Context Hub is a repository and command-line tool that supplies coding agents with curated, versioned documentation and skills in markdown form. It is for agents that need accurate API information across tasks, and the catalogue entries provide skills for searching, fetching, and improving that context.
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/andrewyng/context-hub/integratenpx skills add andrewyng/context-hub --skill integrategit clone --depth 1 https://github.com/andrewyng/context-hubWrote 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/andrewyng/context-hub/integrate)<a href="https://agentmods.dev/skills/andrewyng/context-hub/integrate"><img src="https://agentmods.dev/badge/skills/andrewyng/context-hub/integrate.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 | $0.00030 | $0.04619 |
| Opus 5 | $0.00015 | $0.02309 |
| Sonnet 5 | $0.00006 | $0.00924 |
| Haiku 4.5 | $0.00003 | $0.00462 |
Grade A, and why
integrate 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- integrate — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 661 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Integrate Olakai into Existing AI Code
This skill guides you through adding Olakai monitoring to an existing AI agent or LLM-powered application with minimal code changes.
For full SDK documentation, see: https://app.olakai.ai/llms.txt
Prerequisites
- Existing working AI agent/application using OpenAI, Anthropic, or other LLM
- Olakai CLI installed and authenticated (
npm install -g olakai-cli && olakai login) - Olakai API key for your agent (get via CLI:
olakai agents get AGENT_ID --json | jq '.apiKey') - Node.js 18+ (for TypeScript) or Python 3.7+ (for Python)
Note: Each agent can have its own API key. Create one with
olakai agents create --name "Name" --with-api-key
Why Custom KPIs Are Essential
Adding monitoring is only the first step. The real value of Olakai comes from tracking custom KPIs specific to your agent's business purpose.
Without KPIs configured:
- Only basic token counts and request data
- No aggregated business KPIs on dashboard
- No alerting capabilities
- No ROI tracking
With KPIs configured:
- Custom KPIs (items processed, success rates, quality scores)
- Trend analysis and performance dashboards
- Threshold-based alerting
- Business value calculations
Plan to configure at least 2-4 KPIs that answer: "How do I know this agent is performing well?"
KPIs are unique per agent. If adding monitoring to an agent that needs the same KPIs as another already-configured agent, you must still create new KPI definitions for this agent. KPIs cannot be shared or reused across agents.
Understanding the customData to KPI Pipeline
Before adding monitoring, understand how custom data flows through Olakai:
SDK customData → CustomDataConfig (Schema) → Context Variable → KPI Formula → kpiData
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) |
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 · 661 lines · 30 tokens per session scan A afdfd996e77e
integrate is a skill published in the GitHub repository andrewyng/context-hub (13,960 stars, last pushed 3mo ago), licensed MIT. It adds 30 tokens to every session and 4,619 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 2026-08-30.
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