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 skills add digital-stoic-org/agent-skills --skill pick-modelgit clone --depth 1 https://github.com/digital-stoic-org/agent-skillsWrote 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/digital-stoic-org/agent-skills/pick-model)<a href="https://agentmods.dev/skills/digital-stoic-org/agent-skills/pick-model"><img src="https://agentmods.dev/badge/skills/digital-stoic-org/agent-skills/pick-model/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/digital-stoic-org/agent-skills/pick-model"><img src="https://agentmods.dev/badge/skills/digital-stoic-org/agent-skills/pick-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 4 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00107 | $0.01369 |
| Opus 5 | $0.00053 | $0.00685 |
| Sonnet 5 | $0.00021 | $0.00274 |
| Haiku 4.5 | $0.00011 | $0.00137 |
Grade A, and why
pick-model 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 8d 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pick Model
Take the user's intended prompt as input. Do NOT execute it. Read the live session state, classify the prompt, emit verdict + delta + strategy: is the current model right, and if not, is switching worth the cache cost.
CLAUDE.md contract: this skill is the single source of truth for the model/effort routing call. CLAUDE.md "Execution defaults" carries no model table — it defers here by capability (auto-discovery on the description). Don't re-derive or duplicate the tier table elsewhere; if routing changes, it changes here.
Two levers, different cost (the core call):
- 🎚️ Effort change, same model → cache SURVIVES (cheap). Recommend freely.
- 🔀 Model switch → cache BREAKS, context re-read uncached (costly). Must beat the switch penalty.
A third lever — parallelism (linear vs fan-out, sub-agents vs Workflow) — is out of scope here. To design a skill/agent's per-step execution topology (and have it call this skill per step), use
/pick-workflow.
Recognize-then-route: hit the right tier directly; reserve top-tier (Opus high / Fable) for ambiguous/big/can't-classify. Effort = output-spend, not input. See reference.md for principles, routing detail, escalators, examples.
Workflow
1. Read live state
pick-model-state
Emits model= ctx_tokens= ctx_pct= cache_hit_pct= cost_usd= duration_min=
(jq + path resolution live in dstoic/scripts/pick-model-state, on PATH). Bare
call — no shell expansion, so it's allowlistable and won't trigger a permission
prompt. Don't compute these by hand. Fallback (prints state=missing, or
command not found pre-sync): read model + ctx% from the statusline bar or ask
user; note ⚠️ estimated state, never guess silently.
2. Classify + pick ideal model + effort
Match prompt to tier. Apply escalators (cap +1 tier). See cheatsheet + routing table in reference.md.
| Tier | Model | Effort | Shape |
|---|---|---|---|
| Chore | 🟢 Haiku 4.5 | none | convert/format/extract/typo/lookup |
| Plumbing/standard | 🟡 Sonnet 5 | low–high (default medium) |
GTD/commit, single-file code, content, review, agentic tool use, light multi-file |
| Thinking | 🔴 Opus 4.8 | high→xhigh (⚠️ defaults high) |
strategy, hard 3+ file refactor, architecture, security/audit, PhD-reasoning |
| Boulder | 🟣 Fable 5 | adaptive | multi-day, >200K context, sustained ambiguity |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 87 lines · 107 tokens per session scan A b915294ba8d2
pick-model is a skill published in the GitHub repository digital-stoic-org/agent-skills (20 stars, last pushed 2d ago), licensed MIT. It adds 107 tokens to every session and 1,369 once invoked, about $0.0005 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 2026-08-30.
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