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 skills add andrewyng/context-hub --skill new-projectgit 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/new-project)<a href="https://agentmods.dev/skills/andrewyng/context-hub/new-project"><img src="https://agentmods.dev/badge/skills/andrewyng/context-hub/new-project.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Data Exfiltration · line 462 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 8d 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" \ Copies of this mod
1 near-identical copy found in the catalogue:
- new-project — 100% identical, 0 lines differ
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.
- 8d ago First seen · 642 lines · 29 tokens per session scan A 7122b3a9acae
new-project is a skill published in the GitHub repository andrewyng/context-hub (13,965 stars, last pushed 3mo 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). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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