Model Pyramid

Right-size each subagent's MODEL + REASONING-EFFORT via 4 pyramid rules (peer co-work keeps both; search drops effort one notch, floor medium; ~20+ cheap lookups drop one model tier; else medium). Use when spawning / fanning out / delegating subagents, or sizing them: "$model-pyramid". NOT model shopping/pricing.

Advisory rule card for picking each subagent's model + reasoning-effort at fan-out time, in a pyramid shape. Framing is right-sizing (合理使用): assign what the work needs — never pitch or log these choices as cost reduction. The skill recommends and reports; it never spawns agents, edits configs, or blocks the user.

Compatibility: any agent runtime (Claude Code, Codex CLI, generic harnesses). Optional checker script needs Node.js >= 18.

When this fires

• Before the FIRST subagent spawn of any fan-out (explore / build / verify / delegate).
• When asked what model or effort a subagent / worker / judge panel should get.

Do NOT fire for: model shopping or API pricing with no subagents in play; loop/workflow STRUCTURE design (that is loop-constructor's job — co-fire only to size the agents its loop spawns); "shrink my API spend" asks (out of scope).

Preflight

1. Identify the session's model capability tier and reasoning effort. Tiers: frontier (opus-tier) > mid (sonnet-tier) > small (haiku-tier). Effort ladder: low < medium < high < max.
2. Effort not introspectable? Assume high — never assume low.
3. Runtime knob names differ — load references/runtime-mapping.md when emitting.

The four rules — classify each task independently

Rule	Model	Effort
R1 peer/parallel co-work — equal-difficulty shards, judge panels, adversarial verifiers, or a single delegated deep task (n=1 counts)	keep	keep
R2 search/exploration — codebase/web/evidence gathering	keep	down exactly ONE notch
R2b very large search fan-out — ~20+ homogeneous cheap lookups	down exactly ONE tier (e.g. opus-tier→sonnet-tier); already lowest → use R2	keep
R3 no rule matches	keep	medium

Precedence & clamps

• Work content wins: parallel-shaped exploration is R2, not R1.
• Mixed batch in one spawn → classify PER TASK, never one batch-wide setting.
• HARD FLOOR: low is never emitted; any computed sub-medium effort clamps UP to medium — every rule, every layer.
• One knob per layer: at most ONE effort notch OR ONE tier down, never both.

Verify the pyramid shape

• Two layers is the norm. A third layer, or any haiku-tier pick from a higher-tier session, needs an explicit one-line justification in the report.
• Recursive: when a subagent itself fans out, re-apply the rules per layer — floor stays medium, at most one tier drop per layer, and the deeper pyramid is flagged as an exception.

Report & overrides

• Emit ONE line per subagent: rule=<id> tier=<tier> effort=<notch> plus any clamp / assumed-high / justification flags. Rule ids: peer / search / search-tier-drop / default / override.
• An explicit user override WINS: apply it verbatim, adding a one-line floor caveat if it sits below medium. Advisory means advisory — never block. In scripts/decide.mjs pass it as task.user_override = {tier?, effort?} (a guessed override key is ignored).
• Runtime lacking a knob: map to the nearest supported notch and state the degradation; never emit an unsupported parameter (see the mapping reference).

Deterministic check (optional)

Verify a sizing plan mechanically (stdlib node, JSON in/out):

bash

node scripts/decide.mjs '{"session":{"tier":"opus-tier","effort":"max"},"tasks":[{"peer":true},{"exploratory":true,"fan_out_size":24,"homogeneous_cheap":true}]}'

checkAssignment(session, assignment) audits a proposed assignment and flags below-floor / both-knobs-dropped / skipped-tier / needs-justification.

Files

File	Load when
references/runtime-mapping.md	emitting knobs for a concrete runtime (Claude Code / Codex CLI / generic), or the session's knobs are unclear
scripts/decide.mjs	you want the decision or an assignment audit computed deterministically

Metrics: decision accuracy = evals/run_all.mjs fixture table (target 100%); activation precision ≥ 0.9 on evals/cases/trigger-cases.json.