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.
In this repository27 other files it gives its agents.
- Agent BrowserSkill
- agent-governanceSkill
- agent-system-learning-loopSkill
- agent-system-live-quality-opsSkill
- agent-system-output-hygiene-opsSkill
- agent-system-stability-opsSkill
- auto-updaterSkill
- deep-researchSkill
- domaindetailsSkill
- hnSkill
- humanizerSkill
- memory-hygieneSkill
- model-usageSkill
- nano-pdfSkill
- news-aggregator-skillSkill
- orchestrator-loopSkill
- prompt-guardSkill
- security-reviewSkill
- self-improvementSkill
- structured-brainstormingSkill
- subagent-executionSkill
- summarizeSkill
- systematic-debuggingSkill
- tavilySkill
- tdd-workflowSkill
- topic-monitorSkill
- verification-gateSkill
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
- Distinctive: line 8
Use this skill to keep memory and proactivity loops healthy after config, prompt, or plugin changes.
- Distinctive: line 29
Verify scheduler contract and agent consistency metrics.
- Distinctive: line 30
Verify learning score, filler count, and output quality in recent memory jobs.
- Distinctive: line 31
Check latest runs of:
- Distinctive: line 35
If quality is below target, tighten prompt constraints and re-run smoke checks.
≈ “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
AgentSystem Memory Proactivity Ops
Use this skill to keep memory and proactivity loops healthy after config, prompt, or plugin changes.
Quick Start
- Run baseline checks:
bash scripts/proactivity_check.sh --hours 24- Apply known remediation pack:
bash /Users/user/clawd/scripts/agent-system-plan2-remediate.sh- Re-check and smoke-test memory jobs:
bash scripts/proactivity_check.sh --hours 6 --smokeWorkflow
- Verify scheduler contract and agent consistency metrics.
- Verify learning score, filler count, and output quality in recent memory jobs.
- Check latest runs of:
hachi-self-compound(046bb2db-42ad-4b79-96d4-f9aa1392ede8)memory-fact-extraction(079a9ab8-d706-465c-801d-741f4a2d55a3)
- Validate learning artifacts were updated recently.
- 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.
Does it work?
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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.

