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/tsarihan/model-council-mcp-codex/statusgit clone --depth 1 https://github.com/tsarihan/model-council-mcp-codexWrote 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/tsarihan/model-council-mcp-codex/status)<a href="https://agentmods.dev/commands/tsarihan/model-council-mcp-codex/status"><img src="https://agentmods.dev/badge/commands/tsarihan/model-council-mcp-codex/status.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 | $0.00017 | $0.00327 |
| Opus 5 | $0.00009 | $0.00163 |
| Sonnet 5 | $0.00003 | $0.00065 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
status 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 5d 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
Call the council_status tool and present the result clearly and concisely:
- Detected environment: local Ollama models + whether Ollama cloud is reachable on this plan; whether Claude and Codex are installed and logged in; whether Grok CLI is installed but fail-closed. Do not imply normal operation verifies Grok login.
- Council: the current members grouped by provider (local Ollama / Ollama cloud / Claude / Codex / Grok), with the total count.
- Tiers & concurrency: the resolved subscription tiers and the per-provider concurrency limits. If
reloadPendingis true, note that a/reload-pluginsis needed to apply a recent tier change. - Quota: surface the quota warning verbatim — these members run under the user's own subscription quotas.
- Hints: relay any hints (e.g. how to log a CLI in) so the user can fix anything not usable.
Do not call any other tool unless the user asks to change something (then point them at /model-council:setup or configure_council).
Write model IDs in full and verbatim as they appear in council_status — the Codex members are gpt-5.6-sol, gpt-5.6-luna, and gpt-5.6-terra; never abbreviate them to sol / luna / terra.
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.
- 5d ago First seen · 16 lines · 17 tokens per session scan A 0e64b4dc6765
status is a command published in the GitHub repository tsarihan/model-council-mcp-codex (0 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 17 tokens to every session and 327 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.