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 pbc-os/smb-starter-kit --skill playbook-discoverygit clone --depth 1 https://github.com/pbc-os/smb-starter-kitWrote 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/pbc-os/smb-starter-kit/playbook-discovery)<a href="https://agentmods.dev/skills/pbc-os/smb-starter-kit/playbook-discovery"><img src="https://agentmods.dev/badge/skills/pbc-os/smb-starter-kit/playbook-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/pbc-os/smb-starter-kit/playbook-discovery"><img src="https://agentmods.dev/badge/skills/pbc-os/smb-starter-kit/playbook-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 | $0.00024 | $0.01577 |
| Opus 5 | $0.00012 | $0.00788 |
| Sonnet 5 | $0.00005 | $0.00315 |
| Haiku 4.5 | $0.00002 | $0.00158 |
Grade A, and why
playbook-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 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook Discovery
Discover repeatable workflows from your historical data that AI agents can automate.
Description
This skill analyzes your business communication data (email, calendar, files, chat) to identify "playbooks" — documented, repeatable workflows with clear triggers, steps, and end states.
Why this matters: Before you can automate, you need to know what to automate. Most small business owners have dozens of repeatable workflows buried in their daily habits — they just haven't documented them. This skill surfaces those patterns.
Triggers
- "discover playbooks"
- "find workflows to automate"
- "analyze my email patterns"
- "what can I automate"
- "playbook discovery"
- "workflow analysis"
- User connects email/calendar and wants to find automation opportunities
Prerequisites
At least one of these data sources connected:
- Email — Gmail (
gmailskill) or Microsoft 365 (Graph API) - Calendar — Google Calendar (
google-calendarskill) or Outlook - Files — Google Drive, OneDrive, Dropbox
- Chat — Slack, Teams, Discord
More data sources = better pattern recognition.
Workflow
Phase 1: Data Collection
Collect data systematically to avoid API limits. Chunk by time period (monthly).
For each data source, extract:
Email (Inbox + Sent)
For each of the last 6 months:
- ALL inbox emails (aim for 100-200+ per month)
- ALL sent emails (aim for 100-200+ per month)
- Extract: subject lines, senders/recipients, dates, thread patterns
- Categorize by type: partner comms, internal, requests, approvals, technical
Calendar
Full 6-month period:
- All events with attendees, duration, recurrence
- Identify recurring meetings and cadence (weekly, bi-weekly, monthly)
- Note meeting types: 1:1s, team syncs, partner meetings, training
- Look for meeting sequences that precede deliverables
- Identify high-frequency attendees
Files
- Look for versioned documents (v1, v2, Draft 1, Final, etc.)
- Identify templates and recurring document types
- Note file modification patterns and naming conventions
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 · 221 lines · 24 tokens per session scan A 5671fea25482
playbook-discovery is a skill published in the GitHub repository pbc-os/smb-starter-kit (10 stars, last pushed 3mo ago), licensed MIT. It adds 24 tokens to every session and 1,577 once invoked, about $0.0001 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 2026-08-31.
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