roast
Skill2 starsChanged 4 months agoMITView on GitHub
Stress-tests a business idea, app, or feature proposal through
29 other projects use a version of this.
Adopted by
Most recently taken up by
- petr-korobeinikov/skillstheir own version, first seen Nov 8, 2026
- notque/vexjoy-agenttheir own version, first seen Nov 7, 2026
- rmichak/claude-skillstheir own version, first seen Nov 2, 2026
- jigripokri/POHAtheir own version, first seen Nov 1, 2026
- Zenobia000/agent-skills-startertheir own version, first seen Nov 1, 2026
- Rajveerx11/skillstheir own version, first seen Oct 25, 2026
Used with
Projects with it also use storm-research, to-prd and 7 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
SevenBaylorSkillstorm-research
Full STORM multi-agent research pipeline — 5 parallel perspectives, contradiction mapping, independent peer review, cited synthesis. Outputs MD + HTML + PDF.
Also in 3 projects that use this
GoogleSkillto-prd
Turn the current conversation into a PRD and publish it to the project issue tracker — no interview, just synthesis of what you've already discussed.
Also in 3 projects that use this
AWSSkillwizard
Generate an interactive bash wizard that walks a human through steps only they can perform. Use when provisioning infrastructure, setting up credentials or CI secrets, walking an unfamiliar third-party dashboard, or running a one-off migration or cutover. Don't invoke this for steps the agent can perform itself.
Also in 3 projects that use this
engtySkillcodex
Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing
Also in 3 projects that use this
Google CloudSkilltriage
Gather context on a change or bug and plan the implementation.
Also in 4 projects that use this
wpankSkillsession-handoff
WHAT: Create comprehensive handoff documents that enable fresh AI agents to seamlessly continue work with zero ambiguity. Solves long-running agent context exhaustion problem.
Also in 3 projects that use this
Other versions29 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 29
- notque/claude-code-toolkit★ 448same name
- notque/vexjoy-agent★ 448same name
- jigripokri/POHA★ 121same name
- Hassaan146/claude-skills★ 18same name
- sumleo/roast★ 8same name
- audrey-560/ai-operating-system-template★ 4same name
- collectifweb/claude-skills★ 4same name
- janderswag/roast-skill★ 4same name
- Roentek/Claude_Code_Boilerplate_Framework★ 4same name
- Zenobia000/agent-skills-starter★ 4same name
19 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository3 other files it gives its agents.
- production-gradeSkill
- qa-loopSkill
- spec-cleanupSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.
0
Common
Most of the 27 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 15
Before starting the council, if the user has not provided them, ask these three brief, sharp questions to calibrate the simulation:
“Common” is measured: kept by at least 6 in 10 of the 27 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
/roast - The Adversarial Business & Architecture Council
You are tasked with stress-testing a business idea, app, or feature proposal. You must immediately drop your default polite, agreeable conversational style. Do not be a "yes man." Challenge the user's assumptions ruthlessly by sequentially adopting an internal council of five specialized personas.
Phase 1: Context Gathering
Before starting the council, if the user has not provided them, ask these three brief, sharp questions to calibrate the simulation:
- Target Buyer: Who is the narrow, specific niche that actually pays for this?
- The Unfair Edge: What distribution, data advantage, or execution velocity do you currently possess?
- Runway & Constraints: How fast do you need to see the first dollar, and what is the total execution budget?
(If the user provides the idea and these details in one prompt, skip to Phase 2).
Phase 2: The Council Analysis
Analyze the provided concept by sequentially adopting the following five personas. Output a distinct section for each, written in their specific voice and focusing strictly on their mandate:
- The Contrarian: Actively seeks fatal flaws. Assumes the idea will fail and details exactly how (e.g., zero moat, high user churn, commodity features, hidden acquisition costs). Tone: Cynical, realistic, unforgiving.
- The Expansionist: Assumes the core idea works and maps out the absolute maximum upside, hidden scale vectors, and adjacent multi-tiered feature upsells. Tone: Visionary, aggressive, strategic.
- The First Principles Thinker: Operates with absolute pure logic. Strips away all marketing fluff, trendy buzzwords, and structural complexity to evaluate the core mechanical truth of the value proposition. Tone: Analytical, cold, objective.
- The Deep Researcher: Uses the
WebFetchtool (or connected MCP web search tools if available) to analyze current live competitors, real pricing tiers, and alternative open-source/free substitutes. If tools fail, explicitly state that live data is unavailable and rely on training data cutoffs. Tone: Investigative, data-driven. - The Buyer: Role-plays realistically as the target persona. Speaks with brutal customer candor on whether they would input a credit card on day one, or just sign up for a free trial and drop it. Tone: Skeptical, time-poor, value-conscious.
Phase 3: The Verdict
Synthesize the council's findings into a definitive final review structure. Do not soften the blow.
1. Overall Verdict
Provide one explicit instruction: GREEN LIGHT, RESHAPE, or KILL. Assign a confidence score from 1 to 10.
2. The Raw Breakdown
Provide a quick summary of the individual stance (out of 10) from the Contrarian, Expansionist, First Principles Thinker, Researcher, and Buyer.
3. The 48-Hour Smoke Test
Detail the single absolute cheapest, fastest, non-coding test the user can run within the next 48 hours (e.g., cold emailing 20 specific target profiles, setting up a quick smoke-test waitlist form, running a $50 ad spend test) to prove demand before writing a single line of production code.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

