memory-promote
Skill1 starsChanged 7 months agoMITView on GitHub
Pre-promotion quality audit for OpenClaw short-term memory candidates — checks the top-N entries before Dreaming promotes them to MEMORY.md, detects deleted source files, low-value snippets (imports/comments/debug code), already-promoted duplicates, and false-positive signals. Optional LLM long-term value advisory. Use this skill whenever the user wants to audit memories before Dreaming runs, check promotion candidates, review what's about to be promoted, or uses phrases like "promotion audit", "before dreaming", "check before promote", "memory promotion", "晋升前检查", "晋升审计", "Dreaming 前", "记忆晋升". Use this skill proactively when the user asks about Dreaming or upcoming memory promotions.
In this repository4 other files it gives its agents.
- memory-checkSkill
- memory-cleanupSkill
- memory-diagnoseSkill
- soul-checkSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 27
Run /memory-cleanup first to remove low-quality entries before Dreaming
- Distinctive: line 41
Read-only — never modifies any files.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Memory Promote
Pre-promotion quality audit. Run before OpenClaw's Dreaming process promotes short-term memories to MEMORY.md.
Steps
- Determine parameters:
top_n: user specified number, or default 10use_llm: user said "--llm" or "with LLM" → True, otherwise False
- Call
memory_promotion_audit_oc(top_n=N, use_llm=...) - Summarize in one sentence, then show the full report
Summary format
Top 10 candidates: 7 pass, 2 suggested skip (1 source deleted, 1 import-only), 1 flagged (low avg score).
What users can do with results
This tool outputs recommendations only — it doesn't block Dreaming. Options for the user:
- Ignore and let Dreaming run normally
- Manually remove unwanted entries from the short-term memory file
- Run
/memory-cleanupfirst to remove low-quality entries before Dreaming
LLM mode adds
Long-term value advisory for each passing/flagged entry:
long_term_knowledge— abstract knowledge or stable design facts, good to keepone_time_context— one-off context with low long-term value, consider skippinguncertain— not enough context to judge
Advisory doesn't affect Dreaming — it's informational only.
Notes
Score shown is approximate (±10%, consolidation dimension excluded). Read-only — never modifies any files. LLM mode requires an API key.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

