Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/yeaight7/agent-powerups/model-routingnpx skills add yeaight7/agent-powerups --skill model-routinggit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yeaight7/agent-powerups/model-routing)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/model-routing"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/model-routing.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00036 | $0.00810 |
| Opus 5 | $0.00018 | $0.00405 |
| Sonnet 5 | $0.00007 | $0.00162 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
model-routing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Routing
Choose the right model tier before starting a task. Overusing a capable model wastes cost and context. Underusing it produces lower quality on complex work.
When to Use
- Selecting a model for a new task or subagent
- Deciding whether to escalate after a failed attempt
- Designing a multi-agent pipeline with mixed task complexity
Tier Definitions (vendor-neutral)
| Tier | Typical examples | Task profile |
|---|---|---|
| Fast | Haiku, GPT-4o-mini, Gemini Flash | Mechanical, deterministic, narrow |
| Standard | Sonnet, GPT-4o, Gemini Pro | General implementation and review |
| Deep | Opus, o1, Gemini Ultra | Architecture, security, root-cause, release |
Use your provider's current recommended model for each tier. Do not hard-code model IDs in documentation or scripts; reference tiers instead.
Routing Table
Fast tier
Use when ALL of:
- Single file or single operation
- Output is deterministic (rename, format, classify, generate boilerplate)
- No ambiguity in the task description
- Low blast radius on failure (easy to retry or revert)
Examples: rename a variable, convert a data format, generate a changelog entry, classify issue severity.
Standard tier
Use when ANY of:
- Multi-file change with known scope
- Standard implementation task (add a feature, fix a bug, write tests)
- Code review of a bounded change
- Refactor with clear before/after contract
This is the default. When unsure, use Standard.
Deep tier
Use when ANY of:
- Architecture or system design decision
- Security audit or threat model
- Root-cause analysis with no clear reproduction
- Pre-release verification across a large surface
- Task requires reasoning across many files simultaneously
- Two Standard attempts failed with no clear progress
Do not use Deep speculatively. It is expensive and slower.
Escalation Rule
Try Standard first. Escalate to Deep only after Standard fails with a clear reasoning gap — not just a wrong answer. A wrong answer from Standard often means the task needs more context, not a more capable model.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 96 lines · 36 tokens per session scan A a7870fb7421e
model-routing is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 36 tokens to every session and 810 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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github-code-review
Review PRs: diffs, inline comments via gh or REST.
simplify-code
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skill-authoring
Author SKILL.md: frontmatter, structure, writing principles.
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.
academic-paper-review
Structured peer-review of academic papers.