ONE REPOSITORY
FOR THE ENTIRE PROMPT STACK.

Reviews a PR like a senior who optimizes for the next reader, not style nits.

Write an ADR that future-you will still understand in 18 months.

Outage report that reduces fear of speaking up and produces real action items.

Reach a busy founder without AI-slop tone. Four lines, one ask, no hedging.

Monthly update that earns trust by being honest about misses.

Announce a B2B SaaS launch in the YC style. Sharp lede, two metrics, one founder quote.

Ask the questions that surface pain, not features. Seven questions, no leading the witness.

Set pricing for a new B2B SaaS. Three tiers, unit economics, anchor + decoy logic.

Five-step positioning canvas: competitive alternatives, unique attributes, value, ICP, market.

Distill 12 customer calls into themes, frequencies, and a top-3 list that survives committee.

Hook earns the open. 3 curated items, one personal note, soft CTA.

Hook → reframe → 3 acts → payoff. Designed to hold 60%+ retention.

Announce a feature ship. Lead with the job it does, not the feature name.

Submit a talk that reviewers can't skip. Hook, claim, 3 takeaways, audience.

Compress a 30-page paper into 5 lines + 4 structured takeaways.

Critique a screen's type hierarchy: sizes, weights, leading, optical alignment.

An empty state that teaches the user what to do next. Not just a blank screen.

Error messages that respect the user. Specific, actionable, never condescending.

Acknowledge, explain, propose, follow up. Don't roll over.

Reply to a SIG Lite questionnaire using your SOC 2 report. Cite controls.

Take wishful OKRs and turn them into bets you can actually grade.

Compress a 20-min intake call into a structured clinical note a physician can scan in 60s.

Flag the clauses that matter: mutuality, term, IP carveouts, governing law.

Teach a concept by asking, not telling. Five questions ordered by difficulty.

Design a rubric for grading creative work that doesn't kill the creativity.

Write a policy doc (e.g., remote work, AI usage) that's clear, lived, and won't gather dust.

Sell without lying. Lead with the JTBD. Three specs. Risk reversal.

Sell the moment, not the square footage. 3 paragraphs.

Write a listing that wins the algorithm AND the buyer. Keywords, bullets, A+ copy.

Decision memo that earns the meeting. Context, options, recommendation, risks.

Build a board deck that tells one story. Not 40 slides of metrics.

Design a 5-stage interview loop that surfaces real signal, not theater.

Have a hard performance conversation that respects both people. Script + recovery plan.

Build a 10-slide deck investors don't skim. Inevitability, wedge, traction, ask.

Run a pre-mortem on a decision. Imagine it's failed; trace back the most likely cause.

Build a system prompt for a custom agent that doesn't drift. Role, constraints, format.

Deliver feedback that the recipient can hear AND act on. Specific, kind, future-focused.

Design an A/B test that's powered, falsifiable, and shippable. Hypothesis to readout.

Argue a contrarian position credibly. Steelman opposition, then dismantle.

5-email onboarding that gets users to first value, not just first login.

Write a spec that fits on one page and ships in one sprint.

Diagnose why a prompt is producing poor output and fix it.

**Role:** You are a senior AI engineer who has shipped both RAG-based and fine-tuned LLM products at production scale. You believe most team…

**Role:** AI engineer who has built evals suites that have caught 30+ production regressions before they shipped. You believe vibes-based "t…

**Role:** Senior prompt engineer who has audited 100+ production system prompts. You read prompts the way an editor reads prose — for the me…

**Role:** Applied AI engineer who has shipped agents that completed millions of tool-calling iterations in production. You believe most agen…

**Role:** Database architect specializing in vector stores at production scale. **Context:** Team is migrating from [SOURCE: e.g., pgvector…

**Role:** AI engineer focused on cost optimization. You've cut LLM bills 50-70% at multiple companies by identifying token waste. **Context…

**Role:** AI engineer specialized in evals. You generate synthetic test sets for products with no ground-truth labels yet. **Context:** A t…

**Role:** AI engineer who has watched 5+ companies regress quality silently when model versions rotated. You build the suite that catches it…

**Role:** Applied AI engineer who reads LangSmith traces for a living. You've debugged 200+ production agent runs. **Context:** A specific …

**Role:** AI engineer specialized in LangGraph / state-machine agent architectures. **Context:** Building an agent for [TASK] in LangGraph.…

**Role:** AI infrastructure engineer. You've built multi-tenant LLM systems serving 1000+ customers without cross-tenant leakage. **Context…

**Role:** AI ops lead. You've fine-tuned 20+ customer-specific models and learned that 60% of them weren't worth it. **Context:** Customer …

**Role:** AI ops engineer. You've migrated production traffic across model versions 6 times. **Context:** A model upgrade is incoming (e.g.…

**Role:** Senior AI engineer focused on prompt operations. **Context:** Team has [N] production prompts. Currently they're hardcoded string…

**Role:** Experimentation engineer applied to LLM products. **Context:** Team wants to A/B test prompt variants on production traffic. Curr…

**Role:** AI safety researcher building the team's shared vocabulary for LLM bugs. **Context:** Team is shipping LLM features fast and prod…

**Role:** Senior product engineer for AI products. You've shipped streaming UIs at 4+ companies. **Context:** A team is building a streamin…

**Role:** Engineering manager who has built 3 LLM cost dashboards across companies. **Context:** A team has no visibility into LLM costs by…