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 cintia09/CodeNook --skill model-probegit clone --depth 1 https://github.com/cintia09/CodeNookWrote 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/cintia09/codenook/model-probe)<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.
<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>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 | $0.00000 | $0.00697 |
| Opus 5.5 | $0.00000 | $0.00279 |
| Sonnet 5.5 | $0.00000 | $0.00139 |
| Haiku 4.5 | $0.00000 | $0.00070 |
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.
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 — 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)
- Runtime API — Claude Code
list_models()/ Copilot CLI registry. M1: not implemented; falls through to source 2. CODENOOK_AVAILABLE_MODELSenv var — comma-separated model ids.- 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": [...] }
}
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.
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.
- 2mo ago First seen · 68 lines · 0 tokens per session scan A 1563bffa1d8c
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.
model-probe compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| model-probecintia09/CodeNook | Skill | 5 | 1mo ago | MIT | A | 0 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 2d ago | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | 2d ago | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 315k | 5d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |