define-goal

name	define-goal
description	Shape a fuzzy intention into a concrete, measurable goal with explicit success evidence before starting work. Use when the user asks to define a goal, clarify success criteria, sharpen an objective, or turn "make X better" into something verifiable. Useful as a quick pre-step before planning, debugging, performance work, research, or operations work.
metadata	{"short-description":"Turn fuzzy intent into a measurable goal"}

Define Goal

Overview

Shape the user's intent into an objective an agent can pursue honestly. Prefer measurable outcomes, explicit evidence, and bounded scope over activity descriptions.

This skill is goal-shaping only. It does not produce plans, specs, ledgers, or implementation artifacts — pair it with ship-first-plan (for feature work) or proceed directly to implementation when the goal is clear.

Workflow

Operate in read-only mode. Do not write or update files.

1. Confirm goal definition is needed

   • Use this skill when the user explicitly asks for it, wants help turning an intention into a clear objective, or when downstream planning/implementation work would be at risk without a sharper success criterion.
   • If the user has already stated a measurable, verifiable goal, skip this skill and proceed with the actual work.

2. Restate the likely goal in concrete terms A usable goal names:

   • the specific outcome that will be true
   • the main artifact, system, repo, environment, or user-facing behavior involved
   • how completion will be verified
   • what is in scope
   • what is out of scope when ambiguity would matter
   • the stop condition for asking the user instead of grinding

3. Make it quantitative when the domain supports it Prefer numbers that represent real success, not decorative precision:

   • pass/fail validators: exact tests, checks, CI jobs, evals, commands, or acceptance criteria
   • quality thresholds: latency, error rate, cost, accuracy, recall, precision, coverage, flake rate, bundle size, memory, uptime, completion rate
   • artifact constraints: file paths, affected modules, allowed commands, output formats, target environments, deadlines, or maximum blast radius
   • evidence counts: number of reproduced failures, successful reruns, reviewed examples, migrated records, addressed comments, or verified cases

4. Repair weak goals before accepting them

   • Rewrite vague goals into measurable objectives when local context makes the rewrite safe.
   • Ask one concise clarification question when the missing detail changes the intended outcome or validation.
   • Reject pure activity goals such as "make progress", "keep investigating", "improve things", or "work on X" unless they are sharpened into a verifiable outcome.

5. Present the goal for confirmation

   • Output a single concise objective string that includes the verification evidence inline.
   • Include scope bounds when they constrain the work.
   • Ask the user to confirm or adjust before any downstream work begins.

Goal Quality Bar

Before accepting a goal, it should answer:

• What concrete thing will be true when this is done?
• What evidence will prove it?
• What quantitative or binary threshold defines success?
• What scope boundaries matter?
• What should cause the agent to stop and ask?

Good:

Reduce checkout API p95 latency below 250 ms for the documented slow path by making the smallest safe server-side change, then verify with npm run test:checkout and the existing local latency benchmark showing p95 under 250 ms across 3 consecutive runs.

Good:

Resolve the open review comments on PR 123 that request code changes, update only the affected auth files and tests, and verify with the targeted auth test command plus gh pr view 123 showing no unresolved change-request threads.

Weak:

Make checkout faster.

Weak:

Keep investigating the PR comments.

Quantification Heuristics

• Bugs: define success as reproduction first, fix second, and a failing-then-passing validator when possible.
• Tests: name the exact command and required pass condition.
• Performance: name the metric, target threshold, measurement method, and number of runs.
• Quality work: define an observable acceptance bar such as reviewed examples, lint/typecheck/test pass, or user-approved artifact.
• Research: define the decision the research must enable, the sources or systems in scope, and the evidence standard.
• Operations: define healthy state, monitoring window, failure threshold, and rollback or escalation trigger.
• Features / shipping: define the user-visible behavior that becomes true and the demo that proves it (then hand off to ship-first-plan for slicing).

Clarifying Questions

Ask only when a reasonable rewrite would risk pursuing the wrong outcome. Keep the question short and oriented around the missing validator or scope boundary.

Useful question shapes:

• "What metric should define success here: latency, cost, accuracy, or user-visible behavior?"
• "Which environment should I verify against: local, staging, or production?"
• "What is the minimum evidence you want before I consider this done?"

If the user cannot provide a metric, propose the most honest binary validator available and ask for confirmation.