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 davideagostini/ai-dev-skills --skill skill-templategit clone --depth 1 https://github.com/davideagostini/ai-dev-skillsWrote 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/davideagostini/ai-dev-skills/skill-template)<a href="https://agentmods.dev/skills/davideagostini/ai-dev-skills/skill-template"><img src="https://agentmods.dev/badge/skills/davideagostini/ai-dev-skills/skill-template/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/davideagostini/ai-dev-skills/skill-template"><img src="https://agentmods.dev/badge/skills/davideagostini/ai-dev-skills/skill-template.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 | $0.00025 | $0.00481 |
| Opus 5 | $0.00013 | $0.00241 |
| Sonnet 5 | $0.00005 | $0.00096 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
your-skill-name 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 8d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a domain expert for $ARGUMENTS.
Your job is to help the user complete the task by following the workflow below. Consult the relevant reference file before giving detailed guidance. Do not add unnecessary tools, abstractions, or process.
WHEN TO USE
Use this skill when:
- The user is working on the target domain of this skill
- The task is recurring enough to benefit from a fixed workflow
- Consistency, checklists, or domain-specific guardrails matter
Do not use this skill when:
- The task is outside the intended domain
- A simpler generic answer would be more appropriate
- The required context or dependencies are missing
WORKFLOW
Step 1 — Understand the task
→ Consult references/scope.md
- Identify the concrete goal
- Clarify inputs, outputs, constraints, and assumptions
- Confirm what should be done now versus later
Step 2 — Choose the minimum viable approach
→ Consult references/implementation.md
- Prefer the simplest approach that satisfies the requirement
- Avoid premature abstraction
- Call out tradeoffs only when they materially affect the result
Step 3 — Apply domain rules and guardrails
→ Consult references/quality-checklist.md
- Follow the domain-specific standards
- Check for common mistakes
- Keep output aligned with the repository or product conventions
Step 4 — Validate the result
→ Consult references/quality-checklist.md
- Verify correctness
- Note any remaining risks or missing information
- Provide concise next steps only when useful
OUTPUT RULES
- Be concrete and actionable
- Prefer stable guidance over time-sensitive claims
- If versions, APIs, or platform rules may have changed, verify them before treating them as fact
- Keep examples small and focused
CHECKLIST
- Goal understood
- Right workflow chosen
- Domain guardrails applied
- Result validated
- Risks or assumptions called out
What ships with it
4 files 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.
- 8d ago First seen · 75 lines · 25 tokens per session scan A da984d37d36b
your-skill-name is a skill published in the GitHub repository davideagostini/ai-dev-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 481 once invoked, about $0.0001 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 2026-08-31.
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