Borrowing it
Nothing to install: this file belongs to dotnetpower/infomesh. 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/dotnetpower/infomesh/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/dotnetpower/infomeshWrote 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/dotnetpower/infomesh/copilot-instructions)<a href="https://agentmods.dev/instructions/dotnetpower/infomesh/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/dotnetpower/infomesh/copilot-instructions/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/dotnetpower/infomesh/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/dotnetpower/infomesh/copilot-instructions.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 | $0.16254 | $0.16254 |
| Opus 5.5 | $0.06502 | $0.06502 |
| Sonnet 5.5 | $0.03251 | $0.03251 |
| Haiku 4.5 | $0.01625 | $0.01625 |
Grade B, and why
infomesh copilot-instructions.md scanned grade B 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 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
- WSL startup reads the effective `wslinfo --networking-mode`. Mirrored mode checks port-specific inbound TCP Hyper-V rules in ActiveStore and prints scoped administrator commands, never NAT portproxy setup. Unknown mode How it starts
The opening of the file, as written. The whole thing — 869 lines — stays where its author put it; the contents beside it link to each section on GitHub.
InfoMesh — Copilot Instructions
Project Overview
InfoMesh is a fully decentralized P2P search engine designed exclusively for LLMs. It crawls, indexes, and searches the web via a peer-to-peer network and exposes results through MCP (Model Context Protocol) — no human-facing UI required.
The static EN/KO project website is in site/, deployed separately by
.github/workflows/pages.yml. It is a project introduction and MCP setup guide,
not a hosted search interface. Keep asset URLs relative, client-specific MCP
configuration correct, EN/KO text paired, and keyboard/reduced-motion support.
Its network illustration is conceptual; never imply live peer counts or privacy
guarantees for distributed queries. Validate desktop/mobile interactions and
run node --check site/app.js plus actionlint after website/workflow changes.
Keep social-preview metadata in the initial HTML with absolute HTTPS image URLs,
matching PNG dimensions and alt text for OG/Twitter. Verify with
uv run pytest tests/test_site.py -q; external previews require public deployment.
Mission: InfoMesh does not compete with commercial search providers. These companies serve human search at massive scale with ads-based monetization. InfoMesh provides minimal, sufficient search capabilities for LLMs — for free, via MCP — democratizing real-time web access for AI assistants without per-query billing. It is a community-driven public utility, complementary to existing search providers.
Core Principles
| Principle | Description |
|---|---|
| Fully Decentralized | No central server. Every node is both a hub and a participant. |
| LLM-First | No browser UI. Pure text API optimized for LLM consumption. |
| Contribute = Reward | More crawling contribution → more search quota (cooperative tit-for-tat model). |
| Offline-Capable | Local index is searchable without internet. |
| Privacy | Search queries are never recorded centrally. |
Tech Stack
| Layer | Technology | Notes |
|---|---|---|
| Language | Python 3.12+ | Use modern Python features (type hints, match, type statement, StrEnum, etc.) |
| P2P Network | libp2p (py-libp2p) | Optional (pip install 'infomesh[p2p]'). DHT, Noise encryption built-in. Uses trio, not asyncio — see note below |
| DHT | Kademlia | Distributed hash table for index & crawl coordination |
| Crawling | httpx + asyncio | Async-first HTTP client |
| HTML Parsing | trafilatura | Best accuracy for main-content extraction |
| Keyword Index | SQLite FTS5 | Zero-install, embedded full-text search |
| Vector Index | ChromaDB | Semantic search with embeddings |
| MCP Server | mcp-python-sdk | VS Code / Claude / Cursor / Windsurf integration |
| Admin API | FastAPI | Local status & config endpoints |
| Serialization | msgpack | Faster and smaller than JSON |
| Compression | zstd | Level-tunable compression; dictionary mode for similar documents |
| Local LLM | ollama / llama.cpp | Optional local summarization (Qwen 2.5, Llama 3.x, etc.) |
| Logging | structlog | Structured logging for all library code |
| Package Manager | uv | Fast Python package/project manager (replaces pip/venv) |
| Build Backend | hatchling | PEP 517 build backend for PyPI distribution |
| JS Rendering | Playwright | Optional headless Chromium (pip install 'infomesh[browser]') |
| CJK Tokenization | jieba | Optional Chinese segmentation (pip install 'infomesh[cjk]') |
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 Changed · +57 lines · +2,572 tokens per session scan A → B c8c075869416
- 2mo ago First seen · 812 lines · 13,682 tokens per session scan A 77bc01122659
infomesh copilot-instructions.md is an instructions file published in the GitHub repository dotnetpower/infomesh (7 stars, last pushed 1mo ago), licensed MIT. It adds 16,254 tokens to every session, about $0.0650 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
infomesh copilot-instructions.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| infomesh copilot-instructions.mddotnetpower/infomesh | Instructions file | 7 | 1mo ago | MIT | B | 16,254 | GitHub Copilot |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |