agentleFS
ladyicebergladyiceberg/openclaw-memory-quality/skills/memory-promote/SKILL.md

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.

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
  1. Very specific:

    Run /memory-cleanup first to remove low-quality entries before Dreaming

    line 27
  2. Distinctive:

    Read-only — never modifies any files.

    line 41

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Memory Promote

Pre-promotion quality audit. Run before OpenClaw's Dreaming process promotes short-term memories to MEMORY.md.

Steps

  1. Determine parameters:
    • top_n: user specified number, or default 10
    • use_llm: user said "--llm" or "with LLM" → True, otherwise False
  2. Call memory_promotion_audit_oc(top_n=N, use_llm=...)
  3. 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-cleanup first 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 keep
  • one_time_context — one-off context with low long-term value, consider skipping
  • uncertain — 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.

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.