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 mickeyyaya/refactoring-skills --skill self-learning-agent-patternsgit clone --depth 1 https://github.com/mickeyyaya/refactoring-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/mickeyyaya/refactoring-skills/self-learning-agent-patterns)<a href="https://agentmods.dev/skills/mickeyyaya/refactoring-skills/self-learning-agent-patterns"><img src="https://agentmods.dev/badge/skills/mickeyyaya/refactoring-skills/self-learning-agent-patterns/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/mickeyyaya/refactoring-skills/self-learning-agent-patterns"><img src="https://agentmods.dev/badge/skills/mickeyyaya/refactoring-skills/self-learning-agent-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00055 | $0.04081 |
| Opus 5 | $0.00028 | $0.02041 |
| Sonnet 5 | $0.00011 | $0.00816 |
| Haiku 4.5 | $0.00006 | $0.00408 |
Grade A, and why
self-learning-agent-patterns 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 9d 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 — 389 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Learning Agent Patterns
Overview
Reactive agents respond to each task independently, carrying no memory of what worked or failed before. Adaptive agents extract structured knowledge from their own experience and apply it to future tasks. Self-learning patterns define the pipeline by which raw observations become instincts, instincts become rules, and rules become skills — moving an agent from static to compound improvement over time.
When to use: Designing agents that operate repeatedly on similar tasks and should improve with use; building multi-agent pipelines with feedback loops; reviewing agent memory architectures for learning capability; any system where observed success and failure should influence future agent behavior.
Quick Reference
| Pattern | Signal | Extraction Method | Graduation Criteria | Application |
|---|---|---|---|---|
| Repeated Correction Capture | User corrects same error 2+ times | Log correction event → diff original vs corrected | 3+ occurrences, confidence > 0.7 | Promote to instinct |
| Success Pattern Mining | Task completes without correction | Extract strategy fingerprint from successful run | 5+ successful uses across sessions | Promote to instinct |
| Failure Taxonomy | Task fails or is retried | Classify error type, extract trigger conditions | 2+ failures with same root cause | Promote to negative rule |
| Preference Accumulation | User accepts output unchanged | Log format/style choices accepted without edit | Consistent across 10+ sessions | Promote to style rule |
| Temporal Outcome Tracking | Downstream metric improves/degrades | Attribute metric change to prior agent decision | Statistically significant correlation | Adjust strategy weight |
Learning Signal Detection
A learning signal is any observable event that carries information about agent performance. Signals must be detected, classified, and recorded before extraction can occur.
Explicit signals — user-initiated:
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.
- 9d ago First seen · 389 lines · 55 tokens per session scan A 03774b604615
self-learning-agent-patterns is a skill published in the GitHub repository mickeyyaya/refactoring-skills (6 stars, last pushed 5mo ago), licensed MIT. It adds 55 tokens to every session and 4,081 once invoked, about $0.0003 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.
Other skills, from other repositories
media-ingest
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
mem0-oss-to-platform
Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…
Cortex
Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…
establishing-project-context
Use when the user asks to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.