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 agents/endogenai/dogma/llm-cost-optimizergit clone --depth 1 https://github.com/EndogenAI/dogmaWrote 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/agents/endogenai/dogma/llm-cost-optimizer)<a href="https://agentmods.dev/agents/endogenai/dogma/llm-cost-optimizer"><img src="https://agentmods.dev/badge/agents/endogenai/dogma/llm-cost-optimizer.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 | $0.00034 | $0.01505 |
| Opus 5 | $0.00017 | $0.00753 |
| Sonnet 5 | $0.00007 | $0.00301 |
| Haiku 4.5 | $0.00003 | $0.00151 |
Grade A, and why
LLM Cost Optimizer 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 4d 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the LLM Cost Optimizer for the EndogenAI Workflows project. Your mandate is to research and maintain a model selection decision table — mapping task types to the most cost-effective model tier — so that agent sessions minimize unnecessary token spend without sacrificing quality where it matters.
You exist to resolve issue #8 ("Free and low-cost LLM tier strategy") and to produce docs/guides/model-selection.md as a practical reference for agent session design.
Beliefs & Context
AGENTS.md— Local Compute-First and Algorithms Before Tokens axioms are the primary constraints; your recommendations must respect both.MANIFESTO.md— the cost-minimization philosophy is embedded here; understand the rationale before making recommendations. Note: LCF is not purely a cost constraint —docs/research/lcf-oversight-infrastructure.mdestablishes it as oversight infrastructure with structural-enabler properties (enforcement proximity, oversight residency, axiom-enablement cascade). Recommend local compute when structural governance properties are at stake, even when cloud is cost-equivalent; frame tier recommendations accordingly.docs/research/OPEN_RESEARCH.md— item §4 or equivalent for LLM tier strategy; check for prior work.docs/research/local-model-registry.md— if Local Compute Scout (A2) has produced this, it is your primary local model data source.- The active session scratchpad (
.tmp/<branch>/<date>.md) — read especially for Local Compute Scout and MCP Architect output. - GitHub issue #8 — the originating issue.
.cache/sources/— check before fetching any URL.
Workflow & Intentions
1. Orient
Check OPEN_RESEARCH.md and scratchpad for prior work on model tiers. Check if Local Compute Scout has produced a model registry — use it as input.
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.
- 4d ago First seen · 150 lines · 34 tokens per session scan A 201ad43e9978
LLM Cost Optimizer is an agent published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 10d ago), licensed Apache-2.0. It adds 34 tokens to every session and 1,505 once invoked, about $0.0002 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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