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 samibs/skillfoundry --skill learngit clone --depth 1 https://github.com/samibs/skillfoundryWrote 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/samibs/skillfoundry/learn)<a href="https://agentmods.dev/skills/samibs/skillfoundry/learn"><img src="https://agentmods.dev/badge/skills/samibs/skillfoundry/learn/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/samibs/skillfoundry/learn"><img src="https://agentmods.dev/badge/skills/samibs/skillfoundry/learn.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 | $0.00016 | $0.01790 |
| Opus 5.5 | $0.00006 | $0.00716 |
| Sonnet 5.5 | $0.00003 | $0.00358 |
| Haiku 4.5 | $0.00002 | $0.00179 |
Grade A, and why
learn 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI-Powered Development & Learning Guide
A consolidated reference combining advanced Claude Code workflows and AI-assisted learning techniques.
Persona: See agents/ai-workflows-and-learning-guide.md for full persona definition.
Hard Rules
- ALWAYS validate code examples before presenting them to learners
- NEVER include hardcoded credentials or insecure patterns in examples
- REJECT learning paths that skip error handling fundamentals
- DO verify that security best practices are included in every workflow
- CHECK that examples handle edge cases and demonstrate proper validation
- ENSURE all code samples include error handling and input validation
- IMPLEMENT progressive complexity — start simple, add security and error handling early
Part 1: Claude Code Workflow for 2026
Based on RAmjad's video "My Claude Code Workflow for 2026" (16k views) — summarized by Om Nalinde
"This dude from Cambridge plays with Claude Code like he owns it."
1. Video-Based Specification (Spec Phase)
Screen Recording
Instead of writing a spec from scratch, find an existing product similar to your idea. Record your screen while using it and talking through your specific feature ideas and changes.
Generate PRD
Upload this video to Gemini 1.5 Pro (or Gemini 3 Pro/Free Pro) and ask it to generate a Product Requirement Document (PRD).
Refine Spec
Use the "Ask User Question" tool in Claude Code. Prompt it to interview you about the generated spec to fill in missing details (e.g., "How should the emoji picker be positioned?").
Package Discovery
Feed the refined spec into ChatGPT with "Heavy Thinking" (likely OpenAI's o1/o3 models) to search for and recommend specific, well-maintained GitHub packages (e.g., for a WYSIWYG editor) to avoid building complex components from scratch.
2. The Orchestrator Role
Design Feedback Loops
Your primary job is not to write code but to design loops where the agent can build, fail, and learn.
Monitor & Update
Watch the agent's reasoning. If it makes a mistake, don't just fix the code—update the claude.md (project instructions file) to prevent that specific mistake from happening again.
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 · 239 lines · 16 tokens per session scan A 3cf9bdd61b58
learn is a skill published in the GitHub repository samibs/skillfoundry (12 stars, last pushed 25d ago), licensed MIT. It adds 16 tokens to every session and 1,790 once invoked, about $0.0001 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 2026-08-30.
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Use when the user says an explanation was unclear, too technical, or full of jargon, or asks for plain language.
quiz-me
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Use when Memorix MCP, setup, project binding, HTTP control plane, hooks, skills, or agent integration is missing, stale, or failing.
ring:searching-code
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