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 nrl-ai/chub --skill new-projectgit clone --depth 1 https://github.com/nrl-ai/chubWrote 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/nrl-ai/chub/new-project)<a href="https://agentmods.dev/skills/nrl-ai/chub/new-project"><img src="https://agentmods.dev/badge/skills/nrl-ai/chub/new-project/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/nrl-ai/chub/new-project"><img src="https://agentmods.dev/badge/skills/nrl-ai/chub/new-project.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00029 | $0.04915 |
| Opus 5 | $0.00015 | $0.02457 |
| Sonnet 5 | $0.00006 | $0.00983 |
| Haiku 4.5 | $0.00003 | $0.00492 |
Grade A, and why
new-project scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST "https://app.olakai.ai/api/monitoring/prompt" \ This is a copy
100% identical to new-project — 0 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.
How it starts
The opening of the file, as written. The whole thing — 642 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Build a New AI Agent Project with Olakai
This skill guides you through creating a new AI agent that is fully integrated with Olakai for analytics, KPI tracking, and governance.
Prerequisites
Before starting, ensure:
- Olakai CLI installed:
npm install -g olakai-cli - CLI authenticated:
olakai login - API key for SDK (generated per-agent via CLI — see Step 2.2)
Why Custom KPIs Are Essential
Olakai's core value is tracking business-specific KPIs for your AI agents. Without KPIs, you're tracking events without gaining actionable insights.
What you can measure with KPIs:
- Business outcomes (items processed, success rates, revenue impact)
- Operational data (step counts, retry rates, execution time)
- Quality indicators (error rates, user satisfaction signals)
Without KPIs configured:
- No dashboard KPIs beyond basic token counts
- No aggregated performance views
- No alerting thresholds
- No ROI calculations
Every agent should have 2-4 KPIs that answer: "How do I know this agent is performing well?"
KPIs created here belong to this specific agent only. If you later create additional agents, each one needs its own KPI definitions — KPIs cannot be shared or reused across agents.
Understanding the customData to KPI Pipeline
Before diving into implementation, understand how data flows through Olakai:
SDK customData → CustomDataConfig (Schema) → Context Variable → KPI Formula → kpiData
How It Works
- customData (SDK): Raw JSON you send with each event
- CustomDataConfig (Platform): Schema defining which fields are processed
- Context Variables: CustomDataConfig fields become available for formulas
- KPI Formula: Expression that computes a value (e.g.,
SuccessRate * 100) - kpiData (Response): Computed KPI values returned with each event
Critical Rules
| Rule | Consequence |
|---|---|
| Only CustomDataConfig fields become variables | Unregistered customData fields are NOT usable in KPIs |
| Formula evaluation is case-insensitive | stepCount, STEPCOUNT, StepCount all work in formulas |
| NUMBER configs need numeric values | Don't send "5" (string), send 5 (number) |
| KPIs are unique per agent | Each KPI belongs to exactly one agent — create separately for each |
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 · 642 lines · 29 tokens per session scan A 7122b3a9acae
new-project is a skill published in the GitHub repository nrl-ai/chub (11 stars, last pushed 5mo ago), licensed MIT. It adds 29 tokens to every session and 4,915 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to new-project, differing in 0 lines, and is treated as a copy.
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