Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add pealmeida/anymodel-plugin/plugin install anymodelWrote 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/commands/pealmeida/anymodel-plugin/models)<a href="https://agentmods.dev/commands/pealmeida/anymodel-plugin/models"><img src="https://agentmods.dev/badge/commands/pealmeida/anymodel-plugin/models/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/commands/pealmeida/anymodel-plugin/models"><img src="https://agentmods.dev/badge/commands/pealmeida/anymodel-plugin/models.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 | $0.00012 | $0.00167 |
| Opus 5.5 | $0.00005 | $0.00067 |
| Sonnet 5.5 | $0.00002 | $0.00033 |
| Haiku 4.5 | $0.00001 | $0.00017 |
Grade A, and why
models scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Run:
node "${CLAUDE_PLUGIN_ROOT}/scripts/companion.mjs" models "$ARGUMENTS"
Present the full command output to the user exactly as returned. Do not summarize or condense it. Preserve all details including:
- Each provider's endpoint, auth status, and reachable
/modelslist - Which models each key can actually invoke, per provider
- Plan-verification results built into the probe (capability vs. wire-format checks)
- Any error messages or unreachable endpoints
- Follow-up commands such as
/anymodel:setupand/anymodel:delegate
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.
- 1mo ago First seen · 19 lines · 12 tokens per session scan A 927e9c8cc373
models is a command published in the GitHub repository pealmeida/anymodel-plugin (0 stars, last pushed yesterday), licensed Apache-2.0. It adds 12 tokens to every session and 167 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
ingest
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mega-status
Show detailed status of mega-plan execution including feature progress and story completion. Usage: /plan-cascade:mega-status.
mega-edit
Edit the mega-plan interactively. Add, remove, or modify features. Usage: /plan-cascade:mega-edit.
hybrid-manual
Load an existing PRD file and enter review mode. Supports JSON format with stories, priorities, dependencies, and acceptance criteria.
optimize-prompt
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles: common words tokenize more efficiently, unusual words break into more tokens, and conciseness reduces cost.
prompt-formatter
Reformat a raw prompt using XML tags to clearly delimit each structural layer of meaning.