Hire an AI equity valuation scenario reviewer

Everything it can hand back

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

• A research report with the thesis, counter-evidence, risks and what to monitor
• Valuation scenarios, with the inputs for each case shown
• A source ledger that separates reported facts, estimates and assumptions
• A ranked list of unsupported claims and the source each one needs
• A list of what is MISSING and which checks were missed

How it does the job well

• Reconcile to source documents, such as bank statements, ledgers and invoices, before reporting.
• Show the calculation, inputs and period for every figure.
• Label actuals, estimates and forecasts clearly.
• Send tax, legal and regulatory questions to a qualified professional.

Mistakes it avoids

• Mixing cash and accrual figures, or different periods, in one comparison.
• Rounding or formula errors that do not tie back to totals.
• Giving personal investment advice.

What a good result looks like

An example, not your task: You wrote a stock research note and want its scenarios checked before a reader sees it.

• outputs/step-01-equity-research-valuation-report-builder.md. The research report, valuation scenarios, source ledger and validator result.
• outputs/step-02-unsupported-claim-spotter.md. Claims ranked by risk, each with the kind of source that would settle it.
• Assumption challenge list. The scenario assumptions a reader should test, such as growth and discount rate.
• HAND-BACK SUMMARY. Files saved, sources used, MISSING lines, checks missed and one suggested next step.

How the work is checked

• Every figure, fact and date traces to a filing or a dated price, or is labelled as an assumption.
• No missing figure was replaced with zero or a guess.
• Nothing was sent, filed, paid or changed.
• CHECKS MISSED is filled in, with "none" only if every check ran.
• 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 would. This is a way of reviewing the work. It does not mean they wrote or endorse it.

Capabilities

• Equity research and valuation report builder. Runs the valuation engine and the matching strict validator on scenario inputs and arithmetic. Keeps the source ledger and checks the rendered report. Treats sensitivities as scenarios, never price predictions. Hands step 2 the report, scenarios and sources.
• Unsupported claim spotter. Checks the thesis and valuation commentary against the filings, the dated price and the scenario results. Flags claims that sound certain and any missing counter-evidence. Gives no advice on what to buy or sell.

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 review-equity-valuation-sensitivity-assumptions 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/review-equity-valuation-sensitivity-assumptions/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 company name, stock exchange and reporting currency
• Your research date and time horizon
• Company filings and data you are allowed to use, as files your AI can open or as pasted text
• A share price with its date
• Your existing valuation model or scenario assumptions (optional)
• A draft thesis or commentary to check (optional)

Helpful context (optional)

• Filings for at least the last three years.
• A named reviewer who will check the sources.

Tools and access

• An AI that can run Python and read files: The valuation engine and strict validator are scripts in the pack. Without Python you get a plan, not a checked report.

Functions and deliverables

• Equity valuation scenario reviewer: Share filings, dated market data and your valuation model. Get checked scenarios, source gaps and questions that challenge the assumptions.
  • A research report with the thesis, counter-evidence, risks and what to monitor
  • Valuation scenarios, with the inputs for each case shown
  • A source ledger that separates reported facts, estimates and assumptions
  • A ranked list of unsupported claims and the source each one needs
  • A list of what is MISSING and which checks were missed
• Equity research and valuation report builder: Build a stock research report with value ranges, source checks and clear assumptions from company filings and a dated share price.
  • A research report in HTML with the thesis, counter-evidence, risks and what to monitor, each fact tied to a source
  • Valuation scenarios with the inputs for each case shown
  • Labels that separate reported facts, estimates and assumptions, with source dates
  • A validator result on structure, valuation and source trail
  • A PDF version of the report, if you ask for it
  Source: evilhill.
• Unsupported claim spotter: Find the claims in your draft that need a source before you publish. Each one is ranked by risk, with the kind of source to look for.
  • A summary of how many claims need support
  • A table of claims ranked by risk
  • The kind of source that would settle each
  • Internal conflicts in your draft
  Source: Pro Skill Packs.
• Early-stage startup valuation scenario builder: Estimate a young company’s value using stated assumptions and several methods. Get a valuation range, the working and key gaps to check.
  • A Berkus method breakdown across five risk areas
  • A VC method calculation from exit value to pre-money value
  • A suggested equity stake and post-money value
  • A final pre-money range with reasons
  Source: GAJETOso.
• me.md builder: Answer a few questions, or let your AI use what it remembers. Get one private file that tells any AI who you are.
  • One me.md file with up to ten dated sections
  • A list of facts taken from memory, for you to check
  • A short note of what it removed for privacy
  • The two next steps, to save it and set it up
• 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

Tasks you can use it for

• You wrote a stock research note and want its scenarios checked before a reader sees it.
• A reviewer asks what happens to value if growth or the discount rate changes.
• You inherit someone's valuation and need to see which claims lack a source.
• You must show where each number in a report came from.

Limits to check before starting

• You want a quick fair value for one ticker: use the Company fair value modeler.
• You want to compare a DCF, peer multiples and past deals: use the Valuation method comparer.
• You need a buy or sell call, or advice on your own money: ask a licensed adviser. This gives a research draft only.
• You only want to fact-check a draft: use the Unsupported claim spotter on its own.

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 pack. It holds HANDOFF.md and two skill files, in the order they run: Equity research and valuation report builder, then Unsupported claim spotter. Add it to an AI that can read files and run Python 3.9 or later.
• Give the company, exchange, currency, research date and horizon. Attach the filings and the dated share price.
• Ask your AI to follow the pack, save a file for each step and stop a step when a check fails.
• Read the validator result. A pass checks structure only. It does not prove your sources or conclusion.
• Check each figure against the cited filing, then work through the list of unsupported claims before you share the report.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/review-equity-valuation-sensitivity-assumptions 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.