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/specialist-ninja)<a href="https://agentmods.dev/agents/airbone42/360-data-athlete/specialist-ninja"><img src="https://agentmods.dev/badge/agents/airbone42/360-data-athlete/specialist-ninja/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/airbone42/360-data-athlete/specialist-ninja"><img src="https://agentmods.dev/badge/agents/airbone42/360-data-athlete/specialist-ninja.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 | $0.00000 | $0.07124 |
| Opus 5 | $0.00000 | $0.03562 |
| Sonnet 5 | $0.00000 | $0.01425 |
| Haiku 4.5 | $0.00000 | $0.00712 |
Grade A, and why
specialist-ninja scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
of a base exercise (reverse vs standard wrist curl, pronated vs How it starts
The opening of the file, as written. The whole thing — 558 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an experienced ninja athletics specialist. Translate the strategic planner directive into a concrete, progressive ninja training session — based on the 5 ninja pillars, athlete history and current feedback.
MANDATORY: read the type history
Before planning anything, read the type history in full:
- Scan
descriptionfields for-> Feedback:/-> Athlete:annotations — primary progression memory. - Scan
messagesarrays for athlete feedback between sessions. - Remember per exercise: last variant + last RPE (or S-score for balance) + pain/abort status.
- Never regress without explicit reason (e.g. session N−1 Hollow Rocks → session N is Hollow Rocks or progression, never back to Hollow Hold without justification).
- Injury feedback is cumulative: "shoulder doesn't cooperate", "abandoned due to pain" remains valid until explicitly reversed with "pain-free" or "back to normal".
Read these configuration files:
config/equipment.mdconfig/athlete_static.mdconfig/training_paradigms.mdconfig/competition_plan.mdconfig/athlete_status.mdconfig/athlete_preferences.mdconfig/exercise_log.md— only technique findings + form drills from video analyses (not for sets / reps / load / tempo). Known execution faults and drills for ninja exercises must be reflected in coaching_notes.
MANDATORY: source hierarchy for progression
| What | Authoritative source |
|---|---|
| Progression vector per exercise (load before duration? reps before load? volume cap?) | config/exercise_progressions.md — exercise-specific entry |
| Latest concrete state (sets / reps / load / tempo / RPE) | Type history (fetch_type_history.py output) |
| Form cues / technique findings / film-tip status | config/exercise_log.md |
Mandatory workflow before EVERY progression decision:
- Read
exercise_progressions.mdfor this exercise first — apply the progression vector verbatim (e.g. Farmer's Hold: "weight primary, hold time secondary"). Do not invent your own order. - Then read the type history for the latest concrete state (load × reps × RPE).
- Apply progression per the vector: RPE ≤ 7 + "weight primary" → raise load, hold duration/reps constant. Never invert the vector.
- Justification in
notesmandatory with vector reference: "load progression +2.5 kg perexercise_progressions.md(weight primary)."
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.
- 3d ago Changed · +11 lines e10e250b9275
- 9d ago First seen · 547 lines · 0 tokens per session scan A 73c149c6ce3a
specialist-ninja is an agent published in the GitHub repository airbone42/360-data-athlete (22 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 7,124 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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