Survey available LLM models across the project-app's configured providers (Gemini, Claude, OpenAI, Grok, Perplexity, Cerebras, etc.) and produce a cost-vs-quality upgrade matrix for a specific model upgrade request. Then dispatch an AO worker to land the upgrade PR — never edit the project repo inline. Trigger when the user says 'upgrade <model> in <project-app>', 'bump gemini/anthropic/openai to <new>', 'investigate model upgrades for our providers', 'check cost vs quality on benchmarks', 'is <new model> worth switching to', 'upgrade from <old> to <new>', or pairs a project name with a 'model upgrade' / 'investigate model upgrade' verb.
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Survey available LLM models across the project-app's configured providers (Gemini, Claude, OpenAI, Grok, Perplexity, Cerebras, etc.) and produce a cost-vs-quality upgrade matrix for a specific model upgrade request. Then dispatch an AO worker to land the upgrade PR — never edit the project repo inline. Trigger when the user says 'upgrade <model> in <project-app>', 'bump gemini/anthropic/openai to <new>', 'investigate model upgrades for our providers', 'check cost vs quality on benchmarks', 'is <new model> worth switching to', 'upgrade from <old> to <new>', or pairs a project name with a 'model upgrade' / 'investigate model upgrade' verb.
When a user asks to upgrade a model in a project-app (e.g. jleechanorg/ai_universe, $GITHUB_REPOSITORY), survey the available models across every provider the project uses, build a cost-vs-quality matrix, and dispatch an AO worker to land the upgrade PR. This skill is the research + decision phase; the execution phase is always-pr-never-local-edit + drive-pr-to-green + the AO dispatch path.
The class-level survey has three principles:
Project-app scope, not Hermes scope. This is for jleechanorg/<project> repos, NOT for ~/.hermes/config.yaml. Mutating the Hermes gateway is swap-hermes-provider. Surveying a third-party repo with no project context is eval-vendor-tooling.
Read the actual config first, don't trust the README. The default model in ConfigManager.ts may already be the new model — the "upgrade" may be a no-op or a pricing-table update only.
The output is a cost-vs-quality matrix + a small menu of next actions. The user gets to choose. The agent does not auto-pick.
Phase 0 — Map the project's provider wiring
Before answering "what should we upgrade to," answer "what is the project actually using today." The project's three files always tell the story:
Map of internal model id → user-facing display name
Read all three before doing any vendor research. The defaults may already be at the latest model — the user may be asking based on a stale Slack thread or a docs page they read 6 months ago.
The PROVIDER_CONTEXT_TOKEN_LIMITS constant in ConfigManager.ts (e.g. cerebras: 262_144, claude: 200_000, gemini: 1_048_576, grok: 1_000_000, perplexity: 200_000, openai: 128_000) is the canonical "what contexts does each provider support" table — cross-reference it against the new model's published context window to confirm the upgrade doesn't break long-context code paths.
Three findings worth surfacing even if the user already knew:
"You already moved to gemini-3-flash-preview last quarter — the legacy gemini-2.5-flash pricing key in CostCalculator.ts is dead code."
"ConfigManager still pins claude-sonnet-4-20250514 even though Claude Sonnet 5 launched — that's the actual upgrade target."
"OpenAI default is gpt-5-nano ($0.05/$0.40) but the canonical mapping in CostCalculator.ts has gpt-5 and gpt-5-mini listed separately — make sure the new modelId is in BOTH the modelMappings object AND the PRICING table."
Phase 1 — Survey the new model (verify the user's claim, don't trust it)
The user said "upgrade from X to Y." Before doing the upgrade, prove Y exists AND has published pricing AND has published benchmarks. The research-integrity rule says: search snippets are leads, not evidence. Use web_search/web_extract + curl for vendor pages; verify the model id is in the official model card AND in OpenRouter's API surface.
