Sync model catalog

Keeps the agent model catalog honest. The catalog is the curated decoration over each harness's
accepted model set: clean labels, one-sentence descriptions, real pricing, and 1-5 ratings, keyed
by the id the harness accepts. It is published additively next to the ids-only models map on the
harness capability record (capabilities.py).

Design and rationale:
docs/design/agent-workflows/projects/model-catalog-schema/{design.md,plan.md}.

What it owns

Three JSON data files under sdks/python/agenta/sdk/agents/data/, loaded by
sdks/python/agenta/sdk/agents/model_catalog.py:

• pi_models.generated.json — machine-generated from pi-ai. Objective facts only (name / pricing /
  context_window / modalities), source: "pi_generated". Never hand-edit.
• pi_models.curated.json — human overlay for the generated file (id -> {label?, description?, ratings?}), merged onto the generated facts at load. Survives regeneration.
• claude_models.curated.json — hand-curated Claude alias entries (facts + judgments),
  source: "curated".

It never edits capabilities.py logic — only these data files.

The three jobs

1. Regenerate the Pi file (on a pi-ai version bump)

The generator reads the pinned pi-ai models.generated for the providers Agenta reaches (the
vault-mapped providers plus openai-codex) and emits one entry per model. pi-ai provider names are
mapped to Agenta's vocabulary (google->gemini, together->together_ai); ids are
<agenta-provider>/<pi-model-id>.

# From repo root. Point at the pinned pi-ai in the runner's node_modules (the .pnpm path includes
# the version — resolve it with the glob).
MODELS=$(ls services/runner/node_modules/.pnpm/@earendil-works+pi-ai@*/node_modules/@earendil-works/pi-ai/dist/models.generated.js | head -1)
node .agents/skills/sync-model-catalog/generate_pi_models.mjs "$MODELS" \
  sdks/python/agenta/sdk/agents/data/pi_models.generated.json

Detect the bump from a lockfile diff on @earendil-works+pi-ai@<version>. The _generator field
in the output records the exact pi-ai version. The curated overlay is untouched — only the
.generated.json is rewritten, so the merge on load re-applies the human judgments.

2. Sync Claude to the live accepted set (needs a running runner)

claude_models.curated.json must cover the stable request values the Claude picker can send.
Probe live sessions by reading the model config options (the same getConfigOptions call
allowedModels uses in services/runner/src/engines/sandbox_agent/model.ts), but do not copy one
session's set blindly. Account entitlements and promotions can add or remove context-hinted variants
such as claude-fable-5[1m] while keeping the same model family.

Use the stable bare canonical id when the runner can safely widen it to the session's hinted option.
For Fable, publish claude-fable-5: it matches a bare live option exactly and the runner resolves it
to claude-fable-5[1m] when that is the only offered variant. Do not publish the friendly forms
fable or fable[1m]; the harness does not recognize that model family under those ids. Requires
an authenticated Claude session, so this is a manual/periodic step, not a CI gate.

3. Refresh curated metadata from current public sources (before a release / on demand)

Labels, descriptions, and ratings state a model's current standing, which a language model's
training data gets wrong (the Anthropic frontier is Fable 5, above Opus, as of mid-2026). Look up the
current lineup, pricing, and relative standing from the vendor's pages and announcements (WebSearch +
WebFetch), then propose updated descriptions and ratings for a human to confirm. Never write a rating
from memory. Validate the 1-5 range and flag any entry whose facts you could not verify. Ratings:
higher is better on every axis; cost is cost-efficiency (5 = cheapest).

Validate

The pydantic loader enforces the schema (including the 1-5 rating range) on load, and the unit test
locks coverage and the overlay merge:

cd sdks/python && uv run --no-sync python -m pytest \
  oss/tests/pytest/unit/agents/connections/test_model_catalog.py -q

A malformed data file fails loud there (and at import in capabilities.py, which then publishes an
empty catalog rather than crashing /inspect).

When to run

• Job 1 on a pi-ai bump (automatable from a lockfile diff).
• Jobs 2 and 3 before a release or on demand (need a live session and a web lookup).

The skill writes files and a proposal; a human reviews the curated changes and commits.