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 continuous-discoverygit 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/continuous-discovery)<a href="https://agentmods.dev/skills/wondelai/skills/continuous-discovery"><img src="https://agentmods.dev/badge/skills/wondelai/skills/continuous-discovery/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/continuous-discovery"><img src="https://agentmods.dev/badge/skills/wondelai/skills/continuous-discovery.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 | $0.00142 | $0.02983 |
| Opus 5.5 | $0.00057 | $0.01193 |
| Sonnet 5.5 | $0.00028 | $0.00597 |
| Haiku 4.5 | $0.00014 | $0.00298 |
Grade A, and why
continuous-discovery 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Continuous Discovery Habits Framework
Framework for building a sustainable weekly practice of customer discovery that keeps product teams progressing toward desired outcomes. Discovery is not a phase before development — it is embedded in the ongoing rhythm of product work so every decision is informed by fresh evidence.
Core Principle
Good product discovery requires a continuous cadence, not a one-time event. Teams that talk to customers every week, map opportunities visually, and test assumptions before building consistently outperform teams that rely on intuition, stakeholder opinions, or quarterly research cycles. The benchmark: at least one customer touchpoint per week, every week, by the product trio (product manager, designer, engineer).
Scoring
Goal: 10/10. Score a discovery practice by the seven Quick Diagnostic rows below — start at 3, add 1 point per row answered "yes" (max 10). Bands: 9-10 = weekly cadence, a living Opportunity Solution Tree, systematic assumption testing, and every shipped feature traceable to a customer opportunity; 5-6 = some discovery happening but ad hoc, PM-only, or disconnected from delivery; ≤3 = intuition- and stakeholder-driven with no regular customer contact. Report the current score, the failing rows, and the specific fix for each.
Framework
1. Opportunity Solution Trees
Core concept: An Opportunity Solution Tree (OST) visually connects a desired outcome (top) to customer opportunities (middle) to potential solutions and experiments (bottom), making implicit product thinking explicit and shared.
Why it works: Most teams jump from business outcome straight to solutions, skipping the customer need entirely; the OST forces understanding of the opportunity space first, preventing features nobody wants.
Key insights:
- Four layers: Outcome > Opportunities > Solutions > Experiments
- Opportunities are customer needs, pain points, and desires — framed from the customer's perspective
- The tree is a living artifact, updated weekly as the team learns
- Break large opportunities into smaller sub-opportunities to make them actionable
- Pursue multiple opportunities simultaneously — don't bet everything on one
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.
- 1mo ago First seen · 203 lines · 142 tokens per session scan A 44260b572a05
continuous-discovery is a skill published in the GitHub repository wondelai/skills (2,507 stars, last pushed 1mo ago), licensed MIT. It adds 142 tokens to every session and 2,983 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 2026-08-30.
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