A scheduling helper that predicts when a person's mental performance may peak or dip during the day from sleep information. It uses the WhenPeak service to produce timing guidance, not calendar management.

Use it when deciding when to schedule a meeting, interview, exam, presentation, or focused work session based on predicted alertness.

It helps replace guesswork about when someone may be most alert or focused with a sleep-based estimate. It can also indicate when the day may be generally stronger or weaker.

whenpeak — Skill for Claude Code and Codex by OpenMinis: A scheduling helper that predicts when a person's mental…

Install

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.

agentmods

npx agentmods add skills/openminis/minisskills/whenpeak

Any agent

npx skills add OpenMinis/MinisSkills --skill whenpeak

Clone the repo

git clone --depth 1 https://github.com/OpenMinis/MinisSkills

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/openminis/minisskills/whenpeak.svg)](https://agentmods.dev/skills/openminis/minisskills/whenpeak)

Your own site

<a href="https://agentmods.dev/skills/openminis/minisskills/whenpeak"><img src="https://agentmods.dev/badge/skills/openminis/minisskills/whenpeak.svg" alt="Measured on agentmods" height="20"></a>

Per session 111 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,231 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00111	$0.02231
Opus 5	$0.00056	$0.01115
Sonnet 5	$0.00022	$0.00446
Haiku 4.5	$0.00011	$0.00223

Measured 5d ago against content hash 5846c4c4231d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 5d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/whenpeak_chart.py, scripts/whenpeak_predict.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

whenpeak/SKILL.md · 135 lines

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, and sleep_quality are plain boolean/string with defaults, so a null is 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).

Read the full file on GitHub · 135 lines

Files

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.

Changes

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.

5d ago First seen · 135 lines · 111 tokens per session scan A 5846c4c4231d

Subscribe to this mod's changes

whenpeak is a skill published in the GitHub repository OpenMinis/MinisSkills (397 stars, last pushed yesterday), licensed MIT. It adds 111 tokens to every session and 2,231 once invoked, about $0.0006 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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whenpeak