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 Osipchuk/agent-skills --skill templatesgit clone --depth 1 https://github.com/Osipchuk/agent-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/osipchuk/agent-skills/templates)<a href="https://agentmods.dev/skills/osipchuk/agent-skills/templates"><img src="https://agentmods.dev/badge/skills/osipchuk/agent-skills/templates.svg" alt="Measured on agentmods" 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 | $0.00072 | $0.00223 |
| Opus 5 | $0.00036 | $0.00112 |
| Sonnet 5 | $0.00014 | $0.00045 |
| Haiku 4.5 | $0.00007 | $0.00022 |
Grade A, and why
my-skill 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.
What it actually says
My Skill
Workflow and instructions go here. Use progressive disclosure: keep the core
workflow in this file, and put bulky templates, reference material, or helper
scripts in references/ and scripts/ subfolders, pulled in on demand. Helper
scripts should be stdlib-only / dependency-free where possible.
Do NOT put presentation/catalog metadata (summary, tags, license, when,
highlights, example) in this frontmatter. That lives in catalog/<name>.yaml,
which is never installed to a user — see the authoring guide in
docs/skill-authoring.md.
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.
- 8d ago First seen · 23 lines · 72 tokens per session scan A e7a6393bbab7
my-skill is a skill published in the GitHub repository Osipchuk/agent-skills (5 stars, last pushed 28d ago), licensed MIT. It adds 72 tokens to every session and 223 once invoked, about $0.0004 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.
Other skills, from other repositories
opensrc
Fetch dependency source code to give AI agents deeper implementation context. Use when the agent needs to understand how a library works internally, read source code for a package, fetch implementation details for a dependency, or explore how an npm/PyPI/crates.io package is built. Triggers include "fetch source for"…
lx
Codebase exploration tool that reads many files or whole directories in a single call, with per-file headers, glob include/exclude filters, function/type skeleton extraction (signatures only, no bodies), and head/tail line slicing.
audit
Audit phase. Parallel review: code quality + security + tests. Semantic dedup of cross-mode findings. Outputs PASS/WARN/FAIL per dimension. Validates spec coverage.
discover
Trigger: vague/unfocused request or solution-without-problem. Also invoked via /discover command. Reframes goal before acting.
orbit
State-persisted autonomous pipeline: spec → go → audit → eval → ship → evolve in one command. Auto-detects direct/council/interactive mode. Crash-recoverable via PIPELINE-.json. Hands-off until PR.
reflect
On-demand human self-assessment of AI usage quality. Scores 5 dimensions from session data. Not agent performance review.