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 skills/keyargo/custodian-kernel/openai-completenpx skills add KeyArgo/custodian-kernel --skill openai-completegit clone --depth 1 https://github.com/KeyArgo/custodian-kernelWrote 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/keyargo/custodian-kernel/openai-complete)<a href="https://agentmods.dev/skills/keyargo/custodian-kernel/openai-complete"><img src="https://agentmods.dev/badge/skills/keyargo/custodian-kernel/openai-complete.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 | $0.00014 | $0.00329 |
| Opus 5 | $0.00007 | $0.00164 |
| Sonnet 5 | $0.00003 | $0.00066 |
| Haiku 4.5 | $0.00001 | $0.00033 |
Grade A, and why
openai-complete 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 yesterday.
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.
This is a copy
81% identical to discord-webhook — 16 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Openai Complete
Send a completion request to the OpenAI API
Authority band
This tool runs under L1 authority in the Custodian kernel. Trivial autonomous spend or free side-effect — no human approval required.
Inputs
| Parameter | Type | Required | Description |
|---|---|---|---|
| (see execute.py for full schema) |
Configuration
Set the required environment variables before use. Until configured, custodian tools run openai-complete returns a stub response indicating which variables are needed.
Usage
custodian tools run openai-complete --param value
Custodian governance
Every call to this tool passes through the Custodian kernel authority check before executing. The kernel verifies the current authority band, checks spending caps where applicable, logs the action to the OCSF audit trail, and escalates to a human operator if the action exceeds the declared band.
Adding this tool to any Hermes agent session requires no code changes —
declare custodian-band: L1 in the SKILL.md frontmatter and the kernel
wraps it automatically.
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.
- yesterday First seen · 51 lines · 14 tokens per session scan A 35d21fa5e5f3
openai-complete is a skill published in the GitHub repository KeyArgo/custodian-kernel (118 stars, last pushed 13d ago), licensed MIT. It adds 14 tokens to every session and 329 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 81% identical to discord-webhook, differing in 16 lines, and is treated as a copy.
Other skills, from other repositories
implementing-llm-guardrails-for-security
Implements input and output validation guardrails for LLM-powered applications to prevent prompt injection, data leakage, toxic content generation, and hallucinated outputs. Builds a security validation pipeline using NVIDIA NeMo Guardrails Colang definitions, custom Python validators for PII detection and content…
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
cordis-plugin-sofagent-inject
启动注入企业约束——四层加载链（seam: apply(ctx)）——桥接 @sofagent/harness buildConstrainedSystemPrompt（sofagent 品牌插件 · 主色 #16B8F3）——DSH（DeepSeek Harness）cordis plugin。sofagent 约束层在 DeepSeek Harness 生态的插件形态。.
gnt-check-action
Check a side-effectful action against this organization's approved gnt rules before taking it, and stop on a blocked or needshuman verdict.
dayan-prompt-factory
Compile a vague recurring request into a reusable prompt contract with fixtures and evaluation.
context-optimization
Use when prompt cost is too high, latency is above threshold, or context window limits are being approached. Requires measurement before and after each reduction. Blocks "I shortened the prompt so it should be cheaper" completions.