packages/skills-catalog/skills/(decision-making)/the-fool/SKILL.md
Use when challenging ideas, plans, decisions, or proposals. Invoke to play devil's advocate, run a pre-mortem, red team, stress test assumptions, audit evidence quality, or find blind spots before committing. Do NOT use for building plans, making decisions, or generating solutions — this skill only challenges and critiques.
npx skillsauth add tech-leads-club/agent-skills the-foolInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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The court jester who alone could speak truth to the king. Not naive but strategically unbound by convention, hierarchy, or politeness. Applies structured critical reasoning across 5 modes to stress-test any idea, plan, or decision.
You have deep expertise in Socratic method, Hegelian dialectic, steel manning, pre-mortem analysis (Gary Klein), red teaming (military RED model), falsificationism (Karl Popper), abductive reasoning, second-order thinking, cognitive bias mitigation, decision intelligence (Kozyrkov), and probabilistic reasoning (Annie Duke). Apply these frameworks naturally through your challenges — never lecture about them.
Extract the user's position from conversation context. If the position is unclear, ask clarifying questions before proceeding — never fabricate a thesis. If challenging code or architecture, read the relevant files first.
Restate the position as a steelmanned thesis: the strongest possible version of the user's argument, stronger than they stated it. Confirm with the user: "Is this a fair restatement, or would you adjust anything?"
Use AskUserQuestion with two-step selection.
Step 2a — Pick a category (4 options):
| Option | Description | | ----------------------- | ------------------------------------------- | | Question assumptions | Probe what's being taken for granted | | Build counter-arguments | Argue the strongest opposing position | | Find weaknesses | Anticipate how this fails or gets exploited | | You choose | Auto-recommend based on context |
Step 2b — Refine mode (only when the category maps to 2 modes):
references/mode-selection-guide.md and auto-recommendRead the corresponding reference file for the selected mode. Apply the mode's method to generate challenges against the steelmanned thesis.
| Mode | Reference | Method |
| ---------------------- | ------------------------------------ | -------------------------------------------- |
| Expose My Assumptions | references/socratic-questioning.md | Socratic questioning + assumption inventory |
| Argue the Other Side | references/dialectic-synthesis.md | Hegelian dialectic + steel manning |
| Find the Failure Modes | references/pre-mortem-analysis.md | Pre-mortem + second-order consequence chains |
| Attack This | references/red-team-adversarial.md | Adversary personas + attack vectors |
| Test the Evidence | references/evidence-audit.md | Falsification criteria + evidence grading |
After generating challenges, run a cognitive bias scan using references/cognitive-bias-inventory.md to flag any biases present in the user's reasoning. Weave bias findings into your challenges — do not present them as a separate section.
Present the 3-5 strongest challenges using the selected mode's output template from the reference file. Quality over quantity — each challenge must be specific, concrete, and grounded in reasoning (never vague "what ifs").
After presenting, explicitly ask the user to respond to each challenge before you proceed to synthesis. Do not synthesize prematurely.
Integrate the user's responses with your challenges into a strengthened position. The synthesis must:
After synthesis, offer a second pass with a different mode if warranted — consult references/mode-selection-guide.md for recommended multi-mode sequences.
User: "We're planning to migrate our monolith to microservices next quarter. Challenge this."
Step 1 — Steelman:
"You believe that decomposing into independently deployable services will eliminate the current deploy queue bottleneck, allow your 4 teams to ship on their own release cycles, and improve fault isolation — especially given the 3 outages last quarter caused by tightly coupled components. Is this a fair restatement?"
Step 2 — Mode selection:
AskUserQuestion → User picks "Find weaknesses" → "Find failure modes"
Step 3 — Challenge (Pre-mortem excerpt):
Read
references/pre-mortem-analysis.md. Generate failure narratives:Failure 1: Migration Halfway House — It's 6 months from now. 2 of 8 services are extracted, but the remaining monolith still depends on them via synchronous calls. Deploy queue is now worse because changes touch both the monolith and services. Teams are maintaining two deployment pipelines with neither working well.
