Cheatcode_
Feedback

Front page / By role / Coach and give feedback

Coach and give feedback

Meet each person regularly and help them grow.

For Engineering manager · Knowledge pack

What are you preparing for?

One line is enough. Step 1 asks the rest in your AI app, and nothing is done until you approve the brief it writes.

It never leaves this computer: not uploaded to Cheatcode, never used to train a model. Your AI app reads it from the case folder.

Add details (optional)
Start

Step 1 asks anything else it needs, in your AI app. Nothing you write here or put in the case folder comes to Cheatcode.

In the case folder's materials/: Anything about the situation, with other people's names left out.

The 4 steps

  1. 01

    Understand the situation

    Then you approve, before the next step starts.

  2. 02

    Prepare

  3. 03

    Practice

  4. 04

    Debrief plan

How it does this well

The person does the work live; the harness prepares and debriefs. Not found and out of scope are valid answers here: it says so instead of guessing.

How the field does this well
  • Write the check first: a failing test, a typecheck or a command that comes back pass or fail.
  • Work one unit at a time and show the command that ran and its output as evidence.
  • Have a reader who did not write the change review it against the written spec, and skip style.
  • Keep state in files: a list of checks whose status may only flip to passing, and a progress note.
Ground it first

Ask for, or look in materials/ for: the repository or files to change, how its tests run, and the coding standards. If there is none, say plainly that the result rests on general defaults, and name them.

What stays with the person

Scoping ambiguous work, architecture choices, and the final merge or release. Prepare these; never decide them.

The check that makes it trustworthy

A failing check written first, a passing run shown as output, and an independent review of the change against the spec.

The person's own data

If the person has connected GitHub, Linear, Jira + Confluence, Postgres, read their data through that connector instead of asking for exports, and say which source each number or fact came from. Without one, work from materials/.

Also from people
  • Use structured instruments: the same questions, planned follow ups and a four level rubric with example answers.
  • Remove names, photos and other proxies for protected traits in a script before any model reads a candidate's material. Keep the record IDs the person works with (employee, ticket or case numbers) so every finding names the row they will fix.
  • Score only against the written rubric with evidence quoted.
  • Draft instruments and plans; never rank people.
  • What stays with the person here: every screening, hiring and people decision, and defining the role's attributes.
  • The check here: proxies redacted before the model reads anything about a person, and every judgment quoted against the rubric.

Where things stay

On your computer
Your files in materials/, every result in outputs/, the work itself. It runs in your AI app, on the plan you already pay for.
With Cheatcode
The steps, the gates and how far each case has come, so a case survives closing the app. See your cases.