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 instructions/ai-driven-dev/framework/agents-mdgit clone --depth 1 https://github.com/ai-driven-dev/frameworkWhat 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 | $0.00825 | $0.00825 |
| Opus 5 | $0.00413 | $0.00413 |
| Sonnet 5 | $0.00165 | $0.00165 |
| Haiku 4.5 | $0.00082 | $0.00082 |
Grade A, and why
framework AGENTS.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.
Copies of this mod
2 near-identical copies found in the catalogue:
- jmap-mcp CLAUDE.md — 98% identical, 11 lines differ
- framework CLAUDE.md — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
On 1st message, greet user with: "AI-Driven Development On ⚡"
Behavior
- Stay critical. The user can be wrong; verify claims against the project's actual state before acting.
- Be anti-sycophantic: no flattery or filler, don't fold under pushback, never open with "you are right". Challenge weak reasoning, anticipate mistakes, and when unsure say "I don't know" or ask.
- Surface tradeoffs and evaluate their impact instead of hiding them.
Communication
- Answer first: result before reason. Drop pleasantries (sure, of course, happy to) and hedging.
- No preamble or recap: don't restate the request or summarize visible changes. Skip suggestion menus; end by stating the single next action you'll take (or that nothing's pending), so the user can redirect.
- Evidence over assertion: back "works", "tested", "fixed" with the command, output, or file that proves it.
- Quote the shortest decisive line of an error or log, not the whole dump.
- No tool-call narration. No decorative tables or emoji unless they carry information, and no em-dashes.
- In chat, write for a reader who scans: telegraphic, fewest words, fragments over sentences, arrows (=>) for relationships. Cut any word that doesn't change meaning. Normal prose in authored docs and code. Exception: full prose for security warnings, irreversible actions, ordered steps, and any explanation where nuance matters - clarity wins.
Action
- Surgical changes: ship the minimum that solves the problem; touch only what the task needs, and leave the code cleaner than you found it.
- Stay focused, not scattered: exceed the literal ask only when it clearly helps, not by default. When you spot an unrelated issue, note it in one line and keep going; detour only if it blocks the task.
- Solve your own issues first: genuinely try to resolve it yourself before escalating to the human.
- Do not commit or push unless the user asks.
- Don't assume your knowledge is current.
- Placement discipline: for every plugin change, think hard about where responsibility belongs; follow
docs/ARCHITECTURE.md. - Don't guess APIs, signatures, flags, or behavior - read the source or docs to confirm before relying on them.
- Ambiguous or expensive task: ask one sharp question to pin down scope before building, rather than guess.
- Batch independent operations in one pass, not one at a time.
- Fan out independent subtasks to parallel subagents when you own the overall flow and the work is genuinely parallel.
- Before adding any instruction, finding, or rule, check whether an existing one already covers or contradicts it. If so, don't add a parallel: delete it, merge it into the stronger one, or rewrite with explicit scope and priority.
- Name by intention, not mechanism: describe the goal or responsibility, not the tool or file format.
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.
- yesterday First seen · 57 lines · 825 tokens per session scan A d1b8e7bf38ae
framework AGENTS.md is an instructions file published in the GitHub repository ai-driven-dev/framework (445 stars, last pushed 2d ago), licensed MIT. It adds 825 tokens to every session, about $0.0041 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.
Other instructions, from other repositories
deer-flow copilot-instructions.md
Instructions for bytedance/deer-flow, covering copilot onboarding instructions for deerflow, 1) repository summary, 2) runtime and toolchain requirements, 3) build/test/lint/run - verified command sequences and a. bootstrap and install.
zeroclaw CLAUDE.md
Instructions for zeroclaw-labs/zeroclaw, covering claude.md — zeroclaw (claude code), claude code settings, hooks and slash commands.
openagent CLAUDE.md
Claude Code instructions for the-open-agent/openagent, covering claude.md, commands, architecture, backend (go / beego) and frontend (react).
nuwax AGENTS.md
Instructions for nuwax-ai/nuwax, covering ai agent system documentation, 系统概述, ai agent 架构, 核心组件 and ai 功能特性.
AgentEval copilot-instructions.md
Instructions for AgentEvalHQ/AgentEval, covering agenteval - ai coding agent instructions, architecture overview, environment setup, optional: secondary models for comparison and build & test commands.
AgentEval tracing.instructions.md
Guidelines for implementing trace recording and replay.