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 giuseppe-trisciuoglio/developer-kit --skill learngit clone --depth 1 https://github.com/giuseppe-trisciuoglio/developer-kitWrote 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/giuseppe-trisciuoglio/developer-kit/learn)<a href="https://agentmods.dev/skills/giuseppe-trisciuoglio/developer-kit/learn"><img src="https://agentmods.dev/badge/skills/giuseppe-trisciuoglio/developer-kit/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/giuseppe-trisciuoglio/developer-kit/learn"><img src="https://agentmods.dev/badge/skills/giuseppe-trisciuoglio/developer-kit/learn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 151 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Memory Poisoning · line 156 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00077 | $0.02378 |
| Opus 5.5 | $0.00031 | $0.00951 |
| Sonnet 5.5 | $0.00015 | $0.00476 |
| Haiku 4.5 | $0.00008 | $0.00238 |
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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn
Autonomously analyzes a project's codebase to discover development patterns, conventions, and architectural decisions, then generates project rule files in .claude/rules/ for Claude Code to follow.
Overview
This skill acts as the Orchestrator in a two-agent architecture. It coordinates the overall workflow: gathering project context, delegating deep analysis to the learn-analyst sub-agent, filtering and ranking results, presenting findings to the user, and persisting approved rules to .claude/rules/.
The separation of concerns ensures the analyst operates with a focused forensic prompt while the orchestrator manages user interaction and file persistence.
When to Use
Use this skill when:
- User asks to "learn from this project" or "understand project conventions"
- User wants to auto-generate
.claude/rules/files from the existing codebase - User asks to "extract project rules" or "discover patterns"
- User wants Claude Code to learn the project's coding standards
- After joining a new project and wanting to codify existing conventions
- Before starting a large feature to ensure Claude follows project patterns
Trigger phrases: "learn from project", "extract rules", "analyze conventions", "discover patterns", "generate project rules", "learn codebase", "auto-generate rules"
Instructions
Phase 1: Project Context Assessment
Before delegating to the analyst, gather high-level project context:
-
Verify project root: Confirm the current working directory is a project root (has
package.json,pom.xml,pyproject.toml,go.mod,.git/, or similar markers) -
Check existing rules: Scan for pre-existing rule files to understand what is already documented:
# Check for existing rules
ls -la .claude/rules/ 2>/dev/null || echo "No .claude/rules/ directory found"
cat CLAUDE.md 2>/dev/null || echo "No CLAUDE.md found"
cat AGENTS.md 2>/dev/null || echo "No AGENTS.md found"
ls -la .cursorrules 2>/dev/null || echo "No .cursorrules found"
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 · 230 lines · 77 tokens per session scan A f7f2119b6105
learn is a skill published in the GitHub repository giuseppe-trisciuoglio/developer-kit (365 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 2,378 once invoked, about $0.0003 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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