Eventyay is an open-source platform for managing events, including ticket sales and registration, speaker submissions, session schedules, videos, check-ins, badges, exhibitions, and organiser administration. Event organisers use it to run both in-person and online event workflows.
Borrowing it
Nothing to install: this file belongs to fossasia/eventyay. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/fossasia/eventyay/dev/.agents/skills/connect-recommend/SKILL.mdgit clone --depth 1 https://github.com/fossasia/eventyayWrote 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/fossasia/eventyay/connect-recommend)<a href="https://agentmods.dev/skills/fossasia/eventyay/connect-recommend"><img src="https://agentmods.dev/badge/skills/fossasia/eventyay/connect-recommend/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/fossasia/eventyay/connect-recommend"><img src="https://agentmods.dev/badge/skills/fossasia/eventyay/connect-recommend.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 | $0.00151 | $0.04921 |
| Opus 5.5 | $0.00060 | $0.01968 |
| Sonnet 5.5 | $0.00030 | $0.00984 |
| Haiku 4.5 | $0.00015 | $0.00492 |
Grade A, and why
connect-recommend 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 21d 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:
- connect-recommend — 98% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Connect recommend
Recommend the right Stripe Connect integration configuration. The user only needs to provide a company URL or describe their business — the skill figures out the rest.
Interaction model
User must confirm interactions. Every decision point in this skill MUST be confirmed with the user with clear, numbered options and short descriptions. One question at a time — never overwhelm the user.
Auto-act on low-cost actions. Never ask permission for:
- Generating the markdown recommendation plan — just generate it
- Scanning the codebase — just scan it
- Reading reference files — just read them
Never end with passive text. Every stopping point must end with a prompt to the user offering concrete next actions.
Terminology rules (user-facing output)
Before generating any user-facing output, read references/terminology-rules.md. Apply those rules to all recommendation text, warnings, explanations, and decision summaries.
Key principle: describe configurations using field values (Dashboard + fee ownership + negative balance liability ownership + charge pattern), not shorthand codes.
Output Brevity
Keep responses concise. The user is making decisions, not reading documentation.
- Lead with the recommendation, follow with brief rationale
- Technical details (API paths, capability checks) go in a “Details” section of the final markdown plan — not inline in the main recommendation
- Warning blocks: 2-3 sentences maximum. State the issue and the fix. No mechanism deep-dives unless the user asks.
- Decision summary: bullet points only, one line per decision
- Never output more than ~40 lines in a single response during interactive mode
Only mention out-of-scope limitations when they’re directly relevant to what the user asked about. Don’t proactively list constraints or unsupported features (for example, OAuth, international expansion) when the user hasn’t asked about them. “Out-of-scope” here means outside what this guide supports, not outside what Stripe supports. Research these topics in the Stripe public documentation (docs.stripe.com) rather than saying they’re out-of-scope.
What ships with it
8 files 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.
- references/account-types.md 18 KB
- references/charge-patterns.md 12 KB
- references/company-researcher.md 7.6 KB
- references/compatibility-matrix.md 17 KB
- references/decision-matrix.md 37 KB
- references/discovery-questions.md 19 KB
- references/recommendation-template.md 17 KB
- references/terminology-rules.md 8.7 KB
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.
- 21d ago First seen · 362 lines · 151 tokens per session scan A 6c240a3ea807
connect-recommend is a skill published in the GitHub repository fossasia/eventyay (1,710 stars, last pushed today), licensed Apache-2.0. It adds 151 tokens to every session and 4,921 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-09-29.
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