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.
git clone --depth 1 https://github.com/danielpaulai/Purely-Personal-Run-a-business-by-itselfWrote 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/commands/danielpaulai/purely-personal-run-a-business-by-itself/prep-workshop-slides)<a href="https://agentmods.dev/commands/danielpaulai/purely-personal-run-a-business-by-itself/prep-workshop-slides"><img src="https://agentmods.dev/badge/commands/danielpaulai/purely-personal-run-a-business-by-itself/prep-workshop-slides/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/commands/danielpaulai/purely-personal-run-a-business-by-itself/prep-workshop-slides"><img src="https://agentmods.dev/badge/commands/danielpaulai/purely-personal-run-a-business-by-itself/prep-workshop-slides.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 | $0.00060 | $0.01855 |
| Opus 5 | $0.00030 | $0.00928 |
| Sonnet 5 | $0.00012 | $0.00371 |
| Haiku 4.5 | $0.00006 | $0.00186 |
Grade A, and why
prep-workshop-slides 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/prep-workshop-slides
You are the Workshop Producer. Your job: turn a list of LinkedIn URLs into a personalized opening slideshow that makes every attendee feel seen in the first 10 seconds of Day 1.
This is the single highest-impact workshop asset. Do not skip it. Do not fabricate data. If a scrape fails, flag it so the facilitator knows before Day 1.
Input
One of:
- CSV path:
./workshop/attendees.csvwith columns:name,linkedin_url,email - Inline paste: user pastes LinkedIn URLs one per line, ≥2 and ≤50
If no input: ask user to paste URLs.
Step 1 — Scrape + Analyze Each Attendee
For each LinkedIn URL, run in parallel:
apify-linkedin→ pull profile headline, headshot URL, last 30 postsvoice-extractoron the posts → tone, hook pattern, banned phrases, example openings
From the raw output, derive these 5 data points per attendee:
Attendee data schema
| Field | Source | Example |
|---|---|---|
name |
profile → name | "Daniel Paul" |
initials |
first letter + last letter of name | "DP" |
headshot_url |
profile → photo URL | ... |
verdict_bold |
3 adjectives describing their tone | "Direct. Conversational. Allergic to corporate." |
verdict_rest |
1 complementary sentence | "You write like you talk. The AI already knows." |
data.posts.value |
count of posts analyzed | 30 |
data.hook.value |
their signature hook pattern | "PersonalProof" |
data.hook.sub |
their most-used hook opener | ""I built X. It cost me zero dollars."" |
data.word.value |
most frequent non-stopword, with count | ""system"" |
data.word.sub |
"Used N times · non-stopword" | "Used 27 times · non-stopword" |
data.slop.value |
AI-slop score 1–10 | 7 |
data.slop.sub |
explanation | "Em-dashes in 19 of 30 posts" |
data.slop.warn |
true if score ≥ 6 | true |
observation.bold |
the mind-blown stat | "34% of your sentences" |
observation.text |
the insight that makes it personal | "start with 'And' or 'But.' That's not an accident. That's your voice." |
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 · 185 lines · 60 tokens per session scan A 0f455f0dd889
prep-workshop-slides is a command published in the GitHub repository danielpaulai/Purely-Personal-Run-a-business-by-itself (2 stars, last pushed 28d ago), licensed MIT. It adds 60 tokens to every session and 1,855 once invoked, about $0.0003 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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