FG-0515 Gradient Execution
Description
Structured priority ordering for complex problem-solving: identify the target, version it, check for known vulnerabilities, harvest credentials, escalate privileges, THEN deep dive into custom work. Each step narrows the search space before committing effort to the next. The gradient ensures you run cheap, high-signal checks before expensive exploration. Applies to any domain where enumeration precedes exploitation: security, debugging, data analysis, research.
Implementation
Before starting execution, define the gradient for your domain. List checks from cheapest/fastest to most expensive/slowest. Execute in order, recording findings at each step. Only proceed to the next level when the current level is exhausted or points definitively to deeper work. Build the gradient into your runbooks so it becomes automatic, not a decision you make under pressure.
Success Indicators
- Known solutions are found before custom work begins
- Time spent on expensive deep dives decreases because cheap checks catch most issues
- Execution follows a repeatable priority order rather than ad-hoc exploration
- Team members can articulate their gradient and why each step is ordered where it is
Failure Modes
- Skipping early gradient steps because the problem looks complex, missing the easy win
- Gradient too rigid: refusing to jump ahead when strong evidence points to a specific cause
- Not updating the gradient as new cheap checks become available
- Treating the gradient as a checklist rather than a priority ordering, wasting time on irrelevant steps
Sub-Methods 3
Define the specific gradient for your domain. In security: identify, version, CVE check, creds, privesc, custom exploitation. In debugging: reproduce, logs, known issues, instrumentation, deep trace. Each domain has a natural gradient from cheap to expensive. Map it once, refine as you learn.
At each gradient step, explicitly decide: proceed deeper or stop here. Record what the current step found and why the next step is necessary. This prevents skipping ahead on instinct and creates an audit trail of the execution path.
After each engagement, review the gradient. Did a step consistently produce nothing? Move it down or remove it. Did you keep finding issues that should have been caught earlier? Add a step. The gradient evolves with your domain knowledge.