irac-prompt-stephane-boghossian

name	irac-prompt-stephane-boghossian
version	0.1.0
description	Restructure any rough build, research, or legal-drafting request into an IRAC-shaped prompt — Issue, Rule, Analysis, Conclusion — optimized for a frontier model. It's the bar-exam framework, repurposed as prompt engineering. The skill leads with the issue and ends with the conclusion (where models weight attention most), forces you to name your constraints and non-goals, and specifies what "good" looks like before a single token is generated. Use it before any non-trivial build, or whenever a vague ask deserves a precise brief.
triggers	["irac this","structure this prompt","make a proper prompt for X","brief the model","turn this into a memo prompt","prompt like a lawyer"]
metadata	{"author":"Stephane Boghossian","license":"agpl-3.0","version":"2026-06-05"}

IRAC Prompt

A lawyer doesn't hand an associate "go look into the housing thing." They write a memo: here's the issue, here are the rules that govern it, here's the analysis of how they apply, here's the conclusion I want. That same structure is the single biggest lever on frontier-model output quality. This skill turns a vague ask into that brief.

When to use

• The user has a fuzzy build/research/drafting task and wants a good prompt, not a guess.
• Before kicking off a non-trivial vibecode task (pairs naturally before /grill-me and /yalla).
• Repackaging a task to hand to a sub-agent or to HAQQ's Justinian.

The method

Take the user's raw ask and rewrite it into four labelled blocks. Lead with the issue, end with the conclusion — models weight the top and bottom of a prompt most.

I — Issue (top)

One or two sentences: what exactly are we trying to do, and for whom. The single problem statement. If the user gave three problems, pick the one that matters or split into three prompts. No problem, no solution, no value.

R — Rule (constraints)

The governing facts the model must respect:

• Hard constraints (stack, language, libraries, file paths, output format).
• Domain rules (for legal: the statute/clause/jurisdiction; for code: the API contract, existing patterns to match).
• Non-goals — what NOT to do. Lawyers specify what they don't want; do the same.
• Definition of done / what "good" looks like, ideally measurable.

A — Analysis (the reasoning the model should do)

• Why this matters and who the real user is.
• The known hard part / where prior attempts or weaker models failed.
• The approach or first principles to apply (or explicitly: "figure out the approach, here are the inputs").
• Edge cases to handle: empty state, auth failure, network error, malformed input.

C — Conclusion (the ask)

The concrete deliverable, restated crisply. What artifact, in what shape, verified how.

Output contract

Emit the rewritten prompt inside a fenced block the user can copy verbatim, with the four headers (Issue / Rule / Analysis / Conclusion). Then, below the fence, a 2–3 line note on what you inferred or assumed, and any gap worth a /grill-me pass. Don't pad — if a block is genuinely empty (rare), say so rather than inventing constraints.

Modifiers (the user can ask for any of these)

--adversarial — append a three-agent split so no agent grades its own homework (Mike's core trick: "you don't ask the associate if they double-checked it; you give it to someone else"):

Run this as three roles. Planner: produce a step plan from the Issue/Rule, no code. Executor: implement the plan against the Rule constraints. Tester: adversarially verify the Executor's output against the definition-of-done and edge cases; report failures, do not fix. Loop Executor↔Tester until the Tester passes. (Maps to /grill-me + /lecun-world-model.)

--budget — prepend a context-budget directive (Mike: performance "drops off a cliff" as context fills):

Treat the context window as a budget, not a dumpster. Keep this prompt lean. Use parallel sub-agents with narrow jobs and fresh context rather than one bloated agent. Compact or start a new conversation between phases. Load only the Skills/files each step needs.

--voice — the user dictated this; tolerate disfluency, infer structure, don't nitpick phrasing.

--legal — Issue/Rule/Analysis/Conclusion map literally to IRAC. Rule = the statute/clause + jurisdiction; Analysis = application to facts; flag anything JUDGMENT-BOUND (hand to /law-as-code-audit). Never assert a legal conclusion the source text doesn't support.

Worked example

Raw ask: "make something that checks if building permits are gonna get rejected"

Output:

ISSUE
Build a tool that, given a set of ADU building-permit blueprints, flags the corrections a California city plan-reviewer will require — before submission — for the builder filing the permit.

RULE
- Inputs: multi-page blueprint PDFs, table-sized, with dense margin annotations.
- Constraint: OCR alone strips annotations from the wall/element they reference —
  preserve visual context (which wall a "thickness" note belongs to).
- Stack: Anthropic SDK, parallel sub-agents, Skills for the ruleset. No paralegal in the loop.
- Done = ≥70% agreement with the city's real correction letters on a held-out set.
- Non-goal: do not auto-submit or give legal advice; output is an action plan a human files.

ANALYSIS
Prior generation models can't do this — that's the point. The hard part is grounding each rule check in the right region of the blueprint. Chunk the blueprint, pass each chunk + the relevant rules, ask "what's wrong here," then aggregate. Handle: missing pages, illegible scans, blueprints with no annotations.

CONCLUSION
A pipeline that ingests blueprints and returns a precise, per-item correction action plan in <20 min, benchmarked hourly against the correction-letter set.

Inferred: ADU + California + builder-side from "permits." Open question for /grill-me: builder-side only, or also the city reviewer side? (CrossBeam ended up doing both.)

Notes

• This skill writes a prompt, it doesn't execute the task.
• Inspired by Michael T. Brown's CrossBeam — the personal-injury lawyer who, never having written a line of code, beat 13,000 builders to win Anthropic's 2026 global hackathon by prompting the model the way he briefs an associate. Three modifiers: --adversarial (a plan→execute→test agent split so no agent grades its own homework), --budget (treat the context window as a budget, not a dumpster), and --legal (literal IRAC that flags judgment-bound clauses).