Skills Profile

Identify the highest-cost rules and encode them with appropriate priority in the AI's context

Sense that the AI's current trajectory will not produce the desired result before significant divergence occurs

Get a taste of it. Fire Fast, Retract Fast in Frontier & Serendipity— Reddit Discovery & Analysis

Adjust how long to let the AI run before intervening based on task risk, model behaviour, and observed trajectory

Get a taste of it. Fire Fast, Retract Fast in Frontier & Serendipity— Reddit Discovery & Analysis

Assess whether the AI can correctly interpret and act on its own verification output

Identify when AI self-verification is insufficient and human judgement must re-enter the loop

Get a taste of it. Validate Before Celebrating in Scoring & Calibration— Reddit Discovery & Analysis

Match task complexity and type to model capabilities, switching when the work demands it

Begin every non-trivial task by asking the AI to plan before executing

Get a taste of it. Explore Before Building in Bootstrap & Scaffold— Reddit Discovery & Analysis

Provide specific, actionable corrections that address the root cause of the AI's wrong direction

Anticipate how a specific model will approach a task given the current context and constraints

Assign tasks to parallel sessions based on relationships, context requirements, and dependency minimisation

Make build, test, and lint commands available to the AI in the session context

Adjust context loading, window management, and CLAUDE.md structure for different models' behaviours

Maintain cognitive awareness of multiple concurrent sessions without losing track of each session's state and needs

Choose when to end and start sessions based on the cost of reconstructing context versus the cost of accumulated noise

Select verification tools whose output the AI can reliably interpret and act on

Classify interrupt triggers as direction errors, quality errors, or context errors to choose the right intervention

Get a taste of it. Constrain the Proposal in Bootstrap & Scaffold— Reddit Discovery & Analysis

Use interrupt and rewind mechanics fluidly as part of the standard workflow, not just as emergency stops

Commit at meaningful checkpoints so recovery always has a clean state to return to

Choose commit points based on logical completeness, creating a chain of recoverable states

Structure the workflow so that recoverable states exist at predictable intervals, not just when remembered

Design feedback mechanisms that allow the AI to check its own output and self-correct

Get a taste of it. Fix What the Question Found in Signal Integrity— Reddit Discovery & Analysis

Structure AI verification-and-fix cycles that narrow toward correctness rather than oscillate

Construct reusable verification infrastructure — test environments, CI pipelines, deployment checks — that make verification repeatable