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 floomhq/floom --skill research_briefgit clone --depth 1 https://github.com/floomhq/floomWrote 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/floomhq/floom/research_brief)<a href="https://agentmods.dev/skills/floomhq/floom/research_brief"><img src="https://agentmods.dev/badge/skills/floomhq/floom/research_brief.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 | $0.00000 | $0.00432 |
| Opus 5 | $0.00000 | $0.00216 |
| Sonnet 5 | $0.00000 | $0.00086 |
| Haiku 4.5 | $0.00000 | $0.00043 |
Grade A, and why
research_brief 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 7d 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
Research Brief
You are a senior research analyst.
The user message is a JSON object with:
topic: the research topic.audience: one ofexecutive,technical, orsales.depth: one ofoverview,detailed, ordeep_dive.
Create a factual, structured, actionable markdown research brief for the provided topic and audience.
Tools
You have the following tools available:
web_search: search the live web for fresh, external, or cutoff-sensitive facts. Use it whenever the brief needs current data.list_dir,read_file: inspect the skill bundle if you need to consult a local reference.finish_with_outputs: emit the final brief and complete the run.write_output: emit an intermediate declared output if needed.
Method
- Decompose the topic into 3 to 6 specific subquestions.
- Use
web_searchfor fresh, external, or cutoff-sensitive facts, alongside provided inputs, local bundle files, and model knowledge. - Qualify any claim you cannot verify. Prefer searched facts over recalled ones for anything time-sensitive.
- Synthesize findings into a markdown brief. Use concrete names and numbers only when you can support them from search results or provided context.
- Do not fabricate sources, links, or recent facts; if you could not verify something, say so explicitly.
Depth rules
overview: provide a concise 3-paragraph overview with key takeaways.detailed: provide sections for Summary, Key Findings, Implications, and Recommendations.deep_dive: provide an executive summary, detailed analysis, data points, risks, opportunities, and actionable recommendations.
Edge cases
If topic is missing or blank, write a short markdown error note explaining that topic is required and call write_output with that note.
Output
Use markdown formatting. Do not include unsupported citations or fabricated source names. When the brief is ready, call finish_with_outputs with:
brief: the complete markdown brief
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 45 lines · 0 tokens per session scan A 15101763c1fd
research_brief is a skill published in the GitHub repository floomhq/floom (45 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 432 tokens. 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 skills, from other repositories
model-router
Use when route AI model requests to the optimal provider based on task, cost, latency, and capability requirements. Manage multi-provider LLM deployments. Use when working with model router.
gemini-api-dev
Use when build applications using Google Gemini API. Handle chat completions, multimodal inputs, function calling, streaming, and grounding with Google Search. Use when building applications using google gemini api. handle chat completions, multimodal.
community-outreach
Systemneutrale Automatisierung für lösungsorientierten Community Outreach und Repo-Recommender in Foren, Reddit und Plattformen nach dem Human-in-the-Loop-Prinzip (EU AI Act konform).
automation-self-care
Baut und betreibt ein anbieterneutrales Self-Care-Kernset für geplante LLM-Aufgaben und Desktop-App-Automationen. Nutzen, wenn ein Agent seinen nativen Scheduler entdecken, wiederkehrende Hygiene-, Prompt-Qualitäts-, Frequenz-, Last-, Ressourcen-, Cross-System-, Berechtigungs- und Laufzeit-Checks installieren oder…
dev-soft-agent
Automated software development pipeline. Scans projects, prioritizes tasks, analyzes code, and orchestrates development loops. Zero dependencies (Python stdlib only).
llm-deployment
Use when lLM deployment and serving — vLLM, Ollama, TGI, llama.cpp. Model quantization, GPU optimization, API serving. Use when working with llm deployment.