I turned my job search into a system, not a blank page.
A canonical answer bank stores every application question I’ve hit with a final, approved answer at the right character count. Reusable cover-letter hooks, source-of-truth files, and a strict voice-and-truth governance layer sit on top. AI agents draft and stage — and I approve every output before it ships.
Applying one at a time is a losing game.
30-plus roles. The same questions, different length caps.
Every application asked variations of “why you,” “your AI-search approach,” “tell us about yourself,” and comp range — each with a different limit (500 chars, 30 words, 200 words).
Rewriting the same proof points from scratch every time was slow and inconsistent — and inconsistency is dangerous. It risked drifting off my verified facts, overclaiming, or breaking the framing that keeps a founder from looking split between a job and a business.
I needed one governed place where the best version of every answer lives, with hard rules on voice and truth — and the drafting and publishing done by agents I direct and approve.
A governed library, not a cold start.
The center is a single canonical answer bank with a strict “lives here and nowhere else” rule — split into a reusable, company-agnostic section and a company-specific section in reverse-chronological order. Every entry is labeled with its character or word count so it drops straight into a form.
On top sits governance and voice enforcement: a canonical framing-rules layer bans specific words and constructions, locks the venture framing, and fences claims to verified proof — no invented wins. New questions and approved answers get appended over time, so the bank compounds.
The LinkedIn work-history builder is the agent-execution arm: I feed it raw source material, it extracts an accomplishment-focused timeline, compresses each into a tight 5–6 bullet Experience entry, drives the logged-in LinkedIn UI to draft it — and stops before saving for my approval.
Each new application becomes selection and light editing against a governed library — assembly, not a blank page.
— Design principle · compounding, not repeatingInputs, a governed core, and an approval gate.
Source-of-truth inputs feed a two-part answer bank. Everything passes through a governance and voice gate before it becomes an output — application forms, cover letters, or LinkedIn entries. A human-approval gate sits in front of anything that publishes: Mike approves before it ships.
Source-of-truth inputs feed the canonical answer bank (reusable + company-specific, every entry count-labeled). Content passes the governance and voice gate — banned-word rules, locked venture framing, claim guardrails — before becoming a form answer, cover letter, or LinkedIn entry. Every publish routes through a human approval gate: Mike approves, and for LinkedIn the agent stages a draft and stops before saving. Approved answers append back to the bank so it compounds.
Governed Core
Governance
Source-of-Truth Files
Agents
LinkedIn Layer
Inputs
One governed library, compounding.
Verified from the live vault artifacts and the Slack record. Company identities withheld — internal system.
The answer bank is real and substantial — a single append target for every new Q&A. Personal-profile LinkedIn posting scope was verified live after a token refresh, and the agent’s Experience drafts always stop before saving for approval.
The design, shown without the private content.
This is an internal system. The panels below illustrate structure only — real answer text, comp figures, target companies, and file paths are redacted.
The LinkedIn builder sequence, sanitized: raw export → extracted accomplishment timeline → tight 5–6 bullet Experience entry → draft in the logged-in UI → stop for approval. Nothing saves without Mike’s sign-off.
What it does — and what it deliberately doesn't.
The left is what exists and works. The right is the honest boundary: this is human-in-the-loop by design.
A live, governed system Mike drives daily.
- New applications are assembled from the answer bank and cover-letter hooks against verified source-of-truth files.
- Every answer runs under enforced voice and framing rules.
- Profile updates are drafted and staged to LinkedIn by an agent that stops for approval before saving.
- The bank is real and substantial and is the single append target for every new Q&A.
- Personal-profile LinkedIn posting scope verified live after a token refresh.
A personal-productivity system — not a product.
- This is a system for Mike’s own job search, not a product or a client deliverable.
- Human-in-the-loop by design: agents draft and stage; Mike approves, and for LinkedIn often pastes final copy himself to avoid browser edge cases.
- The value is speed and consistency with truth-guardrails — not fully autonomous applying.
Internal system. Real answer text, comp figures, target companies, and file paths are withheld here per confidentiality policy; only sanitized structure is shown.
I'm the architect, editor, and decision-maker.
I designed the whole system — the single-source rule, the two-part structure, the count-labeling, and the governance and voice layer that keeps every answer on-brand and on-truth.
I set the framing rules and claim guardrails, directed the agents to extract work histories and draft LinkedIn entries, reviewed and approved every output, and made the final calls on wording and what ships. The agents are the drafting and publishing layer; I’m the architect, editor, and decision-maker.