Verification sequence (canonical recipe):
Vendor's model page (deepmind.google, ai.google.dev, anthropic.com, openai.com, x.ai, cerebras.ai):
curl -fsS -A "Mozilla/5.0""https://deepmind.google/models/gemini/flash/" | \
grep -oE '<th[^>]*>[^<]*</th>|<td[^>]*>[^<]*</td>' | head -200
The <table> HTML for benchmark comparisons gives you the head-to-head with the previous-gen sibling, the previous-gen Pro, and the closest competitors. Headlines like "released 2026-07-21" anchor the date.
OpenRouter pricing as the cross-vendor canonical source (most reliable pricing aggregator):
curl -fsS -A "Mozilla/5.0""https://openrouter.ai/<vendor>/<model-id>" | \
grep -oE '(\$|0\.|7\.5e-6|1\.5e-6|9\.00e-7)[^"]{0,30}|Input (Modalities|Price):[^<]{0,80}'
OpenRouter renders JSON-LD with price as a decimal per token — 7.5e-6 = $7.50/1M tokens. This is the same price Google charges; OpenRouter adds a 5% margin on top, so the divisor is exactly the vendor list price.
curl -fsS -A "Mozilla/5.0""https://ai.google.dev/gemini-api/docs/models/gemini-3.6-flash"
Confirm: text + image + video + audio in, text out, 1M context, 64k max output, function-calling / search-as-a-tool / computer-use.
Cross-check the launch date with a third-party news source (9to5google.com, thedecoder.co, TechCrunch) — this catches the "vendor accidentally published the page before GA" trap.
Pitfall P1 — search snippets vs primary source. A web_search returning "Gemini 3.6 Flash" mentions is a lead, not evidence. The web_extract tool returns "DuckDuckGo is a search-only backend" against most pages — fall back to curl against the raw HTML. The web_search result count is a popularity signal, not a verification.
Pitfall P2 — OpenRouter 5% markup. If the OpenRouter JSON-LD says 5.0e-6 per output token, the vendor list price is 5.0e-6 * 0.9524 = $4.76/1M. Round to the nearest published tier ($5 / $7.50). Don't double-count the markup.
Pitfall P3 — "Gemini 3 Flash has cache pricing too." OpenRouter exposes cache_read and cache_write separately. If the user runs high-prompt-volume secondary-opinion workloads, cache pricing can dominate the cost — surface this in the matrix.
Pitfall P4 — deprecation lanes. Some "preview" and "exp" models have GA tiers with different pricing (gemini-2.0-flash-exp → gemini-2.0-flash doubling output cost). Always check the GA tier separately from the preview.
Phase 2 — Build the cost-vs-quality matrix
The user's mental model is: "is the new model worth it?" The matrix answers that in one table.
Sources to cite (in order of authority):
Vendor's own model-comparison page (deepmind.google/models/gemini/flash/, anthropic.com/news/, openai.com/index/). These have the vendor-curated head-to-head table.
Third-party benchmarks (lm-evaluation-harness, LMArena, BenchLM.ai, llm-stats). Use when the vendor doesn't publish a comparison.
OpenRouter's pricing page — authoritative for list pricing across all major vendors.
The matrix shape (verified deliverable shape for vision-formatted Slack/chat posts):
Also include a sibling-line comparison (e.g. 3.6F vs 3.5F vs 3.1F) — the user's actual decision is often "stay on the older tier" vs "upgrade one tier" vs "upgrade two tiers," and a 3-row comparison shows the gradient.
The recommendation tile (last section of the deliverable):
Slot
Current
Recommended swap
Reasoning
This is the table the user reads. The previous benchmark matrix is the evidence. The recommendation is the action.
Phase 3 — Deliver the response shape
The user wants a structured response, not a flowing narrative. Use this exact shape — verified against Jeffrey's preferences across at least 3 sessions:
What's currently wired (1 short table, 3-6 rows, file:line citations to the actual config files).
Vendors confirmed exists (one paragraph, with citation links to vendor + 3rd-party).
