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 jeet129/praxis --skill adaptive-model-routinggit clone --depth 1 https://github.com/jeet129/praxisWrote 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/jeet129/praxis/adaptive-model-routing)<a href="https://agentmods.dev/skills/jeet129/praxis/adaptive-model-routing"><img src="https://agentmods.dev/badge/skills/jeet129/praxis/adaptive-model-routing/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/jeet129/praxis/adaptive-model-routing"><img src="https://agentmods.dev/badge/skills/jeet129/praxis/adaptive-model-routing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 301 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 303 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 310 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 311 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00132 | $0.04055 |
| Opus 5 | $0.00066 | $0.02027 |
| Sonnet 5 | $0.00026 | $0.00811 |
| Haiku 4.5 | $0.00013 | $0.00405 |
Grade A, and why
adaptive-model-routing 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 9d 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 — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adaptive Model Routing (Codex variant)
capability: foundation
domain: cross-cutting
state: active
dependencies:
- using-praxis
- llm-cost-optimization
- agentic-architecture
triggers:
- "about to launch a specialist Codex session — which reasoning effort should it use?"
- "starting a new session — which model_reasoning_effort should I launch on?"
- "prior attempt failed — should I escalate the reasoning tier?"
- "deciding whether this task needs high reasoning or medium is enough"
- "API spend running high — can I safely use medium reasoning here?"
- "complex architecture decision coming — what reasoning tier?"
- "entering a new workflow phase — should the reasoning tier change?"
outputs:
- reasoning-effort selection (per session or specialist launch)
- complexity score + rationale
- escalation recommendation (when prior attempt fails)
- routing log entry at .project/telemetry/model-routing.jsonl
consumers:
- delivery-lead (primary — runs this SKILL before every specialist launch)
- using-praxis (consumes routing decision when selecting reasoning effort for orchestration)
- agentic-architecture (session-as-agent configuration)
references:
- llm-cost-optimization.md
- routing-examples.md
The Codex variant of adaptive-model-routing. Same underlying routing rubric as the Claude Code version; adapted to Codex CLI's model_reasoning_effort parameter and session-as-agent spawn pattern.
What differs from the Claude Code variant
Codex resolves tiers via model_reasoning_effort in codex-agents/*.toml (high / medium / low) on typically one base model; agents run as launched CLI sessions (no mid-session model switch — relaunch with the new config), and cost is per-token API spend rather than plan quota. Everything else — the 5-signal rubric, fast-path rules, phase defaults, per-agent assignments, escalation protocol — is identical model-agnostic discipline shared with every harness.
What ships with it
1 file 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.
- 9d ago First seen · 314 lines · 132 tokens per session scan A 87c65a8cffd4
adaptive-model-routing is a skill published in the GitHub repository jeet129/praxis (7 stars, last pushed 5d ago), licensed MIT. It adds 132 tokens to every session and 4,055 once invoked, about $0.0007 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.
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