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/illyar80/developer-farm/agents-mdgit clone --depth 1 https://github.com/illyar80/developer-farmWhat 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 | $0.01021 | $0.01021 |
| Opus 5 | $0.00511 | $0.00511 |
| Sonnet 5 | $0.00204 | $0.00204 |
| Haiku 4.5 | $0.00102 | $0.00102 |
Grade A, and why
developer-farm 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 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.
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.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CONTEXT FOR LANGGRAPH CODE GENERATION
1. Environment
- LangGraph version: 1.2.1 (CHECK with
pip show langgraphbefore generating) - Python: 3.11
- Pydantic: v2 (use
from pydantic import BaseModel, NOT v1) - Checkpointer: SqliteSaver (local file, no Postgres)
2. Architecture Constraints (GOODHART-PROOF ISOLATION)
This system implements 4-layer isolation. NEVER violate these boundaries:
- PLANNING nodes receive ONLY: user_spec, tech_spec, codebase_index
- EXECUTION nodes receive ONLY: task_description, context_files, git_worktree_path ❌ NEVER pass: acceptance_criteria, test_files, verification_rubric
- VERIFICATION nodes receive ONLY: SealedArtifact (git_diff + logs), rubric ❌ NEVER pass: worker_id, original_task_prompt, chat_history, planning_context
- OPTIMIZATION nodes receive ONLY: aggregated verdicts, metrics summary ❌ NEVER pass: artifact contents, current graph state, raw logs
All state schemas MUST use TypedDict with explicit field lists. If a field is not in the TypedDict, it CANNOT be passed between nodes.
3. Required Patterns
- Use
StateGraph(notMessageGraph) - Use
add_conditional_edgesfor validation loops (max 3 iterations) - Use
interrupt()fromlanggraph.typesfor human-in-the-loop (NOT deprecatedNodeInterrupt) - Use
Send()API for parallel fan-out in execution wave - Always compile with checkpointer:
graph.compile(checkpointer=checkpointer) - Stream via
graph.astream_events(config, version="v2")
4. Working Example Reference
from typing import TypedDict, Annotated
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.types import interrupt, Send
class ExecutionInput(TypedDict):
task_description: str
context_files: list[str]
worktree_path: str
# ❌ NO acceptance_criteria, NO rubric
class SealedArtifact(TypedDict):
git_diff: str
logs: str
# ❌ NO worker_id, NO task_description
async def code_worker(state: ExecutionInput) -> dict:
# Call local vLLM or API here
artifact = await generate_code(state)
return {"sealed_artifact": artifact}
async def blind_verifier(state: dict) -> dict:
artifact = state["sealed_artifact"]
rubric = state["rubric"]
# ❌ Cannot access state["task_description"] or state["worker_id"]
verdict = await verify(artifact, rubric)
return {"verdict": verdict}
# Conditional edge for retry loop
def should_retry(state: dict) -> str:
if state["verdict"]["passed"] or state["iteration"] >= 3:
return "approved"
return "revise"
builder = StateGraph(dict)
builder.add_node("code_worker", code_worker)
builder.add_node("blind_verifier", blind_verifier)
builder.add_conditional_edges("blind_verifier", should_retry, {
"approved": END,
"revise": "code_worker"
})
checkpointer = SqliteSaver.from_conn_string("./checkpoints.db")
graph = builder.compile(checkpointer=checkpointer)
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 · 98 lines · 1,021 tokens per session scan A b30124ee7b33
developer-farm AGENTS.md is an instructions file published in the GitHub repository illyar80/developer-farm (11 stars, last pushed 2mo ago), licensed MIT. It adds 1,021 tokens to every session, about $0.0051 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
deepagents AGENTS.md
AGENTS.md instructions for langchain-ai/deepagents, covering global development guidelines for the deep agents monorepo, corridor security analysis, development workflow, suppressing ruff rules and pr conventions.
ollama CLAUDE.md
Claude Code instructions for ollama/ollama: See AGENTS.md for the shared agent instructions for this repository.
codexia AGENTS.md
Instructions for milisp/codexia, covering agents.md, project info, project tech, common commands and project structure.
eval-view AGENTS.md
Instructions for hidai25/eval-view, covering evalview agent instructions, what evalview is, core concepts, testcase and evaluationresult.
Elpis AGENTS.md
Instructions for MasihMoafi/Elpis, covering elpis agent map, start, context discipline, definition of done and evals first.
TradingAgents-Telegram CLAUDE.md
Instructions for IvanWng97/TradingAgents-Telegram, covering tradingagents-telegram — architecture reference, layout, architecture (for code reviewers), request lifecycle (manual /watch tap) and state ownership.