Headline reads (4-6 bullet points, each ≤ 2 lines, framed in plain English).
Recommendation matrix (per-slot table: current → recommended → reasoning).
Next-step menu (4 numbered options, no asking "what do you want to do?" — clearly named action paths the user picks from).
Forbidden in the deliverable:
"I recommend X" without the matrix backing it.
"Want me to do X?" — always "Option 1 — X. Option 2 — Y."
"Pick one: A) B) C)" multi-menu picks (per SOUL.md no-pick-one-menus COMMIT).
"I cannot verify pricing" without a fallback to OpenRouter as the canonical cross-vendor source.
Nested quotes of the original request — paraphrase once, then move on.
Phase 4 — Dispatch path (after the user picks a menu option)
The deliverable is research, not code. The user approving "Option 1 — apply now" triggers the dispatch. Per always-pr-never-local-edit + finish-the-job, the right move is:
Spawn the AO worker with the full task description + the cost-vs-quality matrix + the exact file paths to edit. The worker handles the worktree + PR + push.
The worker touches (canonical 4-5 file touch list for an AI-Universe style upgrade):
Run npm test in shared-libs/packages/mcp-server-utils to verify the cost-calculator unit tests pass
The worker does NOT bump the shared library's dist/*.js (those are regenerated by npm run build in CI).
The PR body lists the file:line touched, the new pricing numbers, and WHY the upgrade is worthwhile (the matrix summary).
Worker prompt template (canonical, from the 2026-07-30 ai-universe-gemini-3.6-flash session):
Task: upgrade <vendor>/<model> from <old> to <new> in jleechanorg/<repo>.
Touch these files exactly:
- <repo>/shared-libs/packages/config-utils/src/ConfigManager.ts — change `<provider>.model` to '<new-model-id>'
- <repo>/shared-libs/packages/mcp-server-utils/src/CostCalculator.ts — add pricing entry for <new-model-id>:
'<new-model-id>': { input: <input_price>, output: <output_price> },
AND add a normalizer in `modelMappings`:
'<new-model-id>': '<pricing-key>',
- <repo>/shared-libs/packages/mcp-server-utils/src/ModelDisplayNames.ts — add display name:
'<new-model-id>': '<Display Name>',
- <repo>/ai_universe_frontend/src/mocks/mockMcpServer.ts + src/mockData/canonicalSamples.ts + src/test/mocks/mcpFixtures.ts — update any 'gemini-2.0-flash-exp' / 'gemini-2.5-flash' / 'gemini-3-flash-preview' fixture to 'gemini-3.6-flash' (or whichever new model).
Verify:
- `cd <repo>/shared-libs/packages/mcp-server-utils && npm test` passes
- After bumping, models normalize to the new pricing key (no fallback warnings)
- `git diff --stat origin/main...HEAD` shows only the touched files, no incidental dirt
Cost-vs-quality rationale (paste the matrix from this session):
| Benchmark | New Model | Notes |
|---|---|---|
| Price in | $X / 1M | |
| Price out | $Y / 1M | |
| Long-context | 91.8% on 128k MRCR | only Flash-line model with 1M coverage |
| Agentic | 83% OSWorld | $X/$Y vs Sonnet 5 81% at $3/$15 |
PR body sections (required by your-project.com Gate 0 if this is a `$PROJECT_ROOT/**` file — N/A for ai-universe's shared-libs but include for completeness):
- ## Tenets
- ## Design Decision (the 4 rules: avoid-<old-model>-gotchas, use-vendor-published-pricing, no-preview-model-in-prod, etc.)
- ## Linked artifacts (bead rev-xxxx if applicable)
The worker prompt MUST include the cost-vs-quality matrix — otherwise the worker has no signal to pick the right pricing tier over the wrong one. The matrix is the deliverable's evidence, the upgrade is the action.
