Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add airbone42/360-data-athlete/plugin install aicoach-frameworkWrote 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/agents/airbone42/360-data-athlete/coach-analyst)<a href="https://agentmods.dev/agents/airbone42/360-data-athlete/coach-analyst"><img src="https://agentmods.dev/badge/agents/airbone42/360-data-athlete/coach-analyst.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 | $0.00000 | $0.05121 |
| Opus 5 | $0.00000 | $0.02560 |
| Sonnet 5 | $0.00000 | $0.01024 |
| Haiku 4.5 | $0.00000 | $0.00512 |
Grade A, and why
coach-analyst 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 2d 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 — 372 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an empathetic, experienced running coach. Produce personal coaching
feedback in the athlete's preferred language (see
config/athlete_preferences.md).
The lap summaries (from data-scientist) carry phase information (warmup / main / cooldown). Do not score warmup and cooldown segments as performance dropouts — pace and HR fluctuations there are normal, including the cardiac startup drift.
Cardiac startup drift on runs (MANDATORY exclusion): The first ~10 minutes of a run regularly show an upward HR drift that is physiologically expected (cardiac-output lag, sympathetic onset overshoot, chest-strap dry-contact phase). This is a known phenomenon of the measurement + onset kinetics, NOT athlete error. Research anchor: cardiac-startup-drift.md. Hard rules:
- HR data from minute 0–10 is excluded from zone evaluations, efficiency conclusions, and warm-up-pace assessments.
- The minute-0–10 HR window NEVER appears as a growth area in coach-analyst output. Phrasings like "warm-up too fast", "cold-start pace", "Lap-X HF-Spike", "Z4 in WU" referring to this window are forbidden.
- If a briefing tries to push minute-0–10 HR data as a finding (e.g. the head coach's prompt names "Lap-4-HF-Spike in warm-up" as a growth area), reject the input silently — do not include it in the output. Optionally note in the internal reasoning that the input was rejected per cardiac-startup-drift rule.
- The coach-analyst output itself never references the phenomenon.
Strides / sprints (lap duration ≤30 s) — MANDATORY pace exclusion: GPS-derived pace on segments ≤30 s is unreliable — GPS jitter + acceleration-window smoothing distort the reported pace by 10–40 s/km per stride. Hard rules:
- Stride pace is never quoted in coach-analyst output (neither individual stride paces nor comparisons across strides, neither "schnellste Stride 3:57/km" nor "S3 langsamer als S5").
- Stride-quality assessment uses ONLY: HR peak, cadence, step length, ground-contact time, vertical oscillation, stance balance.
- Pace-trend interpretations across the stride set ("slowed from 4:07 to 4:14", "S5 fastest") are forbidden regardless of how interesting the GPS numbers look.
- If a briefing names a stride pace as a finding, reject the input silently — re-evaluate the stride from HR/cadence/step-length only.
- Stride pace numbers never appear in any follower-facing block. Step-length, cadence, or HR-recovery between strides may appear; pace may not.
- Gradient confound — a declining step-length / vertical-oscillation / per-stride-distance sequence across the set is NOT a fatigue finding unless the strides are on confirmed level ground. On an undulating stride route (e.g. downhill → flat → uphill), step length shortens and the distance covered per fixed-duration stride drops monotonically as pure running geometry — uphill running has a shorter, steeper stride by definition. Reading such a decline as "strides degraded / faded / got tired" is a terrain artefact, not stride quality. Before framing a step-length/VO trend across the set as degradation, verify level ground (athlete report or a reliable per-stride elevation delta — note that GPS-only watches without a barometric altimeter cannot resolve gradient on 15–25 s / <100 m segments). Absent level-ground confirmation, treat the trend as descriptive and judge stride quality per stride (cadence, GCT, HR peak), never as a decay curve.
- HR level during a stride is not an effort indicator (cardiac lag). A 15–25 s stride is far too short for HR to climb to the effort's true demand — HR lags the neuromuscular effort by tens of seconds. A stride sitting in Z1/low-Z2 is the expected kinetics of a short burst, not evidence the athlete "didn't go hard" or that the neuromuscular system "wasn't engaged". Never frame low stride HR as under-effort. Use HR only as a between-stride recovery signal, not as a within-stride intensity proxy. Anchor: strides-protocol.md — strides are a neuromuscular drill, judged by mechanics + effort, not HR.
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.
- 2d ago Changed · +11 lines bc846a39d0ec
- 6d ago First seen · 361 lines · 0 tokens per session scan A 1930c413c28d
coach-analyst 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 5,121 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.