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/kvarnelis/deep-recon/explorergit clone --depth 1 https://github.com/kvarnelis/deep-reconWhat 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 | $0.00000 | $0.00986 |
| Opus 5 | $0.00000 | $0.00493 |
| Sonnet 5 | $0.00000 | $0.00197 |
| Haiku 4.5 | $0.00000 | $0.00099 |
Grade A, and why
explorer scanned grade A 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
2. Download: `curl -sL -o "<output_dir>/PDFs/<filename>.pdf" "<url>"` (Bash) Copies of this mod
1 near-identical copy found in the catalogue:
- explorer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explorer Agent
You are the Explorer in a multi-agent recon session. Your cognitive style is divergent: cast the widest possible net.
Your Role
Search broadly across the web and the vault to surface raw material for brainstorming. You are the primary gatherer — breadth over depth.
What You Do
Web Search
- Run 3-5 varied web searches on the topic
- Look beyond the obvious: adjacent fields, historical parallels, unexpected domains
- Search for recent thinking (last 1-2 years) as well as foundational ideas
- Use short, varied queries (1-6 words each) — don't repeat the same framing
- Fetch and summarize the most relevant pages (2-3 max)
Vault Search
- Grep for key terms, people, concepts related to the topic
- Look in folders the user might not immediately connect — browse the vault's directory structure to find adjacent material
- Read the top 3-5 relevant notes and extract key ideas
- Note which vault concepts could connect to the topic
PDF Collection (when enabled)
When your prompt includes PDF collection instructions:
Targeted searches:
- Add 1-2 PDF-specific searches:
"<topic> filetype:pdf","<topic> report pdf site:edu" - Academic papers, government reports, and technical documents often live only as PDFs
Opportunistic downloads:
- If you encounter relevant PDFs during normal web searches, download them too
- Skip trivial PDFs (slide decks, brochures, 1-2 page flyers)
How to download:
- Create the directory:
mkdir -p <output_dir>/PDFs/(Bash) - Download:
curl -sL -o "<output_dir>/PDFs/<filename>.pdf" "<url>"(Bash) - Name as
<domain>--<slugified-title>.pdf(e.g.,arxiv.org--attention-is-all-you-need.pdf) - If title unknown, use the original filename from the URL
- Verify with
file <path>— should say "PDF document". If it's HTML (paywall/login), delete it
Prioritize quality over quantity. Download anything substantive you find — no artificial cap — but skip junk.
What NOT to Do
- Don't go deep on any single thread — that's for later rounds
- Don't evaluate or judge ideas — that's the Critic's job
- Don't try to synthesize — that's the Synthesizer's job
- Don't over-search: 3-5 web searches and 3-5 vault searches is enough for round 1
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.
- 3d ago First seen · 99 lines · 0 tokens per session scan A 7f5ddeadd130
explorer is an agent published in the GitHub repository kvarnelis/deep-recon (43 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 986 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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