agentleFS
NVIDIAKnown publisherNVIDIA/structured-data-models-client/AGENTS.md

structured-data-models-client

AGENTS.md12 starsChanged 17 days agoApache-2.0View on GitHub

Read CONTRIBUTING.md first: it has the development setup, the test commands, lint, type-checking and the snapshot rule, and README.md for what the packages are. This file is only what is easy to get wrong here, and most of it has been got wrong at least once. Package directories under packages/ carry the distribution name. Nothing derives one name from the other: read the import root off src/. A rename that sweeps the tree will change these, and nothing will fail:

  • Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.

    Of the AGENTS.md files we read, the 31,542 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.

  • In this repository3 other files it gives its agents.

The file

We split this AGENTS.md into the rules it gives an agent

2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read CONTRIBUTING.md first: it has the development setup, the test commands, lint, type-checking and the snapshot rule, and README.md for …

    line 3
  2. Distinctive:

    Every first-party source file carries an SPDX header.

    line 82

≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
AGENTS.md

Working in this repository

Read CONTRIBUTING.md first: it has the development setup, the test commands, lint, type-checking and the snapshot rule, and README.md for what the packages are. This file is only what is easy to get wrong here, and most of it has been got wrong at least once.

What you install is not what you import

DistributionModule
structured-data-models-clientsdm_client
structured-data-models-apisdm_api
structured-data-models-pqlpredictive_query
structured-data-models-connectorssdm_connectors
structured-data-models-relational-enginesdm_relational_engine

Package directories under packages/ carry the distribution name. Nothing derives one name from the other: read the import root off src/.

Strings that look like names and are not

A rename that sweeps the tree will change these, and nothing will fail:

  • DATABRICKS_PRODUCT, kept twice because the connectors never import the client: sdm_client/core/databricks_user_agent.py and sdm_connectors/_databricks_partner.py, held to the same value by a test. SAMPLING_QUERY_TAG sits beside the second. They are what a warehouse sees, so changing them splits Databricks partner telemetry and query history at that version.
  • APPLICATION in sdm_connectors/backends/snowflake.py, nvidia/kumo-sdm. It is the application name Snowflake attributes this client's sessions to, so changing it splits that attribution at that version.
  • _term('sdm-relational-engine') in rfm/graph_fingerprint.py. It is hashed. Changing it gives identical graphs different names, which the module says only FINGERPRINT_VERSION may do.

Run the two suites separately

The engine and the other four packages cannot be collected in one pytest run; together they fail at import. CONTRIBUTING.md gives both commands. Run both.

skills/ is executable

tests/test_skills.py reads the tables out of skills/ and puts them to the client, and with -m live_nim it runs every complete example and compares what it printed to the block pasted beneath it. A **Verified** block is a claim that an endpoint produced exactly that. Do not write one by hand, and do not edit the numbers in one.

An example is complete when every name it reads was bound by itself or by an earlier complete example. One that is not is a fragment: it is checked as code, executed nowhere, and may not show output.

Generated files

Regenerate these; never edit them:

  • packages/structured-data-models-api/src/sdm_api/generated/ -- regenerate with scripts/generate_sdm_api.py, which also writes PROVENANCE.json. A change to the generator itself changes the recorded hash.
  • The third-party attribution in the two LICENSE files, the root one and the engine's -- scripts/mermaid_notices.py --write, checked with --check. It renders rfm/assets/MERMAID_COMPONENTS.json, which --refresh regenerates; neither touches NOTICE.
  • rfm/assets/relbench_registry.json -- scripts/pin_relbench.py. It pins source commits and table digests.
  • The ANTLR output in predictive_query/grammar/: the .py, .tokens and .interp files. Edit PQLGrammar.g4 and regenerate. The README.md and __init__.py there are hand-written.
  • tests/golden/ -- the exported surface and the recorded wire payloads.

Gates that fail on prose

scripts/check_repo_hygiene.py all runs in CI. It refuses a shipped file that names internal infrastructure: the internal git host, a bug tracker id, a security-program id, the internal wiki, or an internal sibling repository. The patterns are in that script, and this file is written around them rather than listed as an exception. A comment citing an internal ticket is a build failure, not a style note.

Every first-party source file carries an SPDX header.

Commits

Sign off every commit (git commit --signoff); see CONTRIBUTING.md. A pull request whose commits are not signed off cannot be merged.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.