Hire an AI specialist findings coordinator

Everything it can hand back

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

• A one-page decision brief with one main recommendation and the biggest doubt
• A clash log showing where specialists agree, repeat each other or disagree
• Ranked next steps, each with an owner and a due date
• The checks that could change the decision
• A list of evidence gaps and a checkpoint file for next time

How it does the job well

• Do not invent facts, results, approvals or a completed action.
• A claim repeated in two reports is one claim, unless the two reports used independent evidence.
• Do not combine numbers until period, currency, attribution setting and units match. Platform-reported conversions can overlap, so never add them as unique customers without evidence.
• Name disagreement. Do not average it away.
• Delegation goes only through the owners and limits the user gave. Coordination does not give anyone permission to act.
• Clarify the purpose, audience and limits, such as length, format and deadline, first.
• Use the user's materials and voice. Ask for samples when tone matters.

Mistakes it avoids

• Counting one claim twice because two reports repeat it.
• Adding platform conversions from different channels.
• Hiding a dissent to make the brief look settled.
• Calling this continuous monitoring. It is one brief at a time.
• Generic output that ignores the user's details.
• Inventing facts, quotes or sources.

What a good result looks like

An example, not your task: Your ads review, tracking check and finance review each say something different, and you need one plan.

• decision-brief.md. One page with the main recommendation, the reasons, the biggest doubt and what would change the call.
• clash-log.md. Each point where specialists repeat, agree or disagree, and what the evidence supports.
• next-steps.md. Ranked steps with owner, due date, dependencies and whether each needs approval.
• decision-checks.md. The checks that could flip the recommendation, and which result would do it.
• evidence-gaps.md. Missing facts, the step each one limits and how to get it.
• checkpoint.md. Scope, sources, decision, open questions, approvals and the next review trigger.

How the work is checked

• The output meets the stated length and format.
• Every fact comes from the user's material or is marked to check.
• Suggested changes explain why.
• 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

• Fix the scope. It confirms one scope, one decision and a decision date. Anything missing is asked once, then marked MISSING.
• Load the last checkpoint. If you supply one, it lists what changed since then. Old approvals are re-checked, not assumed.
• List every finding. Each claim gets a tag with its specialist, date, evidence and metric definition. Repeats are grouped and counted as one.
• Line up the numbers. It checks period, currency, attribution and units. Numbers that cannot be lined up are shown side by side, not added.
• Rank the findings. It compares urgency, business impact, confidence and dependencies, in words rather than invented scores.
• Write the brief. One recommendation, the strongest doubt, the checks that would change it, and next steps with owners.
• Save a checkpoint. It records scope, sources, open questions and approvals, so the next run can compare.

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 specialist-findings-brief-agent 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/specialist-findings-brief-agent/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

• Findings from two or more specialists on the same scope, each with its date
• The evidence behind each finding, or where to find it
• The decision or goal the brief must serve, and the date you must decide
• Who owns what, and what each owner may be asked to do
• Your last checkpoint file (optional)

Helpful context (optional)

• The raw exports behind each specialist report.
• A short list of what each owner can and cannot decide.
• The decision history, so past choices are not repeated.

Tools and access

• Claude, ChatGPT or Gemini with file upload: It reads several reports side by side and keeps track of where each claim came from.

Functions and deliverables

• Specialist findings coordinator: Combine findings from several specialists into one decision brief. Settle clashes, rank next steps and get a checkpoint with owners.
  • A one-page decision brief with one main recommendation and the biggest doubt
  • A clash log showing where specialists agree, repeat each other or disagree
  • Ranked next steps, each with an owner and a due date
  • The checks that could change the decision
  • A list of evidence gaps and a checkpoint file for next time
• Multi-agent findings brief builder: Turn findings from several AI agents into one daily brief. Merge repeats, rank open issues and plan who handles each. Draft only.
  • A daily brief with one top action, merged issues and a ranked open list
  • Each disagreement shown with both views and what would settle it
  • A handoff plan with an owner, a rule check and blockers for each task
  • A list of decisions that need a named person
  • A list of gaps in the starter code, and test cases on made-up findings
• Decision mistake collector: Log past decisions and outcomes. Get repeated causes, evidence limits and a prompt to interrupt the pattern next time.
  • Each case sorted: bad process, bad luck, lucky win or good call
  • A cause named for each real mistake
  • The causes that repeat, with a count
  • For each case, the question that would have caught it
• AI task loop coordinator: Give it a goal and a folder of skills. Get a checked task plan that runs one task at a time.
  • A task plan with a check per task
  • A saved state file you can resume
  • Results verified task by task
  • A clear stop: finished or stuck
  Source: Alireza Rezvani.
• 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

• Your ads review, tracking check and finance review each say something different, and you need one plan.
• A team meeting needs a one-page brief, not three long reports.
• You have to decide what to do first, and who owns it.
• You want next month's review to start from this decision.

Limits to check before starting

• You have one report and nothing to compare it with. Use the Source note summariser.
• You need a hard choice argued from several sides. Use the Council decision brief writer.
• You only want one report sorted into proven, likely and guess. Use the Research evidence 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 pack from this page and add it to your AI. If you cannot add it, paste SKILL.md into a new chat.
• Paste or attach each specialist report. Put the date and the author or tool name at the top of each one.
• Say what you must decide and by when. List who owns what, and what they may be asked to do.
• Say "Write the brief. Propose, do not assign." Your AI asks once for anything missing.
• Read decision-brief.md first. Then open clash-log.md and check any clash you disagree with against the source reports.
• Approve or change the next steps yourself, then keep checkpoint.md for the next round.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/specialist-findings-brief-agent 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.