output-dev-upgrade-prompt-models

Agent Building

Bulk-upgrade the model field across .prompt files to the latest version of each prompt's existing family. Use when prompt models have drifted (eg sonnet-4 → sonnet-4-6), after a long pause between framework updates, or as part of a periodic model-freshness pass. Within-family only — never changes provider or tier.

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/growthxai/output/blob/HEAD/coding_assistants/claude/plugins/outputai/skills/output-dev-upgrade-prompt-models/SKILL.md

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Upgrade Prompt Models In-Place

Walks every .prompt file in a project (or scoped subtree), classifies each model into its provider+family bucket, looks up the latest stable model in that bucket via the output-dev-model-selection snapshot, and rewrites the model: line. Provider and family tier are preserved — a Haiku stays a Haiku, an Anthropic stays an Anthropic.

This skill explicitly does not swap providers or escalate tiers (eg Haiku → Sonnet). Those are deliberate human decisions handled separately.

When to invoke

A periodic refresh: "upgrade all my prompt models to the latest"
After a long break between framework updates, where dated snapshot IDs (eg claude-sonnet-4-20250514) have aged out
Right after creating a new project from the CLI scaffolder, to pull every templated default forward to the current best

Workflow

Step 1 — Discover

Find every .prompt file under the target scope. Default scope is the project's src/ tree; the user may scope to a single workflow.

Step 2 — Parse current model

For each file, read the YAML frontmatter (between the first pair of --- lines) and pull out provider: and model:.

Step 3 — Classify family

Match the existing model into a family bucket. Family is preserved across the upgrade.

Pattern	Family
`claude-opus-*`	`anthropic-opus`
`claude-sonnet-*`	`anthropic-sonnet`
`claude-haiku-*`	`anthropic-haiku`
`gpt-*-pro`	`openai-pro`
`gpt-*-mini`	`openai-mini`
`gpt-*-nano`	`openai-nano`
`gpt-N.M` (no suffix)	`openai-default`
`gemini--flash-lite`	`google-flash-lite`
`gemini--flash`	`google-flash`
`gemini--pro`	`google-pro`

If a model doesn't match any pattern, skip the file and log a warning. Do not guess.

Step 4 — Look up latest in family

For each prompt, find the latest stable model in the same family by following output-dev-model-selection — fetch its snapshot, apply its filter rules (skip preview/alpha/beta, prefer unversioned aliases), and translate the chosen id to prompt-file form.

Use this family → snapshot-key + id regex map to pin the lookup to the existing tier:

Family	Snapshot key	`id` regex
`anthropic-opus`	`anthropic`	`claude-opus-`
`anthropic-sonnet`	`anthropic`	`claude-sonnet-`
`anthropic-haiku`	`anthropic`	`claude-haiku-`
`openai-pro`	`openai`	`-pro$`
`openai-default`	`openai`	`^openai/gpt-[0-9.]+$`
`openai-mini`	`openai`	`-mini$`
`openai-nano`	`openai`	`-nano$`
`google-pro`	`google`	`-pro` (excluding `-flash`)
`google-flash`	`google`	`-flash$\|-flash-[0-9]`
`google-flash-lite`	`google`	`-flash-lite`

If no stable match exists for a family (only pre-release entries available), surface that to the user and skip the file rather than guessing or downgrading to a different family.

Bail loudly on a failed snapshot. If the snapshot fetch itself returned nothing — network down, gateway shape changed, curl or jq missing — abort the run before Step 5. Do not continue with an empty snapshot and report "no upgrades needed", because that lies: the prompts weren't actually checked. Tell the user the snapshot fetch failed, point them at the manual-fallback steps in output-dev-model-selection, and exit.

Step 5 — Diff & confirm

Build a per-file report comparing the current model to the resolved latest:

src/workflows/foo/prompts/bar@v1.prompt   claude-sonnet-4-20250514  →  claude-sonnet-4-6
src/workflows/foo/prompts/baz@v1.prompt   claude-haiku-4-5          ✓ already latest

Print the full report. Wait for explicit user confirmation before writing. In CI / non-interactive contexts, default to dry-run.

Step 6 — Edit

For each confirmed file, edit only the YAML frontmatter:

Replace the model: line with the resolved latest ID.
If a # current as of YYYY-MM-DD … comment is present (the convention used in output-dev-prompt-file examples and CLI scaffolds), update its date to today's (date +%Y-%m-%d). Match the comment by the literal current as of prefix and only rewrite the date — leave the trailing text intact.
Leave provider:, temperature:, maxTokens:, providerOptions:, and the message body untouched.

Refreshing the dated comment in the same edit keeps the "as of" convention coherent — without it, an upgraded prompt would have a fresh model paired with a stale date.

Step 7 — Verify

After the batch:

Spot-check a handful of files to confirm the YAML still has a frontmatter delimiter and message body.
Rebuild the worker so the new model strings take effect (npm run output:worker:build).

The Output SDK doesn't validate prompt model IDs at build time (sdk/llm/src/ai_model.js) — invalid IDs only surface at first run. If smoke-tests are available, run at least one workflow per upgraded family.

Caveats

Within-family only. This skill never upgrades Sonnet → Opus, never swaps Anthropic for OpenAI. To change tier or provider, edit prompts manually or use output-dev-prompt-file.
Dated snapshots get bumped. A pin like claude-sonnet-4-20250514 becomes the unversioned alias claude-sonnet-4-6. If the pin was load-bearing for reproducibility, surface that and skip the file.
@vertex and bedrock namespace suffixes. Models like claude-sonnet-4-20250514@vertex or anthropic.claude-sonnet-4-20250514-v1:0 need manual upgrade. The AI Gateway listing covers direct provider IDs only.
Models.dev pricing lag. Even after the upgrade, calculateLLMCallCost may return total: null for the brand-new model until models.dev catches up. The runtime call still works.