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 luffysolution-svg/obsidian-vault-mcp --skill concept-learninggit clone --depth 1 https://github.com/luffysolution-svg/obsidian-vault-mcpWrote 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/luffysolution-svg/obsidian-vault-mcp/concept-learning)<a href="https://agentmods.dev/skills/luffysolution-svg/obsidian-vault-mcp/concept-learning"><img src="https://agentmods.dev/badge/skills/luffysolution-svg/obsidian-vault-mcp/concept-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/luffysolution-svg/obsidian-vault-mcp/concept-learning"><img src="https://agentmods.dev/badge/skills/luffysolution-svg/obsidian-vault-mcp/concept-learning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Prompt Injection · line 8 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable 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 | $0.00060 | $0.00313 |
| Opus 5 | $0.00030 | $0.00156 |
| Sonnet 5 | $0.00012 | $0.00063 |
| Haiku 4.5 | $0.00006 | $0.00031 |
Grade A, and why
concept-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 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.
What it actually says
Concept learning
Read the concept-model contract before structuring or saving the result.
- Define the concept name, kind, source pool, and the user's learning goal.
- Use
literature_retrieveto find definitions, mechanisms, measurements, examples, and boundary cases. - Use targeted
literature_paper_readcalls only where the cross-paper map lacks decisive context. - Keep the load-bearing concepts rather than collecting every term.
- Explain what the concept distinguishes, what it is not, and which prerequisites it depends on.
- Build a relationship chain from conditions through mechanism to observable consequences.
- Add representative paper cases, counterexamples, boundary conditions, neighboring concepts, and a minimal equation or decision rule when justified.
- End with transfer guidance and self-check questions.
- Use
literature_analysis_getto detect an existing concept note, then preview and commit withliterature_analysis_write.
Do not reduce the result to a glossary or invent a relation that the sources cannot support.
User Customizations
Add local concept-learning conventions below this line.
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.
- 9d ago First seen · 29 lines · 60 tokens per session scan A 9f1ca00198a4
concept-learning is a skill published in the GitHub repository luffysolution-svg/obsidian-vault-mcp (80 stars, last pushed 29d ago), licensed MIT. It adds 60 tokens to every session and 313 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 2026-08-30.
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