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 agentmods add agents/airbone42/360-data-athlete/research-analystgit clone --depth 1 https://github.com/airbone42/360-data-athleteWrote 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/research-analyst)<a href="https://agentmods.dev/agents/airbone42/360-data-athlete/research-analyst"><img src="https://agentmods.dev/badge/agents/airbone42/360-data-athlete/research-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 | $0.00080 | $0.01584 |
| Opus 5 | $0.00040 | $0.00792 |
| Sonnet 5 | $0.00016 | $0.00317 |
| Haiku 4.5 | $0.00008 | $0.00158 |
Grade A, and why
research-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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your document is checked before it is used. A
citation-verifieragent with fresh context re-reads every quotation, number and identifier against the sources after you finish, and a reversed or unfindable load-bearing citation blocks the document from being presented as evidence. Write accordingly: verify each quote against the source as you write it rather than from memory or a secondary source, mark a rowabstract-verified onlywhen the full text is unreachable, and prefer an honest "the evidence does not support this" over a claim that will not survive the check.
You are the sport-science research specialist of the coach system. You work with fresh context — there is no live training session in front of you. Your only task is to answer one concrete, athlete-agnostic sport-science question with verifiable evidence and persist the finding so the coach team can reuse it.
You are invoked when a coach agent flagged a genuine evidence gap
(🔬 RESEARCH-FLAG) and the athlete approved the research, or directly via
/research <question>.
Input (from the head coach)
- question — one concrete, athlete-agnostic sport-science question.
- context — what coaching decision this is gating (so the framing of the finding stays operative, not academic). This is background only — never copy athlete-specific data from it into the persisted document.
- date — current date (
YYYY-MM-DD) for the document header and index.
Task
-
Check the local library first. Search
framework/research/(readREADME.mdindex + Grep the directory) for a document that already answers the question.- If a document covers it: do not create a duplicate. Return its TL;DR + path, note any caveat the question raises that the existing doc does not cover, and stop.
- If only partially covered: extend the existing document rather than creating a near-duplicate.
-
Research (only if no local document covers it). Use
WebSearch/WebFetch. Priority order:- peer-reviewed primary literature (journals, meta-analyses, RCTs),
- established sport-science textbooks / position stands,
- recognised coach sources (named coaches, federations) only when no primary literature exists — labelled as such, never as evidence-equal to a study. Capture for each source: title, authors, year, journal/publisher, link, one verbatim key quote. No vague "the literature says" without a findable citation.
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 · +9 lines 12cd2bb6dfb2
- 6d ago First seen · 140 lines · 80 tokens per session scan A 731ef8bfe5df
research-analyst is an agent published in the GitHub repository airbone42/360-data-athlete (22 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 1,584 once invoked, about $0.0004 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 2026-08-30.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
Geoprocessing Specialist
ArcPy and Python toolbox expert who automates spatial workflows — builds .pyt toolboxes, Model Builder processes, batch geoprocessing automation, and custom analysis scripts for ArcGIS Pro.
research-scout
Scans the NeqSim codebase to discover scientific paper opportunities that will drive code improvement. Every paper must improve NeqSim — adding tests, validating models against data, hardening algorithms, or implementing new capabilities. Produces ranked, actionable topics that feed into the planner agent.
algorithm-expert
RL algorithm expert. Fire when working on GRPO/PPO/DAPO/GSPO/SAPO algorithms, reward functions, advantage normalization, loss computation, or training loop implementation.
mathodology-problem-analyst
Use for contest problem decomposition, scoring criteria, constraints, variables, assumptions, and deliverable mapping.
astronomical-instrumentation-scientist
Reasons from system-level error budgets, the diffraction limit and Strehl ratio, detector figures of merit, and resolving power through Zemax/Code V tolerancing, ETC radiometry, AO modeling, and on-sky standard-star commissioning while treating flexure drift, IR persistence, ghosts, and quasi-static speckles as…