Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill nowait-reasoning-optimizergit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote 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/foryourhealth111-pixel/vibe-skills/nowait-reasoning-optimizer)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/nowait-reasoning-optimizer"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/nowait-reasoning-optimizer/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/foryourhealth111-pixel/vibe-skills/nowait-reasoning-optimizer"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/nowait-reasoning-optimizer.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 | $0.00110 | $0.01257 |
| Opus 5.5 | $0.00044 | $0.00503 |
| Sonnet 5.5 | $0.00022 | $0.00251 |
| Haiku 4.5 | $0.00011 | $0.00126 |
Grade A, and why
nowait-reasoning-optimizer 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.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NOWAIT Reasoning Optimizer
Implements the NOWAIT technique from the paper "Wait, We Don't Need to 'Wait'! Removing Thinking Tokens Improves Reasoning Efficiency" (Wang et al., 2025).
Overview
NOWAIT is a training-free inference-time intervention that suppresses self-reflection tokens (e.g., "Wait", "Hmm", "Alternatively") during generation, reducing chain-of-thought (CoT) trajectory length by 27-51% without compromising model utility.
When to Use
- Deploying R1-style reasoning models with limited compute
- Reducing inference latency for production systems
- Optimizing token costs for reasoning tasks
- Working with verbose CoT outputs that need streamlining
Supported Models
| Model Series | Type | Token Reduction |
|---|---|---|
| QwQ-32B | RL-based | 16-31% |
| Phi4-Reasoning-Plus | RL-based | 23-28% |
| Qwen3-32B | RL-based | 13-16% |
| Kimi-VL-A3B | Multimodal | 40-60% |
| QvQ-72B-Preview | Multimodal | 20-30% |
Important: NOWAIT works best with RL-based models. Distilled models (Qwen3-4B/8B/14B) show degraded performance when reflection tokens are suppressed.
Quick Start
1. Basic Implementation
from scripts.nowait_processor import NOWAITLogitProcessor
# Initialize processor for your model's tokenizer
processor = NOWAITLogitProcessor(tokenizer)
# Use during generation
outputs = model.generate(
inputs,
logits_processor=[processor],
max_new_tokens=32768
)
2. Keywords Suppressed
See references/keywords.md for the complete list. Core keywords:
wait, alternatively, hmm, but, however, check,
double-check, maybe, verify, again, oh, ah
How It Works
- Initialize Keywords: Identify reflection keywords from empirical analysis
- Expand to Token Variants: Map keywords to all token variants in vocabulary (e.g., "wait" → " wait", "Wait", " Wait", ".wait", "WAIT")
- Suppress During Inference: Set logits of reflection tokens to large negative values during decoding
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 · 146 lines · 110 tokens per session scan A fd71e79f3601
nowait-reasoning-optimizer is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,770 stars, last pushed yesterday), licensed Apache-2.0. It adds 110 tokens to every session and 1,257 once invoked, about $0.0004 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 2026-09-03.
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