Big-AGI is an open-source workspace for using multiple AI models through chat and other AI functions. It is intended for engineers, founders, researchers, and other users who want to work with AI personas, model comparisons, image generation, voice, documents, and code-related features. The catalogue entries provide commands, instructions, and a skill for working with Big-AGI.
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 commands/enricoros/big-agi/verify-parametersgit clone --depth 1 https://github.com/enricoros/big-AGIWrote 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/enricoros/big-agi/verify-parameters)<a href="https://agentmods.dev/commands/enricoros/big-agi/verify-parameters"><img src="https://agentmods.dev/badge/commands/enricoros/big-agi/verify-parameters.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 | $0.00010 | $0.00989 |
| Opus 5 | $0.00005 | $0.00495 |
| Sonnet 5 | $0.00002 | $0.00198 |
| Haiku 4.5 | $0.00001 | $0.00099 |
Grade A, and why
verify-parameters 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 2d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify LLM Parameters
Compare model parameterSpecs in definition files against API-validated sweep data.
If $ARGUMENTS provided, verify only that dialect, which includes reading the pair of sweep results and model defintions. Otherwise verify all five, and read the pairs in sequence.
Files
Sweep results (source of truth for select parameters):
tools/develop/llm-parameter-sweep/llm-{dialect}-parameters-sweep.jsonBy the time you see these files, the repo owner has already updated them viatools/develop/llm-parameter-sweep/sweep.sh(very long running, 15 min per vendor).
Model definitions (source of truth for model defintions for the user and application, including constants, interfaces, supported parameters and sometimes allowed parameter values):
- OpenAI:
src/modules/llms/server/openai/models/openai.models.ts - Anthropic:
src/modules/llms/server/anthropic/anthropic.models.ts - Gemini:
src/modules/llms/server/gemini/gemini.models.ts - xAI:
src/modules/llms/server/openai/models/xai.models.ts - Meta AI:
src/modules/llms/server/openai/models/metaai.models.ts
Task
The sweep data is the source of truth for allowed model parameter values or value ranges, and for the fn function-calling capability probe.
For each model in the sweep, verify the model definition exposes exactly those capabilities - no more, no less. This includes:
- The parameter is present in parameterSpecs
- The paramId variant covers exactly the values from the sweep, if applicable
LLM_IF_OAI_Fnininterfacesmatches"roundtrip"in the sweep'sfnarray (see below)- etc.
Report models where the definition doesn't match the sweep.
Parameter Mapping
Example parameter mapping. Note that new parameters may have been added to both the definition, and the sweep. The objective of the sweep is to hint at model definition values, but the model definitions are what matters for Big-AGI, and need to be carefully updated, otherwise thousands of clients may break.
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.
- 2d ago Changed · +3 lines cbb786d3ccd7
- 6d ago First seen · 68 lines · 10 tokens per session scan A 1be1cec859a2
verify-parameters is a command published in the GitHub repository enricoros/big-AGI (7,111 stars, last pushed 2d ago), licensed MIT. It adds 10 tokens to every session and 989 once invoked, about $0.0001 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.
Other commands, from other repositories
AGENTS
Per-command agent-optimized usage and structure guide.
netllm-connect
Wire Cursor, Claude Code, Codex, or Honcho to the local netllm router.
post-claims
Follow the Post Claims SOP in docs/sops/POSTCLAIMSSOP.md.
forge
You are in FORGE MODE. Execute the following.
apex-architect
Use the @apex-architect agent to help the user with the following architectural or debugging matter: $ARGUMENTS.
echo-analyst
Use the @echo-analyst agent to surface requirement gaps and assumptions for the following: $ARGUMENTS.