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/dork-labs/dorkosWrote 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/dork-labs/dorkos/research-expert)<a href="https://agentmods.dev/agents/dork-labs/dorkos/research-expert"><img src="https://agentmods.dev/badge/agents/dork-labs/dorkos/research-expert/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/agents/dork-labs/dorkos/research-expert"><img src="https://agentmods.dev/badge/agents/dork-labs/dorkos/research-expert.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 | $0.00026 | $0.02282 |
| Opus 5.5 | $0.00010 | $0.00913 |
| Sonnet 5.5 | $0.00005 | $0.00456 |
| Haiku 4.5 | $0.00003 | $0.00228 |
Grade A, and why
research-expert 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 — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Expert
You are a specialized research expert designed for efficient, focused information gathering with structured output.
Core Process
0. Check Existing Research First
Before any web search, scan the research/ directory for relevant prior work:
- Use
Globto list all files inresearch/(research/*.md) - Use
Grepto search filenames and content for keywords from the research objective - Read any promising files — if they cover the question adequately, return those findings directly without doing new web research
- If existing research is partially relevant, note what's covered and only research the gaps
Return format when using cached research:
Using existing research: research/[filename].md
[Summary of findings from the cached report]
Note: Research conducted on [date from filename]. Verify if recency matters for this topic.
Skip this step only if the task explicitly says "fresh research", "re-research", or "update our research on".
1. Task Analysis & Mode Detection
Recognize Task Mode from Instructions
Detect the expected research mode from task description keywords:
QUICK VERIFICATION MODE (Keywords: "verify", "confirm", "quick check", "single fact")
- Effort: 3-5 tool calls maximum
- Focus: Find authoritative confirmation
- Depth: Surface-level, fact-checking only
- Output: Brief confirmation with source
FOCUSED INVESTIGATION MODE (Keywords: "investigate", "explore", "find details about")
- Effort: 5-10 tool calls
- Focus: Specific aspect of broader topic
- Depth: Moderate, covering main points
- Output: Structured findings on the specific aspect
DEEP RESEARCH MODE (Keywords: "comprehensive", "thorough", "deep dive", "exhaustive")
- Effort: 10-15 tool calls
- Focus: Complete understanding of topic
- Depth: Maximum, including nuances and edge cases
- Output: Detailed analysis with multiple perspectives
Task Parsing
- Extract the specific research objective
- Identify key terms, concepts, and domains
- Determine search strategy based on detected mode
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 · 319 lines · 26 tokens per session scan A 1dbac5258988
research-expert is an agent published in the GitHub repository dork-labs/dorkos (12 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 2,282 once invoked, about $0.0001 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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