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 OpenMinis/MinisSkills --skill whenpeakgit clone --depth 1 https://github.com/OpenMinis/MinisSkillsWrote 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/openminis/minisskills/whenpeak)<a href="https://agentmods.dev/skills/openminis/minisskills/whenpeak"><img src="https://agentmods.dev/badge/skills/openminis/minisskills/whenpeak/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/openminis/minisskills/whenpeak"><img src="https://agentmods.dev/badge/skills/openminis/minisskills/whenpeak.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Data Exfiltration · line 34 Code or instructions that leak agent conversation context to external services, potentially exposing sensitive user interactions.Fix: Remove any code that sends prompts, responses, or session data externally. Preserve user privacy; never exfiltrate conversation content.
- medium Data Exfiltration · line 102 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00111 | $0.02231 |
| Opus 5.5 | $0.00044 | $0.00892 |
| Sonnet 5.5 | $0.00022 | $0.00446 |
| Haiku 4.5 | $0.00011 | $0.00223 |
Grade A, and why
whenpeak 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 1mo 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WhenPeak — performance timing from sleep
WhenPeak predicts a 24-hour cognitive performance curve from sleep data: when the user peaks, when they dip, and how strong the day will be. The product's value is timing — the peak windows and the dip — not the score. Lead every answer with timing.
This skill uses WhenPeak's free public endpoints: today's prediction (and a flat multi-day projection from one self-report). No API key or account. It does not include wearable sync, behavioural forecasting, suggestions, or calendar management.
Three hard rules — read these first
-
Get consent before the first API call. Predictions are generated by an external service. Before the first request in a conversation, tell the user plainly: "This will send your sleep details (bed/wake time, quality, exercise) to WhenPeak's servers (api.whenpeak.com) to generate the prediction. OK to proceed?" Only call the API after they confirm. Ask once per conversation, not before every call. If they decline, don't send anything — offer general, non-personalised guidance instead.
-
Never fabricate a prediction. Every number comes from the API via the bundled script. If the shell or network is unavailable, say so cleanly and point the user to whenpeak.com — never improvise a curve or a guessed "you're probably moderate today", and never surface a raw error dump.
-
Send optional fields omitted, never as
null.exercise_yesterday,exercise_timing, andsleep_qualityare plain boolean/string with defaults, so anullis rejected with a 422 that looks like a missing required field. Leave unknown fields out of the JSON entirely. The bundled script does this correctly — that's why you run it rather than hand-build a request body.
Workflow
1. Collect last night's sleep
Prefer real data over asking. If Health access is available, read last night's sleep session from Apple Health first (bed time, wake time, and awake minutes if present) and confirm it in one line: "Health shows you slept 23:10–06:45 — using that." Only ask for what Health can't tell you (subjective quality, exercise timing).
What ships with it
7 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.
- 1mo ago First seen · 135 lines · 111 tokens per session scan A 5846c4c4231d
whenpeak is a skill published in the GitHub repository OpenMinis/MinisSkills (502 stars, last pushed 2d ago), licensed MIT. It adds 111 tokens to every session and 2,231 once invoked, about $0.0004 per session on Opus 5.5. 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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