Wondel.ai Agent Skills is a collection of skills and guided workflows that give coding agents structured methods for business, marketing, UX, website, app, and software-development tasks. It is used with agents such as Claude Code, Codex, Cursor, OpenClaw, and other agents that support the agentskills.io format. The catalogue entries are the project's skills, plugins, instructions, and rule packaged for those agents.
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 wondelai/skills --skill inspired-productgit clone --depth 1 https://github.com/wondelai/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/wondelai/skills/inspired-product)<a href="https://agentmods.dev/skills/wondelai/skills/inspired-product"><img src="https://agentmods.dev/badge/skills/wondelai/skills/inspired-product/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/wondelai/skills/inspired-product"><img src="https://agentmods.dev/badge/skills/wondelai/skills/inspired-product.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00119 | $0.03100 |
| Opus 5 | $0.00060 | $0.01550 |
| Sonnet 5 | $0.00024 | $0.00620 |
| Haiku 4.5 | $0.00012 | $0.00310 |
Grade A, and why
inspired-product 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Empowered Product Teams Framework
Framework for building products customers love through empowered teams that own continuous discovery and delivery. The best product companies don't ship features -- they solve problems, and they give teams the autonomy and accountability to figure out how.
Core Principle
Empowered product teams = cross-functional groups given problems to solve (not features to build) who own discovery and delivery end-to-end.
Most product failures come not from bad engineering or design but from building things nobody wants. Feature teams receive roadmaps and execute; empowered teams receive objectives and discover solutions. The difference between a feature factory and an innovation engine is whether teams are missionaries (driven by vision and empathy) or mercenaries (driven by a handed-down backlog).
Scoring
Goal: 7/7. Score product team structures, discovery practices, or delivery processes by the Quick Diagnostic below -- 1 point per satisfied row, scored 0-7. Bands: 6-7 = empowered teams own outcomes and discovery runs continuously with engineers; 4-5 = discovery happens but inconsistently, or teams own output with partial outcome accountability; <=3 = a feature factory: teams receive a roadmap of dated features and skip discovery. Always state the current score and the specific failed diagnostic rows to fix to reach 7/7.
Framework
1. Product Discovery vs Delivery
Core concept: Product work runs on two parallel tracks: discovery determines what to build by addressing risks before engineering investment; delivery builds production-quality software. Most organizations skip discovery entirely, jumping from idea to backlog to sprint.
Why it works: Discovery is cheap and fast; delivery is expensive and slow. Validating ideas before committing engineering avoids the most common failure mode: building something nobody wants.
Key insights:
- Discovery answers four risks: value (will customers use it?), usability (can they figure it out?), feasibility (can we build it?), viability (does it work for the business?)
- Discovery output is validated ideas backed by evidence, not PRDs or specifications
- Run 10-20 discovery iterations per feature that reaches delivery -- most ideas won't work, so fail fast and cheap
- Discovery is not a phase; it runs continuously alongside delivery, with engineers participating
What ships with it
6 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.
- 11d ago First seen · 207 lines · 119 tokens per session scan A 482ab6a32bb5
inspired-product is a skill published in the GitHub repository wondelai/skills (2,138 stars, last pushed 12d ago), licensed MIT. It adds 119 tokens to every session and 3,100 once invoked, about $0.0006 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.
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