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 sushrutb17/doop-showcase --skill doop-traininggit clone --depth 1 https://github.com/sushrutb17/doop-showcaseWrote 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/sushrutb17/doop-showcase/doop-training)<a href="https://agentmods.dev/skills/sushrutb17/doop-showcase/doop-training"><img src="https://agentmods.dev/badge/skills/sushrutb17/doop-showcase/doop-training/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/sushrutb17/doop-showcase/doop-training"><img src="https://agentmods.dev/badge/skills/sushrutb17/doop-showcase/doop-training.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 | $0.00029 | $0.00409 |
| Opus 5 | $0.00015 | $0.00204 |
| Sonnet 5 | $0.00006 | $0.00082 |
| Haiku 4.5 | $0.00003 | $0.00041 |
Grade A, and why
doop-training 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 10d 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.
What it actually says
Doop training workflow
Use this skill whenever the owner asks what to do, mentions heading to the gym, asks about recovery or recent performance, or requests a weekly review.
Today
- Resolve the current local calendar date from the configured timezone.
- Call
get_telegram_training_guidance(date). - Return its
textverbatim. Its completion and recovery state are already canonical and already includes the completion state, recovery confidence, recommendation, and safety override. Do not make a second call, summarize, reformat, reinterpret, or add a personality line. When applicable, that canonical text says “planned; not yet logged”; never substitute “confirmed,” “completed,” or “done.” The word “confirmed” is forbidden unless a separate confirmed-action result exists.
Pre-workout
- Call
get_telegram_training_guidance(date)exactly once. - Return its
textverbatim; it already contains today's exact prescription. - Do not append questions or unplanned work. The canonical relay already contains the safety override.
History or progression
Detailed history/progression is not exposed in the Stage 1 Telegram surface. Say that this view remains in the local Doop Training dashboard; do not infer history from chat memory or substitute a different tool.
Weekly review
- Determine the Monday for the requested week.
- Call
get_telegram_weekly_review(week_start). - Return its
textverbatim, including the missing-log caveat. - Do not make a second tool call or add any conclusion.
Read-only boundary
Do not claim to log, confirm, cancel, accept, or reject anything. In this stage, direct the owner to the local Doop Training dashboard for confirmed actions.
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.
- 10d ago First seen · 47 lines · 29 tokens per session scan A 7b041bcdccbf
doop-training is a skill published in the GitHub repository sushrutb17/doop-showcase (0 stars, last pushed 9d ago), licensed Apache-2.0. It adds 29 tokens to every session and 409 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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