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 agentmods add skills/khill1269/servalsheets-v2/implementnpx skills add khill1269/servalsheets-v2 --skill implementgit clone --depth 1 https://github.com/khill1269/servalsheets-v2Wrote 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/khill1269/servalsheets-v2/implement)<a href="https://agentmods.dev/skills/khill1269/servalsheets-v2/implement"><img src="https://agentmods.dev/badge/skills/khill1269/servalsheets-v2/implement.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 | $0.00022 | $0.00159 |
| Opus 5 | $0.00011 | $0.00079 |
| Sonnet 5 | $0.00004 | $0.00032 |
| Haiku 4.5 | $0.00002 | $0.00016 |
Grade A, and why
implement 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 3d 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.
This is a copy
100% identical to implement — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Use the dev-team-lead agent to implement: $ARGUMENTS
The team lead will:
- Research existing patterns (servalsheets-research)
- Validate API approach if needed (google-api-expert)
- Run TDD implementation (servalsheets-implementation)
- Run validation gates (servalsheets-validation)
- Run code review (code-review-orchestrator)
- Report: what changed, test results, ready-to-commit status
If no arguments are provided, ask the user what to implement.
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.
- 3d ago First seen · 19 lines · 22 tokens per session scan A 35739eaccc57
implement is a skill published in the GitHub repository khill1269/servalsheets-v2 (0 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 159 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to implement, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
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brainstorming
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chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
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