Sk Model Resolver

Model Resolver — 5-Layer Cross-Platform Model Resolution

Translates agent-author intent (model_tier: deep) into concrete platform-specific model strings (claude-opus-4-8 on CC, opencode-go/kimi-k2.6 on OC, gpt-5.5 on Codex). Preloaded by running-a-pipeline Phase 0.45. Pure function — never writes to disk, never calls APIs.

Public API

RESOLVE(agent_frontmatter, profile, prefs) → resolved
LOAD_PREFS(workspace_root) → { workspace, user }
EMIT(resolved, target_format) → string
RENDER_RESOLUTION_TABLE(entries[]) → string
REVERSE_MAP(concrete_model, profile) → tier_id | null
DETECT_CATALOG_DRIFT(prefs, profile) → { drifted: bool, message: string | null }

Where each entries[] element is { step_id, agent_name, model_tier, resolved }.

resolved object schema

{
  "model": "claude-opus-4-8",
  "effort": "high",
  "effort_field_name": null,
  "model_field_format": "shorthand",
  "source": "user_prefs",
  "warnings": []
}

model: null means "omit the field; let the host pick" (Tier 2, dynamic-subagent platforms, or inherit tier).

Algorithm

Normative source: references/resolution-algorithm.md — all six operations (RESOLVE, LOAD_PREFS, EMIT, RENDER_RESOLUTION_TABLE, REVERSE_MAP, DETECT_CATALOG_DRIFT) are specified there. Both the skill adapter (this file) and the inline adapter (running-a-pipeline Phase 0.45) execute the same algorithm. Do not restate steps here.

Preference File Schema

{
  "platforms": {
    "tier_1": {
      "name": "Claude Code",
      "model_tiers_version_acked": "2026-05-19",
      "tiers": {
        "triage": "claude-haiku-4-5-20251001",
        "fast":   "claude-haiku-4-5-20251001",
        "medium": "claude-sonnet-4-6",
        "deep":   "claude-sonnet-4-6"
      },
      "effort_default": {
        "deep": "high"
      }
    },
    "tier_1b": {
      "name": "OpenCode",
      "model_tiers_version_acked": "2026-05-19",
      "tiers": { "fast": "opencode-go/deepseek-v4-flash" }
    }
  }
}

Key rules:

• tier_1, tier_1b, tier_1c, tier_1d, tier_2 are the canonical keys — never localize them. They map directly to platform_profile.tier.
• name is a display-only sibling field. Writers (wizard/change-models) MUST stamp it from platform_profile.name so future readers (and users browsing the JSON) can identify the platform without consulting profile JSONs. Readers (resolver) MUST NOT branch on name — it has no effect on resolution.
• tiers holds the four-tier model overrides (triage | fast | medium | deep). Partial maps allowed; missing keys fall through to profile default.
• model_tiers_version_acked mirrors profile.model_tiers_version at write-time; drift detector compares to current.
• effort_default is optional; per-tier effort override.

Red Flags — STOP

• "I'll cache the resolved model across runs." → STOP. State file caches per run; mid-run pref edits must take effect on the next run, not be stale-cached.
• "I'll re-resolve at dispatch time per step." → STOP. Phase 0.45 resolves once upfront and writes to state. Phase 3 reads state. Re-resolution at dispatch causes mid-run inconsistency if prefs change.
• "I'll merge workspace and user prefs into one object." → STOP. Consult-in-order preserves provenance (which layer won); merging loses the source field.
• "I'll call platform APIs to validate the resolved model exists." → STOP. Resolver is pure. Validation belongs in change-models Phase 4.

Reference Files

• references/resolution-algorithm.md — Normative algorithm spec (RESOLVE, LOAD_PREFS, EMIT, RENDER_RESOLUTION_TABLE, REVERSE_MAP, DETECT_CATALOG_DRIFT) + worked examples.
• references/emit-formats.md — Per-platform serialization specs with byte-for-byte examples.
• fixtures/ — Input/expected fixture pairs for each branch of the algorithm.
• sk-platform-dispatch/profiles/{tier}.json — Profile defaults, single source of truth.
• sk-pipeline-state/SKILL.md — State file schema (metadata.resolved_models).