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 Maximilien-ai/clawmax --skill luma-event-insightsgit clone --depth 1 https://github.com/Maximilien-ai/clawmaxWrote 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/maximilien-ai/clawmax/luma-event-insights)<a href="https://agentmods.dev/skills/maximilien-ai/clawmax/luma-event-insights"><img src="https://agentmods.dev/badge/skills/maximilien-ai/clawmax/luma-event-insights/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/maximilien-ai/clawmax/luma-event-insights"><img src="https://agentmods.dev/badge/skills/maximilien-ai/clawmax/luma-event-insights.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00062 | $0.00887 |
| Opus 5 | $0.00031 | $0.00443 |
| Sonnet 5 | $0.00012 | $0.00177 |
| Haiku 4.5 | $0.00006 | $0.00089 |
Grade A, and why
luma-event-insights 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 11d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lu.ma Event Insights
This skill is a starter protocol for collecting and analyzing Lu.ma event data. It is designed to work with one of three input paths:
- Organizer-provided Lu.ma API access
- Organizer-exported CSV/JSON files
- Manual event URLs, slugs, notes, and attendee lists
Required Inputs
- Lu.ma event URL, slug, organizer page, or event list
- Organizer question:
- attendance quality
- invite conversion
- repeat attendees
- VIP / speaker mix
- chat or social signals
- trends across events
- Time window or event range
- Output artifact:
- analysis memo
- organizer recap
- dashboard brief
- trend summary
Secure Setup
Prefer secure runtime configuration over hardcoding credentials.
LUMA_API_KEYLUMA_EVENT_IDSLUMA_EXPORT_DIR
If direct Lu.ma API access is not available, use exported files and organizer notes as the source of truth instead of inventing missing data.
Working Method
- Confirm the exact event scope and organizer question.
- Confirm which input path is available:
- API
- exports
- manual inputs
- Build a source inventory:
- events
- attendees
- invites / RSVPs
- comments / chats / external signals
- Separate facts from interpretation.
- Produce:
- key metrics
- strongest patterns
- anomalies
- recommended next actions
Suggested Analysis Sections
- Event overview:
- date, format, topic, host, venue, capacity
- Attendance:
- RSVPs, checked-in attendees, no-shows, repeat guests, VIPs
- Invite funnel:
- invite volume, acceptance, declines, waitlist movement
- Signal review:
- comments, organizer notes, social echoes, qualitative themes
- Cross-event trends:
- timing, topic, location, audience mix, conversion quality
- Recommendations:
- repeat
- change
- test next
Output Standard
Always state:
- what data was available
- what was missing
- which findings are high-confidence
- which conclusions are directional only
Example Commands
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.
- 11d ago First seen · 137 lines · 62 tokens per session scan A 1c8878055a8c
luma-event-insights is a skill published in the GitHub repository Maximilien-ai/clawmax (94 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 887 once invoked, about $0.0003 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.
Other skills, from other repositories
swarm-advanced
Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
python-development
Coding standards, conventions, and patterns for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files in the python/ directory.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
trigger-authoring-tasks
Covers writing backend Trigger.dev tasks with @trigger.dev/sdk: defining task() and schemaTask(), the run function and its ctx, retries, waits, queues and concurrency, idempotency keys, run metadata, logging, triggering other tasks (and the Result shape), scheduled/cron tasks, and the essentials of trigger.config.ts.…