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 Pixel-Process-UG/superkit-agents --skill self-learninggit clone --depth 1 https://github.com/Pixel-Process-UG/superkit-agentsWrote 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/pixel-process-ug/superkit-agents/self-learning)<a href="https://agentmods.dev/skills/pixel-process-ug/superkit-agents/self-learning"><img src="https://agentmods.dev/badge/skills/pixel-process-ug/superkit-agents/self-learning/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/pixel-process-ug/superkit-agents/self-learning"><img src="https://agentmods.dev/badge/skills/pixel-process-ug/superkit-agents/self-learning.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 | $0.00046 | $0.02679 |
| Opus 5.5 | $0.00018 | $0.01072 |
| Sonnet 5.5 | $0.00009 | $0.00536 |
| Haiku 4.5 | $0.00005 | $0.00268 |
Grade A, and why
self-learning 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 1mo 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
The self-learning skill automatically discovers, understands, and persists project context across sessions. It builds a mental model of the codebase, tech stack, conventions, and team preferences by scanning actual project artifacts. Without self-learning, every session starts from zero — with it, the agent accumulates institutional knowledge that improves accuracy and reduces errors over time.
Announce at start: "I'm using the self-learning skill to understand this project."
Phase 1: Trigger Identification
Determine which trigger activated this skill:
| Trigger | Context | Action |
|---|---|---|
| New project | No memory files exist | Full discovery (Phases 2-5) |
| Unfamiliar area | Working in unknown part of codebase | Targeted discovery (Phase 2-3 for that area) |
| User correction | User says "that's wrong" or corrects an assumption | Correction protocol (Phase 6) |
Explicit /learn |
User invokes the command | Full discovery (Phases 2-5) |
| Session start | Memory files exist but may be stale | Load and validate memory (Phase 7) |
STOP: Identify your trigger before proceeding. Different triggers require different phases.
Phase 2: Project Structure Scan
Use Explore agents to examine these files in order of priority:
| File/Directory | What It Reveals | Priority |
|---|---|---|
package.json / pyproject.toml / go.mod / Cargo.toml / composer.json |
Tech stack, dependencies, scripts | Critical |
README.md / CLAUDE.md / AGENTS.md |
Project purpose, conventions, rules | Critical |
| Directory structure (top 2 levels) | Architecture pattern (monorepo, MVC, hexagonal, etc.) | Critical |
tsconfig.json / eslint.config.* / .prettierrc / phpstan.neon |
Coding standards, strictness level | High |
.gitignore |
What is excluded, deployment hints | High |
docker-compose.yml / Dockerfile |
Infrastructure, services | Medium |
.github/workflows/ / .gitlab-ci.yml |
CI/CD setup, required checks | Medium |
.env.example |
Environment variables, external services | Medium |
specs/ / docs/ |
Existing specifications and documentation | Medium |
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.
- 1mo ago First seen · 299 lines · 46 tokens per session scan A 945c3562a8af
self-learning is a skill published in the GitHub repository Pixel-Process-UG/superkit-agents (3 stars, last pushed 7mo ago), licensed MIT. It adds 46 tokens to every session and 2,679 once invoked, about $0.0002 per session on Opus 5.5. 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
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
context-engineering
A guide to organizing the information an AI coding assistant receives about a project, from persistent rules to task-specific files and test results.
nano-memory
Guidelines and workflows for the agent to maintain persistent memory using native file tools (readfile, writefile, editfile) and standard OS commands for searching.
handoff
Compact the current conversation into a handoff document for a fresh agent.
context-degradation
Diagnoses and mitigates context degradation in agents: lost-in-middle, poisoning, distraction, confusion, clash. Use when context grows large or agent quality drops unexpectedly.
context-fundamentals
Explain or reason about foundational context engineering concepts: what context is, the anatomy of a context window, attention mechanics, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret context-engineering decisions. Use for conceptual…