Borrowing it
Nothing to install: this file belongs to yoloshii/gigaxity-deep-research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/yoloshii/gigaxity-deep-research/main/AGENTS.mdgit clone --depth 1 https://github.com/yoloshii/gigaxity-deep-researchWrote 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/instructions/yoloshii/gigaxity-deep-research/agents-md)<a href="https://agentmods.dev/instructions/yoloshii/gigaxity-deep-research/agents-md"><img src="https://agentmods.dev/badge/instructions/yoloshii/gigaxity-deep-research/agents-md/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/instructions/yoloshii/gigaxity-deep-research/agents-md"><img src="https://agentmods.dev/badge/instructions/yoloshii/gigaxity-deep-research/agents-md.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 | $0.09784 | $0.09784 |
| Opus 5 | $0.04892 | $0.04892 |
| Sonnet 5 | $0.01957 | $0.01957 |
| Haiku 4.5 | $0.00978 | $0.00978 |
Grade A, and why
gigaxity-deep-research AGENTS.md 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| Empty results from `discover` / `synthesize` | SearXNG host unreachable | `curl $RESEARCH_SEARXNG_HOST/healthz` — should return 200. REST mode: `GET /api/v1/health/connectors` probes every connector at once | Copies of this mod
1 near-identical copy found in the catalogue:
- gigaxity-deep-research CLAUDE.md — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 472 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gigaxity Deep Research — Agent Reference
This is the agent reference for Gigaxity Deep Research, an open-source deep research MCP server for Claude Code, Hermes, Cursor, and other MCP-compatible agents. Qwen3-30B-A3B-Thinking runs via OpenRouter, the Triple Stack search MCPs (Context7, Exa, Jina) handle web/docs/code retrieval, and the bundled research-workflow skill routes queries to the right tool per query class.
This file is loaded by Claude Code (CLAUDE.md) and other MCP-compatible agents (AGENTS.md is byte-identical). It documents how to operate the six MCP tools this server exposes (two primitives plus four deep-research tools) and how to plug them into the broader deep research stack.
If your harness loads a global CLAUDE.md or AGENTS.md (Claude Code, Codex, Cursor, Hermes, etc.), copy the instruction block at the bottom of this file into that global file. For standalone agents that take a system prompt instead, paste the block directly into the system prompt. That single block makes any compatible agent automatically route research queries through this MCP plus the six companion MCPs (Context7, Exa, Exa Answer, Jina, Brightdata fallback, gptr-mcp) in the full deep research stack.
Tool surface
The MCP server exposes two primitives plus four deep-research tools — six tools total. Pick a primitive when you want raw or combined behavior in one call; pick a deep-research tool when you want to drive discovery, synthesis, or reasoning as a discrete step.
Primitives
| Tool | Use for | Token cost (typical) |
|---|---|---|
mcp__gigaxity-deep-research__search |
Raw multi-source aggregation (SearXNG + Tavily + LinkUp + Brave + RRF). No LLM call. | 0 LLM tokens; search-API quotas only |
mcp__gigaxity-deep-research__research |
Combined search + synthesis with citations in a single call. The simple pipeline. | ~3000–8000 |
Deep-research tools
| Tool | Use for | Token cost (typical) |
|---|---|---|
mcp__gigaxity-deep-research__ask |
Quick conversational answer; speed > depth (direct LLM, no search hop) | ~500–1500 |
mcp__gigaxity-deep-research__discover |
Cold-start exploration; surfaces explicit/implicit/related/contrasting angles + gap detection | ~2000–5000 |
mcp__gigaxity-deep-research__synthesize |
Citation-aware fusion of pre-gathered content; CRAG quality gate, contradiction surfacing | ~5000–10000 |
mcp__gigaxity-deep-research__reason |
Deep synthesis with explicit chain-of-thought depth control over pre-gathered content | ~5000–15000 |
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 · 472 lines · 9,784 tokens per session scan A 0ab6fb762f97
gigaxity-deep-research AGENTS.md is an instructions file published in the GitHub repository yoloshii/gigaxity-deep-research (59 stars, last pushed today), licensed MIT. It adds 9,784 tokens to every session, about $0.0489 per session on Opus 5. 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.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.