Add AGENTS.md for coding agent discovery

Agent-facing doc that points at live sources of truth (CLI --help,
hf jobs hardware, model card eval results API) rather than duplicating
info that goes stale. Covers script selection, dataset vs bucket I/O
patterns, common flags, and gotchas.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

AGENTS.md

@@ -0,0 +1,92 @@
+# For coding agents
+
+This repo is a curated collection of ready-to-run OCR scripts — each one self-contained
+via UV inline metadata, runnable over the network via `hf jobs uv run`. No clone, no
+install, no setup.
+
+## Don't rely on this doc — discover the current state
+
+This file will go stale. Prefer these sources of truth:
+
+- `hf jobs uv run --help` — job submission flags (volumes, secrets, flavors, timeouts)
+- `hf jobs hardware` — current GPU flavors and pricing
+- `hf auth whoami` — check HF token is set
+- `hf jobs ps` / `hf jobs logs <id>` — monitor running jobs
+- `ls` the repo to see which scripts actually exist (bucket variants especially)
+- [README.md](./README.md) — the table of scripts with model sizes and notes
+
+## Picking a script
+
+The [README.md](./README.md) table lists every script with model size, backend, and
+a short note. Axes that matter:
+
+- **Model size** vs accuracy vs GPU cost. Smaller = cheaper per doc.
+- **Backend**: vLLM scripts are usually fastest at scale. `transformers` and
+  `falcon-perception` are alternatives for specific models.
+- **Task support**: most scripts do plain text; some expose `--task-mode`
+  (table, formula, layout, etc.) — check the script's own docstring.
+
+For the authoritative benchmark numbers on any model in the table, query the model
+card programmatically — every OCR model publishes eval results on its card:
+
+    from huggingface_hub import HfApi
+    info = HfApi().model_info("tiiuae/Falcon-OCR", expand=["evalResults"])
+    for r in info.eval_results:
+        print(r.dataset_id, r.value)
+
+See the [leaderboard data guide](https://huggingface.co/docs/hub/en/leaderboard-data-guide)
+for the full API. This is more reliable than any markdown table that might drift.
+
+## Getting help from a specific script
+
+Each script has a docstring at the top with a description and usage examples. To read it
+without downloading:
+
+    curl -s https://huggingface.co/datasets/uv-scripts/ocr/raw/main/<script>.py | head -100
+
+Or open the URL in a browser. Running `uv run <url> --help` locally may fail if the
+script has GPU-only dependencies — reading the docstring is more reliable.
+
+## The main pattern: dataset → dataset
+
+Most scripts take an input HF dataset ID and push results to an output HF dataset ID:
+
+    hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+      https://huggingface.co/datasets/uv-scripts/ocr/raw/main/<script>.py \
+      <input-dataset-id> <output-dataset-id> [--max-samples N] [--shuffle]
+
+The script adds a `markdown` column to the input dataset and pushes the merged result
+to the output dataset ID on the Hub.
+
+## Alternative: directory → directory (bucket variants)
+
+A couple of scripts have `-bucket.py` variants (currently `falcon-ocr-bucket.py` and
+`glm-ocr-bucket.py`) that read from a mounted directory and write one `.md` per image
+(or per PDF page). Useful with HF Buckets via `-v`:
+
+    hf jobs uv run --flavor l4x1 -s HF_TOKEN \
+      -v hf://buckets/<user>/<input>:/input:ro \
+      -v hf://buckets/<user>/<output>:/output \
+      https://huggingface.co/datasets/uv-scripts/ocr/raw/main/<script>-bucket.py \
+      /input /output
+
+`ls` the repo to check whether a `-bucket.py` variant exists for the model you want
+before assuming it's available.
+
+## Common flags across dataset-mode scripts
+
+Most scripts support: `--max-samples`, `--shuffle`, `--seed`, `--split`, `--image-column`,
+`--output-column`, `--private`, `--config`, `--create-pr`, `--verbose`. Read the script's
+docstring for the authoritative list — individual scripts may add model-specific options
+like `--task-mode`.
+
+## Gotchas
+
+- **Secrets**: pass `-s HF_TOKEN` to forward the user's token into the job.
+- **GPU required**: all scripts exit if CUDA isn't available. `l4x1` is the cheapest
+  GPU flavor and works for models up to ~3B. Check `hf jobs hardware` for current options.
+- **First run is slow**: model download + `torch.compile` / vLLM warmup dominates small
+  runs. Cost per doc drops sharply past a few hundred images — test with `--max-samples 10`
+  first, then scale.
+- **Don't poll jobs**: jobs run async. Submit once, check status later with
+  `hf jobs ps` or `hf jobs logs <id>`.