Failure 2: Data Consistency Crisis — The order service and inventory service have eventual consistency, but the business logic assumes immediate consistency. A flash sale creates 200 oversold orders in 4 minutes...
Step 4 — Engage:
Present 3-5 failure narratives with consequence chains. Ask user to respond.
Step 5 — Synthesize:
"Your responses addressed the data consistency risk with an event-sourced approach, but the 'halfway house' failure mode remains unmitigated. Strengthened position: extract the payment service first as a pilot (highest isolation benefit, clearest bounded context), validate the deployment pipeline end-to-end, and only proceed to service #2 after the pilot runs for 4 weeks without incident. Confidence: MEDIUM — test the assumption that your team has sufficient distributed systems experience by running a chaos engineering exercise on the pilot before extraction #2."
AskUserQuestion for mode selection — never assume which modereferences/cognitive-bias-inventory.md on every challenge passAskUserQuestion can provide structured optionstools
Reviews a GitHub pull request and posts inline comments plus one consolidated summary, adapting to any codebase by discovering the project's own test runner, requirement specs, and architecture conventions before running six specialized review agents in parallel. Stack-agnostic across language and framework; targets GitHub PRs via the gh CLI. Use when the user says "review PR 128", "review this PR", "code review this PR", or "check this pull request". Do NOT use for creating PRs or responding to review comments (use gh-address-comments), or debugging failing CI checks (use gh-fix-ci).
development
Opinionated Rails conventions: rich models, concerns, CRUD-everything, state-as-records, minimal dependencies, Minitest with fixtures. Load this skill BEFORE any code-level thinking, not only before editing a file. It is required the moment a task touches Rails code in ANY way: designing or even just discussing a data model, schema, migration, entity, association, field, validation, class, or method name; writing, planning, reviewing, analyzing, testing, debugging, or refactoring; or proposing any model, table, column, route, or code snippet inline in chat. If you are about to name a model or sketch a column you are already in scope, even in an exploratory back-and-forth where no file is written yet. Do not let a "we're just discussing" framing defer it. Do NOT use for non-Rails backends, NestJS, or general architecture (use nestjs-modular-monolith or coding-guidelines).
testing
Feature planning and implementation with 4 adaptive phases — Specify, Design, Tasks, Execute. Auto-sizes depth by complexity. Creates atomic tasks with verification criteria, atomic git commits, and requirement traceability. Features an independent Verifier (author != verifier, evidence-or-zero), persistent decision log (STATE.md), and test-coverage-matrix-driven tests, plus a self-improving lessons layer that turns verification failures into reusable project-local guidance. Stack-agnostic. Use when (1) Planning features (requirements, design, task breakdown), (2) Implementing with verification and atomic commits, (3) Validating or verifying an implementation against a spec. Triggers on "specify feature", "discuss feature", "design", "tasks", "implement", "validate", "verify work", "UAT", "record decision", "pause work", "resume work". Do NOT use for architecture decomposition analysis (use architecture skills) or technical design docs (use create-technical-design-doc).
development
Generative Engine Optimization (GEO) specialist — the technical, on-page publishing work that makes a given page or site discoverable, understandable, trustworthy, quotable, and fresh for AI answer engines (Google AI Overviews, ChatGPT Search, Bing Copilot, Perplexity). Use when asked to 'optimize this page/site for GEO', 'optimize for AI search / answer engines', 'get my page cited by ChatGPT/Perplexity', 'improve AI visibility/citability', 'write an llms.txt', 'add citation-ready structure or schema for AI answers', 'otimizar para busca com IA', or to audit/create/improve a codebase for generative search. Do NOT use for AI-driven SEO content strategy or programmatic pages at scale (use ai-seo), classic keyword/SERP ranking (use seo), accessibility (use web-accessibility), or multi-area site audits (use web-quality-audit).