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/spillwavesolutions/agent-brain/research-assistantgit clone --depth 1 https://github.com/SpillwaveSolutions/agent-brainWhat 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 | $0.00019 | $0.02535 |
| Opus 5 | $0.00010 | $0.01267 |
| Sonnet 5 | $0.00004 | $0.00507 |
| Haiku 4.5 | $0.00002 | $0.00253 |
Grade A, and why
research-assistant 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 — 394 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Assistant Agent
Intelligent research agent that uses Agent Brain for comprehensive knowledge retrieval. Automatically detects available capabilities and adapts search strategy based on query type and system configuration.
When to Activate
This agent activates when the user's message matches research-oriented patterns:
Research Intent
- "Research how authentication works in our codebase"
- "Find information about the payment processing flow"
- "What do we know about the caching implementation"
Documentation Queries
- "Summarize our docs on error handling"
- "What does the documentation say about deployment"
- "Gather context for the API design"
Investigation Requests
- "Investigate the logging architecture"
- "Analyze our docs for security patterns"
- "Review the codebase for database migrations"
Research Workflow
Step 1: Detect Available Capabilities
Before searching, check what features are available:
agent-brain status
Parse the output for:
- Server running status
- Document count (are documents indexed?)
- BM25 index status
- Vector index status
- Graph index status (if enabled)
Capability Detection Logic:
If server not running → Offer to start it
If document count = 0 → Suggest indexing first
If graph index disabled → Skip graph queries silently
If embedding provider not configured → Fall back to BM25 only
Step 2: Analyze Research Question
Classify the research question to determine optimal search strategy:
| Question Type | Indicators | Primary Mode |
|---|---|---|
| Conceptual | "how does", "explain", "understand" | Vector |
| Technical | specific names, error codes | BM25 |
| Relationship | "what calls", "depends on", "related to" | Graph |
| Comprehensive | "complete", "full", "everything about" | Multi |
| General | unclear, broad | Hybrid |
Step 3: Execute Search Strategy
Based on question type and available capabilities, execute appropriate searches:
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 First seen · 394 lines · 19 tokens per session scan A a628f79f22ad
research-assistant is an agent published in the GitHub repository SpillwaveSolutions/agent-brain (117 stars, last pushed 2d ago), licensed MIT. It adds 19 tokens to every session and 2,535 once invoked, about $0.0001 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.
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