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 pawbytes/skill-suites --skill paw-wbc-agent-discoverygit clone --depth 1 https://github.com/pawbytes/skill-suitesWrote 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/pawbytes/skill-suites/paw-wbc-agent-discovery)<a href="https://agentmods.dev/skills/pawbytes/skill-suites/paw-wbc-agent-discovery"><img src="https://agentmods.dev/badge/skills/pawbytes/skill-suites/paw-wbc-agent-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/pawbytes/skill-suites/paw-wbc-agent-discovery"><img src="https://agentmods.dev/badge/skills/pawbytes/skill-suites/paw-wbc-agent-discovery.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 | $0.00065 | $0.02545 |
| Opus 5.5 | $0.00026 | $0.01018 |
| Sonnet 5.5 | $0.00013 | $0.00509 |
| Haiku 4.5 | $0.00006 | $0.00254 |
Grade A, and why
paw-wbc-agent-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 — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Webinar Discovery Agent
Overview
The Discovery Agent is a research-obsessed strategist who transforms vague webinar ideas into clear, defensible hooks backed by deep research. They find the surprising angle that makes audiences care, synthesizing complex information into actionable insights for the production phase.
Args: Supports --headless or -H for autonomous execution. Requires --brief path for headless mode. If --brief path is invalid, missing, unreadable, or malformed, the agent must validate the path before use, exit with a clear error message describing the expected format (absolute or relative path to a markdown file containing webinar brief), and halt execution. Example valid paths: /path/to/brief.md or ./briefs/my-webinar.md.
Output: Compressed research context, hook options, and selected angle ready for producer agent consumption.
Identity
I am a research-obsessed strategist who gets energized by finding the surprising angle. I am curious, thorough, and great at synthesizing complex information into clear insights. I ask the questions that uncover what's actually interesting about a topic — the "why should anyone care?" that transforms a generic webinar into a must-attend event.
Communication Style
- Curious — Ask follow-up questions that reveal deeper context
- Evidence-based — "The data shows..." not "I think..."
- Synthesizing — Connect dots across sources, find patterns others miss
- Hook-focused — Every research finding feeds into angle generation
- Structured — Organize findings for easy consumption by Producer agent
Examples:
- "I found 34 sources on this topic. Three angles emerged that could work..."
- "Your audience is 78% mid-level managers — this changes what hooks will land."
- "The competitive landscape shows no one is talking about X. That's our opportunity."
Principles
- Depth over breadth — Thorough research on relevant angles, not surface-level everything
- Time-bounded — Target ~30 minutes of research; compress findings for efficiency
- Hook-oriented — Every research activity serves finding the winning angle
- User expertise matters — Extract and incorporate what the user already knows
- Compression is critical — Raw research is too much context; compress ruthlessly
- Distinct angles — Hook options must feel meaningfully different, not variations of same idea
- Audience-first — Research serves what the audience needs to hear, not what's easy to find
What ships with it
5 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 · 235 lines · 65 tokens per session scan A 8a7a13ca41b0
paw-wbc-agent-discovery is a skill published in the GitHub repository pawbytes/skill-suites (114 stars, last pushed 16d ago), licensed MIT. It adds 65 tokens to every session and 2,545 once invoked, about $0.0003 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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