agentsclimarketplace

Calibrate model routing

Skill lemun/dispatch-compiler/calibrate-model-routing

Refresh the shared Codex and Claude model-routing calibration from current official provider documentation. Use when the user explicitly asks to calibrate, refresh, or update model and effort recommendations, especially after OpenAI or Anthropic announces model, effort, alias, availability, or guidance changes.From its SKILL.md

Install
npx -y skills add lemun/dispatch-compiler --skill calibrate-model-routing

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.8 KB, 925 tokens by cl100k_base, as published. Nobody here has run it

Calibrate Model Routing

Refresh volatile provider guidance without changing existing work packets.

Scope

Refresh both providers unless the user names only Codex or only Claude. Keep their recommendations independent. Never recommend one provider over another.

Resolve the snapshot path from MODEL_ROUTING_CALIBRATION_PATH; otherwise use ~/.agents/model-routing/calibration.md.

Preflight

Before researching or writing anything:

  1. Set CALIBRATION_HELPER to the absolute path of scripts/calibration_snapshot.py, resolved from the directory containing this SKILL.md. Do not assume the current working directory is the skill directory.
  2. Run python3 "$CALIBRATION_HELPER" status against the resolved snapshot path.
  3. Read the existing snapshot before research when status is fresh or stale. Record both verification dates and retain the exact provider section bytes so an incomplete refresh can preserve them unchanged.

When status is missing, continue research but treat this as a first-snapshot run. When status is invalid, report the validation error and stop before writing; do not build on an untrusted snapshot.

Research

Use current official sources only.

  • Codex: official openai.com and chatgpt.com documentation. Prefer the OpenAI documentation capability when the active client provides it.
  • Claude: official anthropic.com and claude.com documentation, including Claude Code model configuration and Claude Platform model guidance.

For each requested provider, verify:

  • current model names and stable IDs or aliases when useful
  • client, account, plan, or cloud-provider availability constraints
  • daily workhorse, frontier, long-horizon or exceptional, and helper tiers
  • supported effort levels, defaults, and provider guidance
  • whether increased model capability or increased effort addresses the failure
  • all official source URLs used

If official sources conflict or do not support a complete provider section, preserve the existing section and explain the uncertainty.

Build the Candidate

Use this exact provider-section schema:

## <Provider>

- Verified at: YYYY-MM-DD
- Official sources:
  - https://official.example/path
- Client or account constraints: <concise current constraint>
- Workhorse: <model, effort, and task fit>
- Frontier: <model, effort, and task fit>
- Long-horizon or exceptional tier: <model, effort, and task fit>
- Helper or high-volume tier: <model, effort, and task fit>
- Effort guidance: <when to raise or lower effort>
- Routing notes: <when to change models>

When creating the first snapshot, assemble both provider sections beneath:

---
schema_version: 1
stale_after_days: 30
---

# Model routing calibration

Do not write model rumors, cross-provider rankings, packet examples, or long rationales into the snapshot.

Validate and Install

When a valid snapshot already exists, evaluate requested providers independently. For a normal two-provider refresh, apply only successfully completed provider sections and preserve each incomplete provider section byte-for-byte. Save each completed section to its own temporary file and run replace-provider once per completed provider:

python3 "$CALIBRATION_HELPER" replace-provider \
  --provider codex|claude \
  --input <provider-section-file>

If one provider validates and the other provider's evidence is incomplete, install only the validated provider section with replace-provider; do not construct or install a full-snapshot candidate that rewrites the incomplete section.

For a first snapshot, install only after both complete provider sections validate together. Never install a partial first snapshot. If either provider is incomplete, write nothing and report that both complete provider sections are required. Once both are complete, save the full candidate to a temporary file and run:

python3 "$CALIBRATION_HELPER" install --input <candidate-file>

The helper validates official domains and writes atomically. If it rejects the candidate, correct the evidence-backed defect or preserve the old snapshot. Never weaken validation to force an update through.

Report

Report:

  • provider sections refreshed
  • previous and new verification dates
  • model or effort guidance that materially changed
  • sections preserved because evidence was incomplete
  • snapshot path

Do not modify existing packets, create a schedule, or start packet generation.

What ships with it: 2 files

10.9 KB alongside SKILL.md, 1 of them executable

agents/

scripts/

Keep looking

Skills are one crate of 326,144. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.