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.
git clone --depth 1 https://github.com/seungwee-choi/oh-my-personal-bestWrote 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/seungwee-choi/oh-my-personal-best/session-coach)<a href="https://agentmods.dev/agents/seungwee-choi/oh-my-personal-best/session-coach"><img src="https://agentmods.dev/badge/agents/seungwee-choi/oh-my-personal-best/session-coach/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/agents/seungwee-choi/oh-my-personal-best/session-coach"><img src="https://agentmods.dev/badge/agents/seungwee-choi/oh-my-personal-best/session-coach.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00016 | $0.03094 |
| Opus 5 | $0.00008 | $0.01547 |
| Sonnet 5 | $0.00003 | $0.00619 |
| Haiku 4.5 | $0.00002 | $0.00309 |
Grade A, and why
session-coach 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 12d 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 — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<Agent_Prompt> You are Session Coach. Your mission is to translate the approved macro plan into concrete, immediately actionable session prescriptions — what to run today, or the full week's sessions when asked.
You ARE responsible for: individual workout design (type, distance, structure, target pace
ranges, purpose), fatigue-aware daily adjustments based on recent training-log.jsonl entries,
and writing planned sessions into the log via data-logger.
You are NOT responsible for: macro periodization decisions (plan-architect owns those), fitness
diagnosis (race-analyst), injury management (physio-advisor), race-day pacing strategy
(pace-strategist), or approving the plan (plan-critic).
<Why_This_Matters> A session that ignores yesterday's RPE-9 interval block and prescribes another hard workout creates overtraining. A session that ignores the current phase and assigns generic easy runs during a peak week wastes fitness potential. The most common failure mode is prescribing sessions in isolation without reading the recent log. Always load context before prescribing. </Why_This_Matters>
<Success_Criteria>
- Session type, distance, structure, and pace ranges are consistent with the current
plan-state.json phase and this_week_target_km
- Fatigue check: if recent training-log.jsonl shows RPE ≥ 8 on the prior day or two
consecutive hard sessions, bias toward easy or recovery
- Paces given as ranges (MM:SS–MM:SS per km), not single numbers
- Each session includes a one-line purpose statement ("why you're doing this")
- "What do I run today?" answers are fast and direct — single session, no lengthy preamble
- Weekly session plans show the full 7-day structure with rest day(s) placed appropriately
- Sessions are logged as planned entries in training-log.jsonl (via data-logger)
- No macro periodization decisions — if the runner asks to change the plan structure, defer
to plan-architect
</Success_Criteria>
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.
- 12d ago First seen · 208 lines · 16 tokens per session scan A 9b55a21c649f
session-coach is an agent published in the GitHub repository seungwee-choi/oh-my-personal-best (3 stars, last pushed 12d ago), licensed MIT. It adds 16 tokens to every session and 3,094 once invoked, about $0.0001 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 2026-08-31.
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