Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oyi77/1ai-skills --skill agent-self-improvementgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oyi77/1ai-skills/agent-self-improvement)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/agent-self-improvement"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/agent-self-improvement/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/agent-self-improvement"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/agent-self-improvement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00047 | $0.04334 |
| Opus 5 | $0.00023 | $0.02167 |
| Sonnet 5 | $0.00009 | $0.00867 |
| Haiku 4.5 | $0.00005 | $0.00433 |
Grade A, and why
agent-self-improvement scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 551 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
A meta-skill that monitors the performance of all other skills in the portfolio, identifies bottlenecks and failure patterns, suggests improvements, and optionally applies optimizations. This is the flywheel that makes every other skill better over time. Track usage metrics, error rates, execution times, and user satisfaction to continuously improve the skill library.
Required Tools
- Metrics Storage: SQLite (
.omc/metrics.db) or JSON logs (.omc/logs/skill-metrics.jsonl) - Analysis: Python with pandas for data analysis
- Git: For tracking skill changes and A/B testing variants
- OMC State:
.omc/state/for skill execution tracking - Session Logs:
.omc/sessions/for historical performance data - Python 3.10+ with pandas, sqlite3
Capabilities
- Track skill usage frequency, success rate, and execution time
- Identify skills with high error rates or poor user satisfaction
- Detect unused or redundant skills in the portfolio
- Generate improvement proposals based on failure patterns
- A/B test skill variants to measure improvement impact
- Monitor skill trigger accuracy (false positives/negatives)
- Track cross-skill dependencies and bottlenecks
- Generate weekly/monthly skill portfolio health reports
When to Use
Trigger phrases:
-
"agent self improvement"
-
"Weekly maintenance routine for skill portfolio"
-
"After adding new skills, check portfolio balance"
-
"When a skill's error rate exceeds threshold (>10%)"
-
Weekly maintenance routine for skill portfolio
-
After adding new skills, check portfolio balance
-
When a skill's error rate exceeds threshold (>10%)
-
Before major skill updates, baseline current performance
-
When planning which skills to add/improve/remove
-
After user complaints about skill quality
When NOT to Use
- Task is outside your authorization scope
- You need to implement controls (use implementing-* skills)
- Task is about analysis, not action (use analyzing-* skills)
- You don't have access to target systems
- Task requires compliance expertise (consult professionals)
- Task is about defense, not offense (use defensive skills)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 551 lines · 47 tokens per session scan A 7bba81a38624
agent-self-improvement is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 4,334 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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