Report · marked 25 Aug 2026
HKUDS/DeepTutor
Ready for an agent to work in without hand-holding.
Python · 37,483 stars · 2,267 files · branch main
87
/100
A
Instructions
27/27Whether the repo tells an agent how to behave before it starts guessing. Full marks.Setup
15/20Whether an agent can install the project and get it running without a human. Losing marks on deterministic install and pinned runtime version.Verification loop
24/25Whether an agent can check its own work. This is the category that most decides whether agent output is trustworthy. Losing marks on static type checking.Context economy
15/20Whether the repo fits in a context window, or fights it. Losing marks on file sizes fit in context and repository weight.Navigability
8/10How quickly anything new can orient itself. Losing marks on written documentation.
Higher is better. Accent marks what is costing you most.
Fix these first
01
Deterministic install
Commit the lockfile your package manager generates. Without it, an agent's install and your install are different builds, and 'works on my machine' becomes unfalsifiable.
02
Repository weight
Large binaries and vendored trees slow every operation an agent performs. Git LFS or a separate assets repo keeps the working tree navigable.
03
Pinned runtime version
Add a .nvmrc, .python-version, or equivalent so the agent's toolchain matches yours.
The full marking
Every deduction below names the file or setting it came from.
Instructions 27/27 A+
Whether the repo tells an agent how to behave before it starts guessing.
✓Agent instruction file
12/12
AGENTS.md is present, which is the format the most tools read.
✓Instruction quality
12/12
Specific enough that an agent can act on it.
6,684 characters, a workable length
names the actual commands to run
uses headings, so an agent can skim it
includes copyable code blocks
✓README as an entry point
3/3
README has a clear getting-started section.
Setup 15/20 B+
Whether an agent can install the project and get it running without a human.
✗Deterministic install
3/6
Only requirements.txt, which pins loosely at best.
Commit the lockfile your package manager generates. Without it, an agent's install and your install are different builds, and 'works on my machine' becomes unfalsifiable.
✓Discoverable commands
6/6
Commands are declared where an agent will look for them.
pyproject.toml scripts
scripts/ directory
✓Environment config
4/4
.env.example documents what the app needs to run.
✗Pinned runtime version
0/2
No pinned runtime version.
Add a .nvmrc, .python-version, or equivalent so the agent's toolchain matches yours.
✓Reproducible environment
2/2
dockerfile gives a known-good environment.
Verification loop 24/25 A+
Whether an agent can check its own work. This is the category that most decides whether agent output is trustworthy.
✓Tests exist
8/8
584 test files against 1829 source files.
e.g. deeptutor/learning/tests/__init__.py, deeptutor/learning/tests/test_api_endpoints.py, deeptutor/learning/tests/test_grading.py
✓Test command is discoverable
7/7
An agent can find and run `pytest`.
✓Continuous integration
4/4
4 GitHub Actions workflows define what "passing" means.
✓Lint and format rules
3/3
pyproject.toml encodes the house style.
✗Static type checking
2/3
Type checking is configured, but not in strict mode.
A type checker gives an agent an error message instead of a runtime surprise. It's the second-fastest feedback loop after the compiler.
Context economy 15/20 B+
Whether the repo fits in a context window, or fights it.
✓No committed build output
6/6
No generated directories committed.
✓.gitignore hygiene
3/3
.gitignore covers 195 patterns.
✗File sizes fit in context
4/6
3 source files are over 100KB.
A single file that fills the context window forces an agent to work from fragments, and it will confidently edit code it never saw. Splitting the worst offenders pays for itself immediately.
deeptutor/api/routers/knowledge.py — 130KB
deeptutor/agents/research/pipeline.py — 110KB
deeptutor/services/session/turn_runtime.py — 108KB
✗Repository weight
2/5
About 244MB checked out. Large enough that cloning and searching are both slow.
Large binaries and vendored trees slow every operation an agent performs. Git LFS or a separate assets repo keeps the working tree navigable.
Here is your AGENTS.md
Drafted from what is actually in this repository: the install
command from your lockfile, the commands you already declare, your real directory layout. Anything
marked TODO needs a person. Save it at the root as AGENTS.md.
# AGENTS.md DeepTutor: Lifelong Personalized Tutoring. https://deeptutor.info/. ## Setup ``` pip install -r requirements.txt ``` ## Commands ``` pytest # run the test suite ruff check . # lint ``` ## Layout - `deeptutor/` 740 source files - `web/` 581 source files - `tests/` 475 source files - `deeptutor_cli/` 21 source files - `scripts/` 11 source files - `deeptutor_web/` 1 source file ## Conventions - Tests live alongside the code they cover, following `deeptutor/learning/tests/__init__.py`. - CI defines what passing means. See `.github/workflows/docker-release.yml`, and keep it green. - TODO: add the two or three conventions a newcomer always gets wrong here. ## Gotchas - `deeptutor/api/routers/knowledge.py` is 130KB. It will not fit comfortably in context, so read it in parts. - Copy `.env.example` before running anything that needs configuration. - No lockfile is committed, so an install here may not match what CI produced. --- Drafted by agentreadme.com from what is in this repository. Everything marked TODO needs a human. Check it in as AGENTS.md at the root.
Open the raw markdown
or
curl -o AGENTS.md agentreadme.com/draft/HKUDS/DeepTutor.md
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Think this mark is wrong?
Every deduction above names the file it came from, so this can be settled by looking. If a check missed something, that is a rule worth fixing.