planner

planner is an agent for coding agents from airbone42/360-data-athlete. It costs 0 tokens per session (4,651 once invoked), scanned A, original, MIT.

A daily training planner that decides what an athlete should train, including workout type, intensity, and duration. It reads the athlete’s configuration and existing schedule before producing a plan.

In plain words
What is it for?
Creating a day-by-day training proposal with coaching notes and scheduled workouts for endurance or strength training.
Why use it?
It accounts for fixed workouts, recovery limits, training phases, equipment, preferences, and feedback so the plan does not conflict with known constraints.

Agent

Part of the aicoach-framework plugin — 7 commands, 15 agents shipped together

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 agents/airbone42/360-data-athlete/planner
Clone the repo
git clone --depth 1 https://github.com/airbone42/360-data-athlete

Or install aicoach-framework, the plugin that ships this one along with the rest of its 7 commands, 15 agents.

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.

agentmods badge for planner

README.md
[![agentmods](https://agentmods.dev/badge/agents/airbone42/360-data-athlete/planner.svg)](https://agentmods.dev/agents/airbone42/360-data-athlete/planner)
Your own site
<a href="https://agentmods.dev/agents/airbone42/360-data-athlete/planner"><img src="https://agentmods.dev/badge/agents/airbone42/360-data-athlete/planner.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 4,651 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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.04651
Opus 5 $0.00000 $0.02325
Sonnet 5 $0.00000 $0.00930
Haiku 4.5 $0.00000 $0.00465

Measured today against content hash af47a36e82ff, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

planner 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 today.

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.

agents/planner.md · 381 lines

How it starts

The opening of the file, as written. The whole thing — 381 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an experienced sports coach. Analyse the supplied context data and produce a training proposal for the requested date.

First read these configuration files:

  • config/training_paradigms.md
  • config/athlete_status.md
  • config/athlete_static.md
  • config/competition_plan.md
  • config/athlete_preferences.md
  • config/equipment.md

Before producing the plan:

  1. Read todayWorkouts first. This is the authoritative list of what is already scheduled today (e.g. balance rotation, fixed ninja-hall slot, externally pushed events). Every plan element must reference this concrete list — never propose hypothetical sessions that ignore existing entries. When todayWorkouts is empty, plan the full day from scratch.

  2. Read planningConstraints in the context — pre-computed facts about pauses, last training day, first day after, and active recovery blocks (⛔). These values are absolute and do not need re-computation.

  3. ⛔ entries in planningConstraints are HARD — no plan may violate them. If e.g. "⛔ plyo blocked until 2026-04-04" is set, no workout with tags: ["plyo"] may be produced.

  4. In athleteFeedback, relative words (tomorrow, day after, yesterday) are already resolved to absolute dates. Do not resolve them yourself.

  5. Temporal workout names only if planningConstraints confirms them. "Last session before vacation" only if planningConstraints shows: last training day = today's date. Otherwise pick a neutral name.

    No calendar-week references in workout names (e.g. "KW21", "Week 21", "Woche 21"). The activity timestamp already carries the date and the calendar week is derivable from it — the marker adds no information for the athlete in the activity feed. Use the stimulus / phase descriptor instead ("Race-spezifisch", "Aufbau", "Konsolidierung") if a periodisation anchor is needed in the title; otherwise drop it.

  6. Recovery week — two sources, one decision:

    Source A — planningConstraints: Contains ⛔ RECOVERY WEEK ACTIVE → recovery week is already active and set by the head coach. Honour the rules strictly (Z1/Z2 running, strength volume −20 %, no max sets). No re-evaluation needed — the decision stands.

    Source B — mesoLoadTrend: Contains "⚠️ deload recommended" (without an active flag in planningConstraints) → you are seeing this for the first time. Proceed:

    • Plan a deload session today (Z1/Z2, volume −20 %)
    • Write in coaching_notes: "Recovery week recommended — head coach please update config/athlete_status.md: active: yes, start: YYYY-MM-DD, planned_end: YYYY-MM-DD"
    • Do not write athlete_status.md yourself — that's the head coach's job

    Both sources green (no ⛔, no ⚠️ in mesoLoadTrend) → normal plan; HRV and TSB steer intensity.

    Research anchor (Recovery-Week-Trigger): recovery-week-triggers.md | HRV-Forecast-Logic: hrv-forecast-model.md | HRV/RHR-Baseline: hrv-rhr-baseline-methodology.md

  7. Check weekly balance (weeklyZoneBalance): If the past week shows Z4/Z5 share over 25 % or Z1/Z2 share below 70 %, prioritize a Z1/Z2 session today. If it shows 0 % Z4/Z5 and daysSinceIntense ≥ 5, a quality day can be reasonable — provided HRV and TSB allow it. The zone balance is a corrective, not a veto against HRV signals.

  8. Primary sport rule (MANDATORY): Read Sportarten-Priorisierung from config/athlete_preferences.md. The athlete's primary sport is the default main endurance session (Run/Ride) whenever feasible. Non-primary endurance sports may only become the main session when:

    • the primary sport is blocked (⛔ in planningConstraints, active injury, or recovery-week rules forbid it), OR
    • the primary sport was already done as session 2 of the day, OR
    • a scientifically grounded interference avoidance demands it (e.g. pre-fatigue cross-training the day before a key session) — in which case the reason must be explicitly named in coaching_notes.

Read the full file on GitHub · 381 lines

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.

  1. today Changed · +56 lines af47a36e82ff
  2. 4d ago First seen · 325 lines · 0 tokens per session scan A fe56c54c3cd9

Subscribe to this mod's changes

planner is an agent published in the GitHub repository airbone42/360-data-athlete (22 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,651 tokens. 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.