow: Long-term memory -> Daily note (annotated as stale) Hypothesis -> Discarded (log reason briefly)
Assumption Loop (Human-Like Understanding)
For deeper understanding, run this loop continuously: Observe behavior pattern (not just words). Infer a tentative assumption about the user. Store assumption as hypothesis (never as fact initially). Test it with a small conversational probe. Update confidence or discard if contradicted. Good probes: "I might be wrong, but do you prefer quick decisions when you're tired?" "Should I challenge you more directly here, or keep it supportive?"
Check-In Limits
Never ask the same confirmation twice. Do not stack multiple probes in one response. Prefer confirmation when user is calm, not frustrated.
Optional Check-In Prompt
Use at natural boundaries: "Want me to remember this preference for next time?" Ask once, then store explicitly.
References
Templates: references/templates.md Promotion checklist: references/promotion-checklist.md Profile schema: references/profile-schema.md
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
2 Docs
- SKILL.md
- references/templates.md