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/patrickdappollonio/claude-plugins/use-premium-models-efficientlynpx skills add patrickdappollonio/claude-plugins --skill use-premium-models-efficientlygit clone --depth 1 https://github.com/patrickdappollonio/claude-pluginsWrote 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/patrickdappollonio/claude-plugins/use-premium-models-efficiently)<a href="https://agentmods.dev/skills/patrickdappollonio/claude-plugins/use-premium-models-efficiently"><img src="https://agentmods.dev/badge/skills/patrickdappollonio/claude-plugins/use-premium-models-efficiently.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 5.1 | $0.00078 | $0.01049 |
| Opus 5 | $0.00039 | $0.00524 |
| Sonnet 5 | $0.00016 | $0.00210 |
| Haiku 4.5 | $0.00008 | $0.00105 |
Grade A, and why
use-premium-models-efficiently 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 6d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use Premium Models Efficiently
The premium model is the orchestrator, architect, synthesizer, and final judge. Cheaper subagents do the token-heavy research, coding, testing, and summarization that doesn't require its full judgment.
flowchart TB
input["User goal<br>repo context<br>constraints"] --> premium["Premium model<br>orchestrator + judge<br>plan, tradeoffs, synthesis"]
premium --> result["Integrated result<br>final review<br>user answer + next action"]
premium <--> research["Lighter agents<br>research scans<br>docs, APIs, repo map"]
premium <--> coding["Lighter agents<br>bounded coding<br>patches, refactors"]
premium <--> testing["Lighter agents<br>testing passes<br>scripts, browser, logs"]
Which Models Are Which
The split is by relative cost within whatever provider you're running, not by brand. As of mid-2026:
| Provider | Premium (orchestrator + judge) | Cheaper (subagents) |
|---|---|---|
| Anthropic | Claude Fable, Claude Opus | Claude Sonnet, Claude Haiku |
| OpenAI | GPT-5.6 Sol | GPT-5.6 Terra, GPT-5.6 Luna |
Model families evolve; when these names are stale, apply the same rule — the most expensive available model takes the judgment seat, the cheaper tiers take the bounded heavy lifting.
Keep with the Premium Model
- Decomposing ambiguous work into clean parallel slices.
- Architecture, product, and safety tradeoffs.
- Reading conflicting subagent reports and deciding what matters.
- Integrating partial implementations into one coherent result.
- Final review, risk assessment, and user-facing synthesis.
Delegate to Cheaper Subagents
- Spot the heavy lifting — you can't predict token counts, but you can recognize the shapes that always burn them: large repo search, long logs, broad docs, repetitive edits.
- Split independent work into subagents before reading everything yourself.
- Use cheaper models for research scans, inventory, search summaries, narrow bug hunts, browser/testing passes, test-output reduction, and bounded code edits.
- Ask subagents for concise evidence: files, line references, commands run, diffs, uncertainties, and stop conditions they hit.
- Spend premium tokens on the decision layer: compare results, resolve conflicts, choose the implementation path, review the final patch.
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.
- 6d ago First seen · 101 lines · 78 tokens per session scan A bddeebfd2446
use-premium-models-efficiently is a skill published in the GitHub repository patrickdappollonio/claude-plugins (8 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 1,049 once invoked, about $0.0004 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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