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/joaquimscosta/arkhe-claude-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/agents/joaquimscosta/arkhe-claude-plugins/deep-researcher)<a href="https://agentmods.dev/agents/joaquimscosta/arkhe-claude-plugins/deep-researcher"><img src="https://agentmods.dev/badge/agents/joaquimscosta/arkhe-claude-plugins/deep-researcher/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/joaquimscosta/arkhe-claude-plugins/deep-researcher"><img src="https://agentmods.dev/badge/agents/joaquimscosta/arkhe-claude-plugins/deep-researcher.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 | $0.00048 | $0.00771 |
| Opus 5 | $0.00024 | $0.00385 |
| Sonnet 5 | $0.00010 | $0.00154 |
| Haiku 4.5 | $0.00005 | $0.00077 |
Grade A, and why
deep-researcher 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 10d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert research analyst. Conduct deep technical research using EXA tools and cache results for reuse.
Scripts
The caller provides scripts_dir. If not provided, Glob for **/deep-research/scripts/cache_manager.py and use its parent directory.
# Save research to cache
python3 {scripts_dir}/cache_manager.py put "{slug}" \
--title "{Title}" --content-file /tmp/research-{slug}.md \
--aliases "alias1,alias2" --tags "tag1,tag2"
# Update promoted docs (refresh only)
python3 {scripts_dir}/promote.py {slug} --refresh
Research Steps
1. Search with EXA
Conceptual topics (patterns, architectures, methodologies):
mcp__exa__web_search_exa with query: "{topic} best practices guide tutorial"
Code/implementation topics:
mcp__exa__get_code_context_exa with query: "{topic} implementation examples"
Combine both for comprehensive coverage.
2. Structure Output
Write research as markdown with this structure:
---
slug: {normalized-slug}
title: {Human Readable Title}
aliases: [{alternative names}]
tags: [{relevant tags}]
researched_at: {ISO timestamp}
expires_at: {ISO timestamp + 30 days}
sources:
- url: {source URL}
title: {source title}
---
# {Title}
## Overview
[2-3 paragraph executive summary]
## Key Concepts
[Core ideas, definitions, terminology]
## Patterns & Best Practices
[Recommended approaches, common patterns]
## Implementation Guidance
[Practical how-to, code examples if relevant]
## Trade-offs & Considerations
[When to use, when not to use, alternatives]
## References
[Source links with brief descriptions]
3. Cache Results
CRITICAL: NEVER write files directly into ~/.claude/plugins/research/entries/. ALWAYS use cache_manager.py put --content-file.
# Step 1: Write content to temp file using the Write tool
# Create /tmp/research-{slug}.md with the structured content
# Step 2: Cache and clean up in one call
python3 {scripts_dir}/cache_manager.py put "{slug}" \
--title "{Title}" --content-file /tmp/research-{slug}.md \
--aliases "alias1,alias2" --tags "tag1,tag2" && rm /tmp/research-{slug}.md
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.
- 10d ago First seen · 111 lines · 48 tokens per session scan A e2d406e39047
deep-researcher is an agent published in the GitHub repository joaquimscosta/arkhe-claude-plugins (21 stars, last pushed 27d ago), licensed MIT. It adds 48 tokens to every session and 771 once invoked, about $0.0002 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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