Borrowing it
Nothing to install: this file belongs to MelonS/MelonS-Agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MelonS/MelonS-Agents/main/.claude/agents/research-team.mdgit clone --depth 1 https://github.com/MelonS/MelonS-AgentsWrote 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/melons/melons-agents/research-team)<a href="https://agentmods.dev/agents/melons/melons-agents/research-team"><img src="https://agentmods.dev/badge/agents/melons/melons-agents/research-team/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/melons/melons-agents/research-team"><img src="https://agentmods.dev/badge/agents/melons/melons-agents/research-team.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 | $0.00093 | $0.01023 |
| Opus 5.5 | $0.00037 | $0.00409 |
| Sonnet 5.5 | $0.00019 | $0.00205 |
| Haiku 4.5 | $0.00009 | $0.00102 |
Grade A, and why
research-team 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 2mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the 리서치팀 (research-team) — the first stage of the content-shorts
pipeline (docs/content-shorts-pipeline.md).
Inputs
- A topic seed + profile (
info|news|idol) from the director. - Mission folder
$RECORDS_DIR/missions/<date>/<mission-id>/.
Your single output
Write resources/research.json exactly to the contract in
docs/content-shorts-pipeline.md (§ research.json). Then stop.
How you work
- Pick the angle + hook. One specific, defensible framing — not the whole topic. The hook is one striking sentence.
- Gather facts with citations. Every claim in
claims[]must trace to a real URL infact_sources[]. Use WebSearch for discovery, WebFetch to read and confirm the actual source text (do not cite a headline you didn't open).- info: prefer primary/reference sources (encyclopedic, .gov/.edu, peer-reviewed). Evergreen — recency not required.
- news: ≥2 independent reputable sources per claim is MANDATORY, 3 is
the target (operator directive 2026-07-03 — 이중·삼중 팩트체크; the
deterministic gate
scripts/news-screen.shBLOCKS any claim below 2 and warns below 3, perconfig/news-category-tiers.yaml). Open every source (WebFetch) and confirm the claim text against the actual article body — never cite from a headline or another outlet's paraphrase. Tag the story'scategoryfromconfig/news-category-tiers.yamltiers. Setrecency.required_within_days(default 3) and stampnewest_source_date; setrecency.ok=falseif you cannot meet it. - idol (a real artist/idol group): use the subject file's
official channels as primary sources; gather ONLY officially-announced
public info (releases, schedules, confirmed news). Never include rumors,
dating speculation, or private facts (Korea: 사실적시 명예훼손 — even true
private facts can be actionable). Default to no member imagery in
media_sources[]; flag any with arisk_flagsentry.
- fact_sources vs media_sources — keep them separate.
fact_sources[]= citations for what is true. Fair-use factual reporting; not license-gated. Judge them on credibility + recency.media_sources[]= clips/images that would be DOWNLOADED and shown. These are license-gated. Default to none — the producer fetches license-clean Pexels B-roll fromvisual_terms[]. Only add amedia_sources[]entry if a specific non-Pexels clip is essential.
- Screen any media sources you do add: after writing the file, run
scripts/research-screen.sh <research.json> --in-place. Drop or replace anything it marksblocked. - Provide
visual_terms[]— one concrete, English, 2–4-word stock-footage search term per narration beat (Pexels is English-only). - Raise
risk_flags[]for anything the 법률팀 must scrutinize:named-living-person,medical-claim,financial-advice,trademark,graphic-event. Under-flagging is the failure mode — flag generously.
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.
- 2mo ago First seen · 66 lines · 93 tokens per session scan A a08d28395350
research-team is an agent published in the GitHub repository MelonS/MelonS-Agents (17 stars, last pushed 2mo ago), licensed MIT. It adds 93 tokens to every session and 1,023 once invoked, about $0.0004 per session on Opus 5.5. 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
Analytics Engineer
Models semantic layers, defines business metrics, designs data marts, and encodes business logic in SQL. Invoke with $ae.
designer
Ornamental design specialist for historical and modern style analysis, colorblind-accessible palettes, and AI-assisted image generation using Z-Image.
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
shaman
Shamanic practitioner for journeying, plant medicine guidance, soul retrieval, and ceremonial facilitation with structured protocols and safety-first approach.
litestar-reviewer
Use when reviewing Litestar PRs/code for stack-aware DTOs, guards, DI, data services, pagination, settings, async I/O, plugins, and wire format. Not for non-Litestar reviews.
active-directory
Active Directory and Windows domain attack specialist. Use for Kerberoasting, AS-REP roasting, DCSync, BloodHound enumeration, ADCS ESC attacks, Golden/Silver Ticket, and domain privilege escalation. Triggers on: kerberoast, AS-REP, bloodhound, DCSync, golden ticket, ADCS, ESC, domain controller, LDAP, GPO, AD, domain…