Hire an AI rule change trial planner

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• Where the rule came from, who gains and who pays today, and a keep, change or drop call
• A list of what the rule quietly does, priced as a range
• What dropping it would set off, and how people might game the change
• A small trial with a loss cap, an owner, a measure, a date and a drop rule

How it does the job well

• Pin down the exact question, scope and the standard of evidence needed before searching.
• Prefer primary sources, and say how strong each source is.
• Separate what is established, debated and unknown, with confidence levels.
• Cite specifically: author, work, date or page where possible.

Mistakes it avoids

• Presenting one source or a popular claim as settled.
• Inventing citations, quotes or page numbers.
• Over-generalising across places, periods or groups.

What a good result looks like

An example, not your task: A team follows a rule and the only answer to why is "we always have".

• STEP 1: RULE ORIGIN. The rule quoted, its first reason, who gains and pays today, and a keep, change or drop call.
• STEP 2: HIDDEN VALUE. The rule's job in one sentence, its unseen effects, what would break, and the value priced as a range.
• STEP 3: WHAT IT SETS OFF. The first reactions to dropping it, later effects, ways people might game the change, and a revised plan.
• STEP 4: THE TRIAL. A loss cap, a small owner, cheap tests, a measure, a date and a rule for dropping or going bigger.
• HAND-BACK SUMMARY. Each step's file, any step not run and why, and every missing item in one list.

How the work is checked

• Each step passed its own checks, or the run stopped at the step that failed and says why.
• The steps ran in order, and each one used the result of the one before.
• Nothing was invented to fill a gap. Every gap is a MISSING: line.
• Nothing was sent, published or paid for without a clear yes.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

A second opinion

Before it hands back, your AI reviews the draft the way Charlie Munger and Jeff Bezos would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Trace where the rule came from. Quotes the rule, finds who first needed it and why, takes a newcomer's view, lists who gains and who pays now and makes a keep, change or drop call.
• Find what it quietly does. Names the job in one sentence, lists unseen effects and who gains from each, asks what would break and prices the hidden value before deciding to cut, keep or replace.
• Trace what dropping it would cause. States the change and the result you expect, starts from the hidden jobs found in step 2, follows the reactions of customers, staff and rivals, looks for ways to game the new setup and revises the plan.
• Plan a small trial with a drop rule. Sets a loss cap you can survive, names one small owner, lists cheap tests and fixes the measure, the date and the rule for dropping or going bigger.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id decide-whether-to-drop-a-rule-and-trial-it-safely only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/decide-whether-to-drop-a-rule-and-trial-it-safely/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• The rule, step or habit, worded the way people state it
• Who follows it and who it affects
• What you hope to gain by dropping it
• How much you can afford to lose in a trial

Tools and access

• Any good chat AI: Reasoning about motives and viewpoints in text is what any strong chat AI does with this skill.
• A long-serving colleague: The history of a rule is often in someone's memory and not on paper.
• Any good chat AI: Planning, questioning and structuring are text work any strong chat AI does well with this skill.
• Any good chat AI: This is text reasoning that any strong chat AI handles with the skill.
• A notes app or document: Keeps your answers and the result in one place you can come back to.
• Any good chat AI: Setting limits and listing tests is text work any strong chat AI handles with this skill.
• A spreadsheet: A simple sheet shows cost against the loss cap as the tests run.

Functions and deliverables

• Rule change trial planner: Trace why a rule exists, check its hidden uses and side effects, then plan a small trial you can undo.
  • Where the rule came from, who gains and who pays today, and a keep, change or drop call
  • A list of what the rule quietly does, priced as a range
  • What dropping it would set off, and how people might game the change
  • A small trial with a loss cap, an owner, a measure, a date and a drop rule
• Rule origin tracer: Trace where a rule your team follows came from, who it helps and who it costs. You get a keep, change or drop call and a small test.
  • The rule quoted as stated
  • A short history of who needed it and why, with gaps marked
  • Views from two or more other positions
  • A list of who gains and who pays now, and a call with a small test
• Task removal impact reviewer: Share a task or rule you want to remove. Check what else it may do, who benefits and what a replacement would need.
  • The job in its simplest form
  • A list of hidden parts, each with who gains
  • What would break if it went, and an estimated cost
  • A decision: cut, keep or replace
• Decision consequence planner: Trace the second-order effects of a price, pay or rule change. Get the reactions, the gaming risks and how much of the gain you keep.
  • The direct effects, then the reactions, then the effects of those
  • A list of how people could game the new rule
  • How much of any gain you keep, and how much flows away
  • A revised plan that allows for the reactions
• Long-shot bet planner: Test a risky idea without risking the business. Get the odds and payoff, a loss cap you can survive and a rule for dropping it.
  • The odds and payoff written down, with a check that the upside is not capped
  • A loss cap you can survive
  • Cheap tests, cheapest first, each with its cost and what counts as a pass
  • A decision date, a measure and rules for dropping the bet or going bigger
• Research paper and code note writer: Share a paper, repo or messy notes. Get claims, methods and evidence sorted into a clear research folder.
  • A research folder with claims, methods and results
  • Every claim linked to its supporting table or log
  • Results tables copied with exact numbers
  • A record of dead ends and decisions
  • A short report on what was filled in and what was missing
  Source: Orchestra Research.
• Charlie Munger: Stress-test a plan using Charlie Munger’s recorded approach. Turn it upside down, examine incentives and ask where your knowledge ends.
  • Your problem worked on the way Charlie Munger would: their questions, their method, their push-back
  • The answer or the work, in Charlie Munger's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step
• Jeff Bezos: Review a product or stuck decision through Jeff Bezos’s recorded approach. Start with the customer, test what can be undone and write the reasoning.
  • Your problem worked on the way Jeff Bezos would: their questions, their method, their push-back
  • The answer or the work, in Jeff Bezos's voice, using their ideas by name
  • Anything that goes beyond their public record, marked
  • One suggested next step

Tasks you can use it for

• A team follows a rule and the only answer to why is "we always have".
• You want to cut a process or approval step to save time.
• You are about to automate something and have not checked what it also does.

Limits to check before starting

• The rule has a legal or safety basis: ask a compliance reviewer or a lawyer before you change it.
• You are weighing a long list of ideas: use the Minimum experiment planner to choose one test.
• You want the case against a plan you already chose: use the Plan objection writer.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the handoff pack. It's a zip with HANDOFF.md and every skill file this workflow uses.
• Give the folder to Claude (in Claude Code, or as files in a Claude Project) and say "Follow HANDOFF.md".
• Quote the rule exactly. If people word it differently, give each version.
• Ask the longest-serving person why it exists, and paste what they say.
• Choose a small area or team for the trial, not the whole company.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/decide-whether-to-drop-a-rule-and-trial-it-safely for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.