model-probe

model-probe is a skill for Claude Code, Codex from cintia09/CodeNook. It costs 0 tokens per session (697 once invoked), scanned A, original, MIT.

A built-in skill that discovers available language models and groups them into strong, balanced, and cheap tiers.

In plain words
What is it for?
It is for probing model availability, using environment or fallback model lists, assigning tiers, and writing a model catalogue.
Why use it?
It lets configuration use tier names while the skill records which concrete models are available in the current runtime.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions Claude Code.

Good fit It is for probing model availability, using environment or fallback model lists, assigning tiers, and writing a model catalogue.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/cintia09/codenook/model-probe
Install

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.

Any agent
npx skills add cintia09/CodeNook --skill model-probe
Clone the repo
git clone --depth 1 https://github.com/cintia09/CodeNook

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for model-probe

README.md
[![agentmods](https://agentmods.dev/badge/skills/cintia09/codenook/model-probe/github.svg)](https://agentmods.dev/skills/cintia09/codenook/model-probe)
Your own site
<a href="https://agentmods.dev/skills/cintia09/codenook/model-probe"><img src="https://agentmods.dev/badge/skills/cintia09/codenook/model-probe/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.

agentmods 80×15 button for model-probe

Your own site · 80×15
<a href="https://agentmods.dev/skills/cintia09/codenook/model-probe"><img src="https://agentmods.dev/badge/skills/cintia09/codenook/model-probe.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 697 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.00697
Opus 5.5 $0.00000 $0.00279
Sonnet 5.5 $0.00000 $0.00139
Haiku 4.5 $0.00000 $0.00070

Measured 2mo ago against content hash 1563bffa1d8c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

model-probe 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 2mo ago.

The scan reads SKILL.md. This mod also ships 2 executable files (_probe.py, probe.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/codenook-core/skills/builtin/model-probe/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

model-probe (builtin skill)

Role

Discover available LLM models in the current runtime, classify each into the three tiers (strong / balanced / cheap), and write the resulting catalog so config-resolve can expand tier_* symbols. Implements architecture §3.2.4.2 (Model Discovery & Tiering).

Triggers

  • init.sh --refresh-models (manual)
  • init.sh --install-plugin / --upgrade-core (auto on install events)
  • Workspace catalog older than ttl_days (auto-refresh in M2+)
  • Main session "刷新模型" natural-language command (M2+)

Probe sources (in order)

  1. Runtime API — Claude Code list_models() / Copilot CLI registry. M1: not implemented; falls through to source 2.
  2. CODENOOK_AVAILABLE_MODELS env var — comma-separated model ids.
  3. Builtin fallback — minimum viable triple (opus-4.7,sonnet-4.5,haiku-4.5).

The runtime label is set to "env", "builtin-fallback", or one of the runtime names as appropriate.

CLI

probe.sh                                       print catalog JSON to stdout
probe.sh --output <file>                       write catalog JSON to <file>
probe.sh --tier-priority <yaml_file>           override built-in priority
probe.sh --check-ttl <file> --ttl-days <int>   exit 0 if fresh, 1 if stale

Any catastrophic probe error (e.g. unreadable --tier-priority file) → stderr starts with probe failed: and exits non-zero.

Tier classification

Each model id is matched against tier_priority (user-supplied or built-in). The model's tier is the first priority bucket containing it. resolved_tiers.<tier> is the first id from tier_priority[tier] that is also in available.

Built-in tier_priority mirrors implementation.md §3.5.1.2:

strong:   [opus-4.7, opus-4.6, sonnet-4.6, gpt-5.4]
balanced: [sonnet-4.6, sonnet-4.5, gpt-5.4, gpt-5.4-mini]
cheap:    [haiku-4.5, gpt-5.4-mini, gpt-4.1, sonnet-4.5]

Output schema

{
  "refreshed_at": "ISO-8601 UTC",
  "ttl_days": 30,
  "runtime": "env | builtin-fallback | claude-code | copilot-cli",
  "available": [
    {"id": "opus-4.7", "tier": "strong", "cost": "high", "provider": "anthropic"}
  ],
  "resolved_tiers": { "strong": "opus-4.7", "balanced": "sonnet-4.6", "cheap": "haiku-4.5" },
  "tier_priority": { "strong": [...], "balanced": [...], "cheap": [...] }
}

Read the full file on GitHub · 68 lines

Files

What ships with it

2 files 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.

Changes

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.

  1. 2mo ago First seen · 68 lines · 0 tokens per session scan A 1563bffa1d8c

Subscribe to this mod's changes

model-probe is a skill published in the GitHub repository cintia09/CodeNook (5 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 697 tokens. 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.

Related

model-probe compared with skills from other repositories

model-probe next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
model-probecintia09/CodeNookSkill51mo agoMITA0Claude Code, Codex
imagegenopenai/codexSkill134ktodayApache-2.0A113Codex
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79k2d agoMITA200Claude Code, Codex
playwright-climicrosoft/playwrightSkill100k2d agoApache-2.0A19Claude Code
browser-automationopenclaw/openclawSkill394ktodayMITA36Claude Code
systematic-debuggingobra/superpowersSkill315k5d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143ktodayMITA95Claude Code