academic-commercialization-agent: Instructions file for Codex

AGENTS.md

academic-commercialization-agent AGENTS.md is an instructions file for Codex, OpenCode from shuxiachai/academic-commercialization-agent. It costs 4,800 tokens per session, scanned A, original, MIT.

Repository guide for a six-agent system that checks whether academic research could become a viable commercial product. It gathers evidence about research, patents, and markets before writing and scoring a report.

In plain words
What is it for?
Use it to work on the FastAPI service, browser client, command-line tool, tests, or the research-to-report workflow.
Why use it?
It explains the pipeline and the commands used to verify it, so an agent can make changes without losing evidence or breaking the web and command-line versions.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions AGENTS.md.

This is shuxiachai/academic-commercialization-agent's own configuration. It tells Codex and OpenCode how to work on academic-commercialization-agent itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything academic-commercialization-agent configures →

Reuse

Borrowing it

Nothing to install: this file belongs to shuxiachai/academic-commercialization-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/shuxiachai/academic-commercialization-agent/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/shuxiachai/academic-commercialization-agent

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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Per session 4,800 This file is loaded in full into every session.
When invoked 4,800 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.04800 $0.04800
Opus 5 $0.02400 $0.02400
Sonnet 5 $0.00960 $0.00960
Haiku 4.5 $0.00480 $0.00480

Measured yesterday against content hash bda01ab368f9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

academic-commercialization-agent 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.

AGENTS.md · 318 lines

How it starts

The opening of the file, as written. The whole thing — 318 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — working on this project

An index, not a summary. Almost everything worth knowing is already in the repository — the point of this file is to tell you where, and to stop you redoing a handful of things that have already been tried and measured.

What it is

A six-agent CrewAI pipeline that assesses whether a piece of academic research is commercially viable. Three evidence agents (academic / patent / market) run in parallel, then a report writer, a reviewer, and a scorer. Served two ways from one codebase: a FastAPI + vanilla-JS web client and a CLI.

Live at https://academic-commercialization-agent.up.railway.app

Baseline at 0fdaa76 (2026-09-05 documentation audit): 2071 tests and 678 subtests. CI covers Linux + Windows × Python 3.11/3.12; Railway hosts the demo. See current evidence status for qualified results, not an ever-growing chronological paragraph here.

Commands

uv run pytest -q                       # the whole suite; zero provider calls
uv run --with ruff ruff check .        # CI uses latest ruff, the local pin is older
uv sync --group e2e                    # opt-in real-browser dependency; not part of the default suite
uv run --group e2e playwright install chromium
uv run --group e2e python -m e2e.browser_smoke   # loopback only; zero provider calls
uv run --group e2e python -m e2e.composer_smoke  # static-only server; intercepted paid POSTs
uv run uvicorn api.main:app --reload   # web client on :8000
uv run academic_agent --topic "<topic>"          # one run from the CLI

uv run --with ruff is deliberate. The pinned local ruff is 0.12.0 and CI resolves latest; they disagree, and the flag is what makes a local check mean what CI will say.

Hard conventions

  • filterwarnings = ["error::UserWarning"]. What it mainly guards: code paths that would reach a paid API warn and fall back rather than raising, so a test that leaks would otherwise pass silently. When a test trips it, read which warning fired before concluding anything. If it is the network one, the test is calling something real — fix the test's isolation, never add an ignore. But the project also warns deliberately on an unrecognised weight profile and on an audit screen that failed, and two test files assert those on purpose (assertWarns), so not every UserWarning is a leak.
  • No bare except Exception without a # noqa: BLE001 and a reason. This is a review convention; the current Ruff selection does not enable BLE001. Do not assume a green lint job enforces it.
  • CI also runs pylint, narrowly, for bad exception order, unreachable code, used-before-assignment and undefined variables. The exception-order check caught HTTPError placed after URLError: dead code that retried auth failures as transient. Use the exact command in CONTRIBUTING.md.
  • Commit messages carry the reasoning. They are long on purpose and are a primary record — see below. Do not add Co-Authored-By lines.
  • Reply to the user in Chinese. The code, comments, and commit messages stay in English.

Read the full file on GitHub · 318 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 Changed · +31 lines · +424 tokens per session bda01ab368f9
  2. 2d ago Changed · +29 lines · +458 tokens per session 523d212f3a11
  3. 3d ago Changed · +17 lines · +256 tokens per session d2fe3c4d30af
  4. 4d ago Changed · -870 lines · -13,687 tokens per session cb75e41ca048
  5. 5d ago Changed · +437 lines · +6,947 tokens per session 8ba37dee717c
  6. 9d ago First seen · 674 lines · 10,402 tokens per session scan A 31ababdcdb8f

Subscribe to this mod's changes

academic-commercialization-agent AGENTS.md is an instructions file published in the GitHub repository shuxiachai/academic-commercialization-agent (806 stars, last pushed yesterday), licensed MIT. It adds 4,800 tokens to every session, about $0.0240 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.

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