agentmods.dev

semantic-deer-flow copilot-instructions.md

Repository-specific onboarding instructions for DeerFlow, a full-stack system for building AI agents with a Python backend, a web frontend, tools, memory, and integrations. They document the required runtimes and verified commands for development.

In plain words
What is it for?
Installing dependencies, starting DeerFlow locally or with Docker, running its backend and frontend, and following the repository's build, test, and lint procedures.
Why use it?
They give a coding agent a reliable starting point for understanding the repository and running it correctly. This reduces setup mistakes across its backend, frontend, Docker, and local development services.

Instructions file for GitHub Copilot

21 26d ago A scan 1,714 tokens copy · 100% MIT
View the source file CamiProject/semantic-deer-flow
Install

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.

agentmods
npx agentmods add instructions/camiproject/semantic-deer-flow/copilot-instructions
Clone the repo
git clone --depth 1 https://github.com/CamiProject/semantic-deer-flow

Made for: GitHub Copilot.

Per session 1,714 This file is loaded in full into every session.
When invoked 1,714 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01714 $0.01714
Opus 5 $0.00857 $0.00857
Sonnet 5 $0.00343 $0.00343
Haiku 4.5 $0.00171 $0.00171

Measured yesterday against content hash c0f1dabcd26d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

semantic-deer-flow copilot-instructions.md 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 yesterday.

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.

Origin

This is a copy

100% identical to deer-flow copilot-instructions.md — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.github/copilot-instructions.md · 214 lines

How it starts

The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Copilot Onboarding Instructions for DeerFlow

Use this file as the default operating guide for this repository. Follow it first, and only search the codebase when this file is incomplete or incorrect.

1) Repository Summary

DeerFlow is a full-stack "super agent harness".

  • Backend: Python 3.12, LangGraph + FastAPI gateway, sandbox/tool system, memory, MCP integration.
  • Frontend: Next.js 16 + React 19 + TypeScript + pnpm.
  • Local dev entrypoint: root Makefile starts backend + frontend + nginx on http://localhost:2026.
  • Docker dev entrypoint: make docker-* (mode-aware provisioner startup from config.yaml).

Current repo footprint is medium-large (backend service, frontend app, docker stack, skills library, docs).

2) Runtime and Toolchain Requirements

Validated in this repo on macOS:

  • Node.js >=22 (validated with Node 23.11.0)
  • pnpm (repo expects lockfile generated by pnpm 10; validated with pnpm 10.26.2 and 10.15.0)
  • Python >=3.12 (CI uses 3.12)
  • uv (validated with 0.7.20)
  • nginx (required for make dev unified local endpoint)

Always run from repo root unless a command explicitly says otherwise.

3) Build/Test/Lint/Run - Verified Command Sequences

These were executed and validated in this repository.

A. Bootstrap and install

  1. Check prerequisites:
make check

Observed: passes when required tools are installed.

  1. Install dependencies (recommended order: backend then frontend, as implemented by make install):
make install

B. Backend CI-equivalent validation

Run from backend/:

make lint
make test

Validated results:

  • make lint: pass (ruff check .)
  • make test: pass (277 passed, 15 warnings in ~76.6s)

CI parity:

  • .github/workflows/backend-unit-tests.yml runs on pull requests.
  • CI executes uv sync --group dev, then make lint, then make test in backend/.

C. Frontend validation

Run from frontend/.

Recommended reliable sequence:

pnpm lint
pnpm typecheck
BETTER_AUTH_SECRET=local-dev-secret pnpm build

Read the full file on GitHub · 214 lines

Changes

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.

  1. yesterday First seen · 214 lines · 1,714 tokens per session scan A c0f1dabcd26d

Subscribe to this mod's changes

semantic-deer-flow copilot-instructions.md is an instructions file published in the GitHub repository CamiProject/semantic-deer-flow (21 stars, last pushed 26d ago), licensed MIT. It adds 1,714 tokens to every session, about $0.0086 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to deer-flow copilot-instructions.md, differing in 0 lines, and is treated as a copy.

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