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 IvanCampos/agents --skill confettigit clone --depth 1 https://github.com/IvanCampos/agentsWrote 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/ivancampos/agents/confetti)<a href="https://agentmods.dev/skills/ivancampos/agents/confetti"><img src="https://agentmods.dev/badge/skills/ivancampos/agents/confetti/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/ivancampos/agents/confetti"><img src="https://agentmods.dev/badge/skills/ivancampos/agents/confetti.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 | $0.00046 | $0.00198 |
| Opus 5.5 | $0.00018 | $0.00079 |
| Sonnet 5.5 | $0.00009 | $0.00040 |
| Haiku 4.5 | $0.00005 | $0.00020 |
Grade A, and why
confetti 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 1mo 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.
What it actually says
Confetti
Overview
Run a fixed two-command celebration sequence.
Quick Start
scripts/run_confetti.sh
Optional control flags:
scripts/run_confetti.sh --wait
scripts/run_confetti.sh --async
Default behavior is optimized for performance:
- Interactive terminal (
TTY): waits for speech to finish. - Non-interactive automation: starts speech in background and returns immediately.
Tasks
Run confetti
- Execute
scripts/run_confetti.shto open Raycast confetti and speak the phrase.
Resources
scripts/
run_confetti.sh: Triggers confetti and speaks the phrase, with async fast path for automation.
What ships with it
2 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.
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.
- 1mo ago First seen · 37 lines · 46 tokens per session scan A 1f3a564b0732
confetti is a skill published in the GitHub repository IvanCampos/agents (5 stars, last pushed 8mo ago), licensed MIT. It adds 46 tokens to every session and 198 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
bootstrap
Onboarding conversation to generate a user profile.
using-powerups
Use when starting work in a repository with Agent Powerups installed, when a task may match a reusable local skill, command, workflow, hook recipe, AGENTS.md template, or MCP feature.
context-minimization
Use when a task runs long and the context window is filling -- large files are being read whole, command output is flooding the transcript, or responses are getting verbose and slow.
no-fluff
Ultra-compressed communication mode. Cuts token usage 75% by dropping filler, articles, and pleasantries while keeping full technical accuracy. Use when user says "no fluff", "be concise", "use less tokens", or similar.
repo-map
Use when the task is to understand an unfamiliar codebase, locate key entry points, or summarize architecture before editing.
verification-before-completion
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always.