Pitfalls
P1. The user may be asking about a model the project already uses
Always re-read the three canonical files BEFORE answering. The "upgrade" may be a no-op (model already on the new tier) or a pricing-only update (ConfigManager already points to the new model, but CostCalculator still has the old pricing key). Surface this in the first paragraph of the deliverable — the user trusts you more if you say "actually, you already did this last quarter" than if you treat their request as fresh.
P2. OpenRouter markup confuses pricing math
OpenRouter's listed price is vendor-list + ~5% markup. Divide by 1.05 (or look at the OpenRouter JSON-LD price field — it's the OpenRouter price, not the vendor price). When in doubt, cite the vendor's own pricing page for the headline number and OpenRouter for the cross-vendor comparison.
P3. Preview/experimental models have different pricing than GA
gemini-2.0-flash-exp (preview) → gemini-2.0-flash (GA) → gemini-2.5-flash (GA) all have different price points. The default in ConfigManager.ts may be the preview string; the user is asking about the GA tier. Always confirm which tier the upgrade targets.
P4. Don't trust unit numbers — read the full benchmark table
A single MMLU score (e.g. "88.5%") doesn't tell you the model is the best. The full table on the vendor's model page includes the model's weakness on long-context or reasoning. The deliverable should cite the model's STRENGTH (why upgrade) and the closest competitor's STRENGTH (why not). Surface both.
P5. Cache pricing dominates at high-prompt-volume
For second-opinion workloads (the same prompt sent to N models), the cache_read rate ($0.15/1M on Gemini 3.6 Flash) is the dominant cost when the prompt is reused. The cost-vs-quality matrix should break out cache pricing when the user runs high-volume secondary queries.
P6. The "best" model is often the wrong upgrade
When the matrix shows "the new model is 5% better at 2× the cost," the right answer is "stay on the current model." The deliverable should explicitly say so when the trade-off is unfavorable. The user wants a recommendation, not a sales pitch.
P7. The dispatch decision is "apply now" vs "AO worker" vs "research only"
Per always-pr-never-local-edit + finish-the-job, the default is "AO worker on a fresh worktree from origin/main" — never inline edits. The deliverable's menu should default to this option. Inline edits are only OK if the change is single-file and <10 lines and the user has explicitly said "do it now, don't bother with a PR."
Verification
Before posting the deliverable:
All three canonical files have been read — ConfigManager.ts, CostCalculator.ts, ModelDisplayNames.ts. The "current model" claim is anchored to a file:line.
The new model is verified to exist — at least one curl against the vendor's model page or OpenRouter returned the model name + pricing.
The cost-vs-quality matrix has ≥ 4 columns (new model, prev gen, top competitor, cheaper competitor) and ≥ 6 benchmark rows.
The recommendation matrix has a per-slot decision — at least one slot is "keep" (don't blindly upgrade everything).
The next-step menu has 3-4 concrete options — no "what would you like me to do?" framing.
The LLM-provenance caveat is included at the bottom of any Slack post per SOUL.md llm-provenance-caveat COMMIT.
Related references
references/gemini-3.6-flash-verified-pricing-benchmarks-2026-07-30.md — the verified Gemini 3.6 Flash vendor numbers + same-vendor benchmark comparison from deepmind.google/models/gemini/flash/. Read this when starting a Gemini 3-line upgrade.
references/ai-universe-config-file-map-2026-07-30.md — the canonical 3-file layout for jleechanorg/ai_universe (ConfigManager, CostCalculator, ModelDisplayNames).
Related skills
always-pr-never-local-edit — the PR lifecycle that the dispatch step calls.
finish-the-job — the end-state contract for the dispatched AO worker.
drive-pr-to-green — when the upgrade PR needs to navigate your-project.com's Design Doc Grep Gate (Gate 0) or equivalent.
swap-hermes-provider — the sibling skill for ~/.hermes/config.yaml provider swaps. NOT this skill.
eval-vendor-tooling — the sibling skill for evaluating a third-party vendor ML repo. NOT this skill.
