agentleFS
mahsumaktasmahsumaktas/agent-evolution-kit/skills/agent-memory-proactivity-ops/SKILL.md

agent-system-memory-proactivity-ops

Skill2 starsChanged 9 months agoMITView on GitHub

Audit and improve AgentSystem memory retention, proactive behavior, learning quality, and agent consistency. Use when bots forget context, skip memory writes, wait for explicit prompts, or repeat the same errors across runs.

The file

We split this skill into the rules it gives an agent

5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill to keep memory and proactivity loops healthy after config, prompt, or plugin changes.

    line 8
  2. Distinctive:

    Verify scheduler contract and agent consistency metrics.

    line 29
  3. Distinctive:

    Verify learning score, filler count, and output quality in recent memory jobs.

    line 30
  4. Distinctive:

    Check latest runs of:

    line 31
  5. Distinctive:

    If quality is below target, tighten prompt constraints and re-run smoke checks.

    line 35

≈ “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

AgentSystem Memory Proactivity Ops

Use this skill to keep memory and proactivity loops healthy after config, prompt, or plugin changes.

Quick Start

  1. Run baseline checks:
bash scripts/proactivity_check.sh --hours 24
  1. Apply known remediation pack:
bash /Users/user/clawd/scripts/agent-system-plan2-remediate.sh
  1. Re-check and smoke-test memory jobs:
bash scripts/proactivity_check.sh --hours 6 --smoke

Workflow

  1. Verify scheduler contract and agent consistency metrics.
  2. Verify learning score, filler count, and output quality in recent memory jobs.
  3. Check latest runs of:
  • hachi-self-compound (046bb2db-42ad-4b79-96d4-f9aa1392ede8)
  • memory-fact-extraction (079a9ab8-d706-465c-801d-741f4a2d55a3)
  1. Validate learning artifacts were updated recently.
  2. If quality is below target, tighten prompt constraints and re-run smoke checks.

Resources

  • scripts/proactivity_check.sh: one-command audit + optional memory smoke tests.
  • references/quality-gates.md: acceptance thresholds and remediation sequence.

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.