smart-dispatch

Smart Agent & Model Dispatch (Pro Edition)

Stage input — consume the EXEC-MAP contract if present

If an EXEC-MAP v1 block from senior-prompt-engineer is in context, consume it instead of re-deriving complexity:

• EXEC-MAP.effort seeds the tier (trivial/low → Tier 0; medium/high → Tier 1).
• EXEC-MAP.models is the per-phase routing intent (plan→Opus/quality, impl→Sonnet/balanced, mechanical→Haiku/budget); honor it unless validation proves it wrong.
• EXEC-MAP.executor picks the provider/CLI; map model tiers to that CLI's tiers.
• EXEC-MAP.mcp lists tools to wire up. Treat the map as a starting routing decision, not gospel — escalation rules (Tier 1) still override it when validation keeps failing. With no EXEC-MAP, derive routing from the request as usual.

Tier 0 — Automated Boilerplate & Verification (RTK Bypass)

0.1 Local-First Rule

Before dispatching to an AI agent for Tier 0 tasks, attempt to solve it using local CLI tools.

• Lint: Run npm run lint --fix or eslint --fix.
• Formatting: Run prettier --write.
• Typecheck: Run tsc and check if errors are trivial.

0.2 RTK Bypass Dispatch (The "Hammer")

If local tools fail or aren't enough, use Gemini-Flash YOLO via RTK.

• Lint/Typecheck/I18n/Tests: Route to rtk gemini --yolo.

0.3 Verification Mandate

Any agent dispatched in YOLO/Bypass mode MUST execute the relevant validation command before completion.

• If it fixed types, it must run tsc --noEmit.
• If it fixed lint, it must run npm run lint.
• If it fixed tests, it must run npm run test <file>.

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Tier 1 — Escalation & Intelligence Mapping

1.0 Task-Type → Model Routing (default table)

Always pick the cheapest tier that passes validation; escalate per 1.1. Tiers map per provider:

Tier	Claude	Codex	Gemini
budget	Haiku 4.5	gpt low/mini reasoning	Flash
balanced	Sonnet 4.6	gpt medium reasoning	Pro
quality	Opus 4.8 / Fable 5	gpt high reasoning	Pro (max thinking)

Task type	Tier	Notes
Docs, comments, changelogs, summaries, translations	budget	Haiku-class is enough; never burn Opus here
Test writing — mechanical / known pattern	budget	Verify with test run (0.3 mandate)
Lint / typecheck / format fixes	local CLI → budget	Tier 0 rules apply first
Implementation, refactor, multi-step agentic	balanced	Sonnet-class default for code
Test design — integration/e2e strategy	balanced	Strategy needs reasoning; writing the cases can drop to budget
Plan, architecture, root-cause, security audit, migration	quality	Opus-class; deep reasoning pays for itself

EXEC-MAP's models: {plan, impl, mechanical} maps 1:1 to quality/balanced/budget rows above.

1.1 Auto-Escalation Policy

If a Tier 0 agent fails to resolve the issue (validation still fails) after 2 attempts, the dispatcher MUST:

1. Stop using the "cheap" model.
2. Escalate the task to a Tier 1 model (Claude Sonnet or Gemini Pro).
3. Notify the user: "Escalating to [Model] due to persistent validation failures."

1.2 Capability Matrix

Domain	Best Platform	Reason
Multimodal / UI screenshots	Gemini Pro (latest)	Best image/screenshot interpretation.
Complex Logic / Math	Codex (gpt high reasoning)	Superior reasoning for algorithms.
Agentic Chains / Auth	Claude Sonnet 4.6+	Best tool-use and multi-step autonomy.
Deep Plan / Architecture	Claude Opus 4.8 / Fable 5	Strongest long-horizon reasoning.
Open Source / Docs	OpenCode	Deep integration with community patterns.

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Rules

• Announce & Verify: Always say what you are doing and how you will verify it.
• Token Economy: Prefer Gemini Flash for context-heavy reads but simple edits.
• Fail Fast: Don't loop on cheap models. Escalate early.
• Local First: CLI tools > AI Agents for mechanical fixes.