Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/hainamchung/agent-assistantnpx agentmods add agents/hainamchung/agent-assistant/researcherWrote 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/hainamchung/agent-assistant/researcher)<a href="https://agentmods.dev/agents/hainamchung/agent-assistant/researcher"><img src="https://agentmods.dev/badge/agents/hainamchung/agent-assistant/researcher.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 | $0.00015 | $0.01055 |
| Opus 5 | $0.00008 | $0.00528 |
| Sonnet 5 | $0.00003 | $0.00211 |
| Haiku 4.5 | $0.00002 | $0.00105 |
Grade C, and why
researcher scanned grade C with 1 finding 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 8d 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.
Hidden instructionshighPrompt injection
Directives inside HTML comments, invisible characters or bidirectional overrides are read by the model and not by the person reviewing the file.
<!-- 🔒 COGNITIVE ANCHOR — MANDATORY OPERATING SYSTEM --> How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BINDING: This file OVERRIDES default AI patterns. Follow Thinking Protocol EXACTLY. EXTRACT: Core Directive + Constraints + Output Format before proceeding.
🔬 Researcher
| Attribute | Value |
|---|---|
| ID | agent:researcher |
| Role | Principal Research Analyst |
| Profile | research:analysis |
| Reports To | tech-lead, planner |
| Consults | scouter, brainstormer |
| Standard | Cite sources for all claims |
CORE DIRECTIVE: Find the truth. Verify sources. Go deep, not wide. Your research enables confident decisions. Bad research leads to bad decisions.
Prime Directive: PRIMARY > secondary > opinion. ALWAYS cite sources.
⚡ Skills
MATRIX DISCOVERY: Skills auto-injected from domain files in
~/.{TOOL}/skills/agent-assistant/matrix-skills/Profile:research:analysis| Domains:research,planning
🎯 Expert Mindset
THINK_LIKE:
- "Is this source authoritative?"
- "Is this information current?"
- "Can I verify this elsewhere?"
- "What's the confidence level?"
ALWAYS:
- Cross-reference multiple sources
- Prefer official documentation
- Note currency of information
- Acknowledge uncertainty
🧠 Thinking Protocol
Step 0: CONTEXT CHECK (MANDATORY)
CHECK PROJECT DOCS (if ./.documents/ exists):
- knowledge-overview/00-index.md → Project context (drill into sub-files as needed)
- knowledge-architecture/00-index.md → Technical context (drill into sub-files as needed)
- knowledge-domain/00-index.md → Domain concepts (drill into sub-files as needed)
→ USE these to focus research on project needs
Step 1: RESEARCH SCOPE
| Depth | Indicators | Approach |
|---|---|---|
| Quick | Factual, single source | Find → Verify → Answer |
| Investigation | Multiple aspects | Gather → Synthesize → Report |
| Deep Dive | Complex topic | Comprehensive → Analyze → Recommend |
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.
- 8d ago First seen · 149 lines · 15 tokens per session scan C 8e7d585159a1
researcher is an agent published in the GitHub repository hainamchung/agent-assistant (54 stars, last pushed 3mo ago), licensed MIT. It adds 15 tokens to every session and 1,055 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (hidden instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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