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.
git clone --depth 1 https://github.com/revaya-ai/revaya-aios-workspace-templateWrote 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/commands/revaya-ai/revaya-aios-workspace-template/research)<a href="https://agentmods.dev/commands/revaya-ai/revaya-aios-workspace-template/research"><img src="https://agentmods.dev/badge/commands/revaya-ai/revaya-aios-workspace-template/research/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/commands/revaya-ai/revaya-aios-workspace-template/research"><img src="https://agentmods.dev/badge/commands/revaya-ai/revaya-aios-workspace-template/research.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 | $0.00530 |
| Opus 5 | $0.00000 | $0.00265 |
| Sonnet 5 | $0.00000 | $0.00106 |
| Haiku 4.5 | $0.00000 | $0.00053 |
Grade A, and why
research 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 11d 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research
Deep research agent. Searches the web thoroughly on any topic and returns a structured brief. Use before drafting content, building lead magnets, or making strategic decisions.
Variables
topic: $ARGUMENTS (the topic to research — e.g., "AI anxiety in small businesses", "Claude Code for solopreneurs")
Instructions
You are running a deep research session on the provided topic. Dispatch a research subagent to search broadly and return a structured brief you can use to inform content creation, strategy, or product decisions.
Step 1: Check for existing research
Before running new research, check outputs/content-engine/research/ for any existing briefs on this topic. If a relevant brief exists from the last 14 days, summarize what's already known and ask you if she wants a fresh search or to build on what exists.
Step 2: Dispatch research subagent
Use the Agent tool (general-purpose) to search the web thoroughly. Instruct the agent to:
- Search for the latest articles, expert opinions, data, and examples on the topic
- Look for what's working (examples with evidence), what's trending, and what's changing
- Find specific statistics, named examples, and concrete data points — not vague summaries
- Look across: industry publications, creator content, business publications, and practitioner takes
- Depth: aim for 8-12 high-quality sources minimum
Step 3: Compile the brief
Format the output as a structured research brief:
# Research Brief: [Topic]
**Date:** YYYY-MM-DD
**Query:** [exact topic searched]
## Key Findings
[3-5 most important things to know — specific, not generic]
## Data & Stats
[Specific numbers, percentages, study findings — with sources]
## Expert Takes
[Quotes or perspectives from credible voices — named, not anonymous]
## Examples Worth Noting
[Specific examples — named companies, named people, real outcomes]
## What's Changing
[Trends, shifts, what's new vs. what's established]
## Implications for your Content
[3-5 specific angles or hooks this research enables]
## Suggested Content Angles
[Ranked list of content ideas this research supports, with format recommendation]
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.
- 11d ago First seen · 69 lines · 0 tokens per session scan A 70f57320bbaa
research is a command published in the GitHub repository revaya-ai/revaya-aios-workspace-template (2 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 530 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 2026-08-31.
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