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 majiayu000/claude-skill-registry --skill agent-expert-creation-melodic-software-claude-code-pluginsgit clone --depth 1 https://github.com/majiayu000/claude-skill-registryWrote 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/majiayu000/claude-skill-registry/agent-expert-creation-melodic-software-claude-code-plugins)<a href="https://agentmods.dev/skills/majiayu000/claude-skill-registry/agent-expert-creation-melodic-software-claude-code-plugins"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-expert-creation-melodic-software-claude-code-plugins/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/majiayu000/claude-skill-registry/agent-expert-creation-melodic-software-claude-code-plugins"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-expert-creation-melodic-software-claude-code-plugins.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 | $0.00043 | $0.02970 |
| Opus 5 | $0.00022 | $0.01485 |
| Sonnet 5 | $0.00009 | $0.00594 |
| Haiku 4.5 | $0.00004 | $0.00297 |
Grade A, and why
agent-expert-creation 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 7d 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 — 480 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Expert Creation Skill
Create specialized agent experts that learn and maintain domain knowledge through the Act-Learn-Reuse pattern.
Core Problem Solved
"The massive problem with agents is this. Your agents forget. And that means your agents don't learn."
Generic agents execute and forget. Agent experts execute and learn by maintaining expertise files (mental models) that sync with the codebase.
When to Use
- Repeated complex tasks in a domain (database, billing, WebSocket)
- High-risk systems where mistakes cascade (security, payments)
- Rapidly evolving code that needs tracked mental models
- Need consistent domain expertise across sessions
- Building plan-build-improve automation cycles
The Act-Learn-Reuse Pattern
┌─────────────────────────────────────────────────────────────┐
│ ACT-LEARN-REUSE CYCLE │
├─────────────────────────────────────────────────────────────┤
│ │
│ ACT ──────────► LEARN ──────────► REUSE │
│ │ │ │ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ Take useful Update expertise Read expertise │
│ action file via file FIRST on │
│ (build, fix) self-improve next execution │
│ prompt │
│ │
└─────────────────────────────────────────────────────────────┘
| Step | Action | Purpose |
|---|---|---|
| ACT | Take a useful action | Generate data to learn from (build, fix, answer) |
| LEARN | Store new information in expertise file | Build mental model automatically via self-improve prompt |
| REUSE | Read expertise first on next execution | Faster, more confident execution from mental model |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 480 lines · 43 tokens per session scan A 7a0ad59a3818
agent-expert-creation is a skill published in the GitHub repository majiayu000/claude-skill-registry (600 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 2,970 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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