Report · marked 25 Aug 2026
K-Dense-AI/scientific-agent-skills
Solid foundations, held back in a few specific places.
Python · 34,449 stars · 2,446 files · branch main
76
/100
B+
Instructions
27/27Whether the repo tells an agent how to behave before it starts guessing. Full marks.Setup
0/14Whether an agent can install the project and get it running without a human. Losing marks on deterministic install and discoverable commands.Verification loop
19/25Whether an agent can check its own work. This is the category that most decides whether agent output is trustworthy. Losing marks on lint and format rules and static type checking.Context economy
17/20Whether the repo fits in a context window, or fights it. Losing marks on repository weight.Navigability
10/10How quickly anything new can orient itself. Full marks.
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
Discoverable commands
Declare the handful of commands that matter in package.json scripts, a Makefile, or a justfile. Naming them turns guesswork into a lookup.
03
Lint and format rules
A formatter config is the cheapest way to stop reviewing whitespace in agent diffs. It moves style from opinion to a command.
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.
Also found: CLAUDE.md
✓Instruction quality
12/12
Specific enough that an agent can act on it.
17,842 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 0/14 F
Whether an agent can install the project and get it running without a human.
✗Deterministic install
0/6
No lockfile. An agent installing dependencies may not get what CI got.
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
0/6
No declared commands. An agent has to infer how to build and run this from the file tree.
Declare the handful of commands that matter in package.json scripts, a Makefile, or a justfile. Naming them turns guesswork into a lookup.
–Environment config
n/a
Not applicable — this reads as a library, with no environment to configure.
✗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
n/a
Not applicable — a library doesn't need a container to be worked on.
Verification loop 19/25 B+
Whether an agent can check its own work. This is the category that most decides whether agent output is trustworthy.
✓Tests exist
8/8
216 test files against 685 source files.
e.g. skills/pptx-posters/references/manifest_spec.md, tests/_contract/__init__.py, tests/_contract/cli.py
✓Test command is discoverable
7/7
An agent can find and run `pytest`.
✓Continuous integration
4/4
5 GitHub Actions workflows define what "passing" means.
✗Lint and format rules
0/3
No linter or formatter config. Style is tribal knowledge, so agent output will drift from yours.
A formatter config is the cheapest way to stop reviewing whitespace in agent diffs. It moves style from opinion to a command.
✗Static type checking
0/3
No static type checking.
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 17/20 A
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 13 patterns.
✓File sizes fit in context
6/6
No source file is large enough to crowd out a context window.
✗Repository weight
2/5
About 248MB 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 Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 175,000+ scientists worldwide. 163 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard. ## Setup ``` # TODO: the install command. No lockfile was found, so this could not be inferred. ``` ## Commands ``` pytest # run the test suite ``` ## Layout - `skills/` 543 source files - `tests/` 140 source files ## Conventions - Tests live alongside the code they cover, following `skills/pptx-posters/references/manifest_spec.md`. - CI defines what passing means. See `.github/workflows/pr-skill-scan.yml`, and keep it green. - TODO: add the two or three conventions a newcomer always gets wrong here. ## Gotchas - 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/K-Dense-AI/scientific-agent-skills.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.