Analyze ARIS usage logs to propose evidence-backed SKILL.md patch diffs.
npx skills add https://github.com/Mang30/myskills --skill meta-optimize-mang30
Please help me install this Agent Skill. Skill: meta-optimize Source: https://github.com/Mang30/myskills/tree/main/skills/meta-optimize Command: npx skills add https://github.com/Mang30/myskills --skill meta-optimize-mang30
ARIS usage patterns and friction points remain invisible until users manually adjust skill prompts, defaults, and workflow behaviors, even though the system can learn from past runs.
.aris/meta/events.jsonl and turns recurring behaviors, overrides, failures, and convergence signals into targeted improvement proposals.SKILL.md, applies patches only after user approval, and logs the optimization event.Run the meta-optimize skill after you have at least five logged workflow runs so it can analyze your ARIS event history and propose safe improvements.
High-intent search queries and answers about installing and using this skill.
You can optimize skill prompts by analyzing accumulated `.aris/meta/events.jsonl` usage logs to identify recurring overrides and failures, then proposing evidence-backed diff patches to adjust SKILL.md files and workflow defaults for improved convergence.
Log-driven harness improvement reads ARIS event history to transform recurring behaviors, convergence signals, and failures into targeted patch proposals for SKILL.md files, retry behavior, and reviewer thresholds.
You need at least five logged workflow runs in your ARIS event history before tuning skill defaults, ensuring sufficient data exists to generate safe, evidence-backed improvement proposals for SKILL.md configurations.
Yes, you can apply diff patches to SKILL.md files automatically after user approval, with the system backing up the original file first and logging the optimization event to ensure safe, reversible configuration changes.
Reviewer loops fail to converge when SKILL.md prompts and workflow defaults are misconfigured, which you can resolve by analyzing usage logs for convergence signals and applying cross-model adversarially reviewed patches to adjust thresholds and retry behavior.
Limitations include requiring at least five logged runs for sufficient data, as the system only recommends changes supported by adequate evidence and handles uncertainty explicitly through cross-model adversarial reviews before proposing any SKILL.md modifications.
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