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/build-my-brain)<a href="https://agentmods.dev/commands/danielpaulai/purely-personal-run-a-business-by-itself/build-my-brain"><img src="https://agentmods.dev/badge/commands/danielpaulai/purely-personal-run-a-business-by-itself/build-my-brain/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/build-my-brain"><img src="https://agentmods.dev/badge/commands/danielpaulai/purely-personal-run-a-business-by-itself/build-my-brain.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 | $0.00037 | $0.01668 |
| Opus 5 | $0.00018 | $0.00834 |
| Sonnet 5 | $0.00007 | $0.00334 |
| Haiku 4.5 | $0.00004 | $0.00167 |
Grade A, and why
build-my-brain 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 12d 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/build-my-brain
You are running the 4-act Business Brain intake. Your job is to produce a filled BUSINESS-BRAIN.md at the project root in under 40 minutes. Most of that time is automation — the user only answers 5 strategic questions.
Load purely-personal/examples/BUSINESS-BRAIN.sample.md as the structural reference for the output file.
Opening (30 seconds)
Say this, verbatim:
"We're going to build your Business Brain. This is the one file every AI executive reads before acting. Four acts, four wow moments. Ready?"
Wait for "yes" or equivalent.
Act 1 — Voice (5 minutes)
Say:
"Act 1. Paste your LinkedIn profile URL."
Once they paste:
-
Use
apify-linkedin(if installed) ORapify--linkedin-profile-scraperto pull:- Profile headline, about, experience, headshot URL
- Last 30 posts (content + engagement)
-
Run
voice-extractorskill on the scraped posts. Extract:- 3–5 banned phrases (observed absences — phrases that DON'T appear)
- 5 hook patterns (first-line openings repeated across posts)
- 3 example post excerpts (first 2–3 sentences)
- Tone description (1–2 sentences)
-
Run
linkedin-profile-optimizerto generate a profile score (out of 100). -
Display results to user:
✓ Pulled profile + 30 posts
✓ Voice rules extracted: [N banned phrases, N hook patterns]
✓ Profile score: {score}/100
-
Ask: "Anything to add or remove before I save?"
-
On confirmation, write Voice section to
BUSINESS-BRAIN.md.
Wow moment: "Wait, that's exactly how I write."
Act 2 — Business (5 minutes)
Say:
"Act 2. Paste your website URL."
Once they paste:
-
Use
apify--website-content-crawlerto pull the homepage + pricing page (if found). -
Extract:
- Offer statement (one line)
- Positioning (one sentence)
- Pricing (price points)
- Key claims / social proof
-
Run
brand-identity-extractoron the same URL. Extract:- 4–8 colors (dominant, excluding pure
#ffffffand#000000) - Display font + body font (from CSS inspection)
- Visual voice description (1 sentence, auto-generated from observed patterns)
- 4–8 colors (dominant, excluding pure
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.
- 12d ago First seen · 205 lines · 37 tokens per session scan A f47f96cd21b8
build-my-brain is a command published in the GitHub repository danielpaulai/Purely-Personal-Run-a-business-by-itself (2 stars, last pushed 29d ago), licensed MIT. It adds 37 tokens to every session and 1,668 once invoked, about $0.0002 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.