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/webdevtodayjason/titanium-pluginsWrote 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/webdevtodayjason/titanium-plugins/bmad-research)<a href="https://agentmods.dev/commands/webdevtodayjason/titanium-plugins/bmad-research"><img src="https://agentmods.dev/badge/commands/webdevtodayjason/titanium-plugins/bmad-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/webdevtodayjason/titanium-plugins/bmad-research"><img src="https://agentmods.dev/badge/commands/webdevtodayjason/titanium-plugins/bmad-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 | $0.00006 | $0.03162 |
| Opus 5.5 | $0.00002 | $0.01265 |
| Sonnet 5.5 | $0.00001 | $0.00632 |
| Haiku 4.5 | $0.00001 | $0.00316 |
Grade A, and why
bmad-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 1mo 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 — 577 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BMAD Research - Generate Research Prompts
You are helping the user research technical decisions by generating comprehensive research prompts for web-based AI (ChatGPT, Claude web) which have web search capabilities.
Purpose
Generate structured research prompts that users can copy to ChatGPT/Claude web to research:
- API vendors and data sources
- Authentication providers
- Hosting platforms
- Payment processors
- Third-party integrations
- Technology stack options
Results are documented in structured templates and referenced during architecture generation.
When to Use
During BMAD workflow:
- After PRD mentions external APIs/vendors
- Before architecture generation
- When technical decisions need research
Standalone:
- Evaluating vendor options
- Comparing technologies
- Cost analysis
- Technical due diligence
Process
Step 1: Identify Research Topic
If user provided topic:
# User ran: /bmad:research "data vendors for precious metals"
- Topic = "data vendors for precious metals"
If no topic:
- Ask: "What do you need to research?"
- Show common topics:
Common research topics: 1. Data vendors/APIs 2. Hosting platforms (Railway, Vercel, GCP, etc.) 3. Authentication providers (Clerk, Auth0, custom, etc.) 4. Payment processors (Stripe, PayPal, etc.) 5. AI/ML options (OpenAI, Anthropic, self-hosted) 6. Database options 7. Other (specify) Topic:
Step 2: Gather Context from PRD
If PRD exists:
Read bmad-backlog/prd/prd.md
Extract relevant context:
- What features need this research?
- What are the constraints? (budget, performance)
- Any technical preferences mentioned?
If no PRD:
- Use topic only
- Generate generic research prompt
- Note: "Research will be more focused with a PRD"
Step 3: Generate Research Prompt
Create comprehensive prompt for web AI.
Topic slug: Convert topic to filename-safe string
topic_slug = topic.lower().replace(' ', '-').replace('/', '-')
# "data vendors for precious metals" → "data-vendors-for-precious-metals"
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.
- 1mo ago First seen · 577 lines · 6 tokens per session scan A 212adb332914
bmad-research is a command published in the GitHub repository webdevtodayjason/titanium-plugins (7 stars, last pushed 8mo ago), licensed MIT. It adds 6 tokens to every session and 3,162 once invoked, about $0.0000 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 2026-08-31.
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specify
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converge
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implement
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speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.