Hire an AI decision bias and incentive reviewer

Everything it can hand back

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

• Your clear case, written before the pressure check
• The pressures at work, each tied to a fact in your situation, and a calmer version of the choice
• A table of who is rewarded for what, where it pulls the wrong way and a fix
• A list of ways the plan could fail, survival risks first, with a change for each
• The bet stated, and a set of attacks for the people you pick to argue against it

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: You are about to sign, hire, invest or launch and want the plan attacked before you commit.

• Step 1: Decision pressure check. Your clear case, the pressures at work, how many stack up, and a cooling-off step.
• Step 2: Incentive check. A table of who is rewarded for what, where it pulls the wrong way, and a fix.
• Step 3: Failure-first check. What would guarantee failure, survival risks and other ways to fail, and a change for each.
• The bet and the attacks. The bet stated plainly, and attacks on market, timing and team for the people you pick.
• 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

• Your clear case comes before any pressure is named, and each pressure is tied to a fact in your situation.
• The incentive fix pays for the outcome you want and says how it could be gamed.
• The failure list is specific to this plan, with survival risks first.
• Nothing about the people or the deal is invented.
• 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 Daniel Kahneman would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Writes your clear case. Writes your reasons first, then checks them for incentives, past promises, the crowd and fear of loss.
• Counts the pile-up. Counts how many pressures push the same way and names the strongest one or two, with a step to cool them down.
• Checks the rewards. Takes the calmer plan and lists how each person is paid, what that really encourages and where it differs from what you want.
• Tests the fix. Finds a small change to the reward that pays for the outcome you want, and checks it can't be gamed.
• Starts from failure. Asks what would guarantee failure and checks the revised plan against the standard mistakes in your field.
• Sets up the attacks. States the bet and who pushes it, then lists attacks on market, timing and team for the people you pick to argue against it.

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 plan-bias-check 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/plan-bias-check/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 decision, and the date you must make it
• Your reasons for it, in your own words
• Who is involved, what each person gains and what you have already said or paid (optional)
• How each person or party is paid or rewarded
• What the plan depends on, such as money, people or one big customer (optional)

Tools and access

• Any good chat AI: It is a reasoning task on text that any strong chat AI can do with this skill.
• A trusted second reader: A person outside the situation can spot pressures you cannot see from inside.
• Any good chat AI: It reasons about people, rewards and plans in text, which any strong chat AI handles with this skill.
• A spreadsheet: Easier to try a new pay rule on past numbers and see what it would have paid.
• Any good chat AI: Listing and testing failure modes is text reasoning that any strong chat AI handles with this skill.
• A spreadsheet: Handy for putting numbers on dependencies, cash and runway.

Functions and deliverables

• Decision bias and incentive reviewer: Describe your decision and the evidence behind it. Get judgment risks, incentive conflicts and possible failures with proposed safeguards.
  • Your clear case, written before the pressure check
  • The pressures at work, each tied to a fact in your situation, and a calmer version of the choice
  • A table of who is rewarded for what, where it pulls the wrong way and a fix
  • A list of ways the plan could fail, survival risks first, with a change for each
  • The bet stated, and a set of attacks for the people you pick to argue against it
• Decision bias checker: Check a decision for pressure before you commit. Describe it and get the pressures bending your judgment, plus a calmer version of the choice.
  • Your rational case, written before the pressure check
  • The pressures present, each tied to a fact in your situation
  • A flag when three or more stack, with a cooling-off step
  • A calmer version of the choice, and a rating of how much to trust your gut
• Incentive checker: Check your pay plan or deal with this incentive checker. See who is rewarded for what, where it pulls the wrong way, and get a fix.
  • A table of people, rewards and the behaviour each really encourages
  • Each mismatch with a concrete example
  • A fix that pays for the outcome you want, with a loophole check
  • A note on pay fairness and on which work is routine and which is thinking
• Plan failure reviewer: Find the ways your plan could fail before you commit. Paste your plan and goal, and get ranked failure risks with a fix for each.
  • The goal in one line
  • A list of failure modes specific to your plan, survival risks first
  • A change to the plan for each failure
  • The bet stated, and a set of attacks for the people you pick to argue against it
• 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
• Daniel Kahneman: Check a forecast or choice using Daniel Kahneman’s recorded approach. Compare past cases and ask what the story leaves out.
  • Your problem worked on the way Daniel Kahneman would: their questions, their method, their push-back
  • The answer or the work, in Daniel Kahneman'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

• You are about to sign, hire, invest or launch and want the plan attacked before you commit.
• A deal feels urgent, and you want to know whether the urgency is real.
• Your team's pay or targets might be pulling people the wrong way and you can't tell.

Limits to check before starting

• You face a small choice that is easy to undo: use a simple pros and cons list instead.
• You have already signed and cannot change course: talk to a trusted adviser about how to limit the damage.
• You want to judge whether advice is honest: use the Advice credibility checker.

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".
• Describe the decision and your reasons. It works step by step and saves each result.
• Wait a day before you act on the calmer version, and give the attacks to the people you chose.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/plan-bias-check 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.