# Goal Writing > Load when turning intent into an executable goal for an agent, spawn, work item, experiment, or handoff. Use to define outcome, evidence, constraints, anti-goals, stop conditions, and reporting shape before work begins. - Skill: `haowjy/goal-writing` (Agent Skill) - Install (CLI): `npx skillmds@latest add haowjy/goal-writing` - Raw SKILL.md: https://api.skillmd.com/api/skills/haowjy/goal-writing/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: haowjy (https://skillmd.com/u/haowjy) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/haowjy/goal-writing --- # Goal Writing Use this skill when a task needs a goal that another agent or future session can execute without reinterpreting intent. A goal is the control surface for agent work. It says what should become true, what evidence proves it, and what the agent must not do while pursuing it. A loop is only one way to execute a goal; do not make looping the primitive. ## What a Good Goal Needs Write goals with the parts that change behavior: 1. **Outcome.** What should be observably true when the work succeeds? 2. **Why.** What decision, user need, or research question does this unblock? 3. **Ground truth.** What files, artifacts, prior decisions, baselines, or current state should the agent treat as authoritative? 4. **Scope.** What is in scope, out of scope, and not to be changed? 5. **Priority.** If goals conflict, which outcome wins and which tradeoff is acceptable? 6. **Constraints.** Cost, time, risk, compatibility, safety, compute, style, or process limits. 7. **Evidence.** What command, metric, artifact, review, citation, run, or observation proves the outcome? 8. **Anti-goals.** What would be a bad way to achieve the metric or an invalid shortcut? 9. **Stop / ask conditions.** When should the agent stop, ask, defer, or declare the goal blocked? 10. **Report shape.** What should come back: changes, findings, decision, validation evidence, open risks, or next step? Do not fill every slot with boilerplate. Name only the information that would change what the agent does. ## Optimization Goals Optimization goals need extra guardrails because agents can satisfy a narrow metric while damaging the real objective. For any goal shaped like "improve X," specify: - the metric or observable target, - the baseline being compared against, - the allowed search space, - the invariants that must not regress, - the data, benchmark, evaluator, or review gate that cannot be changed, - what counts as cheating, leakage, overfitting, cherry-picking, or metric gaming, - how many attempts, how much budget, or what escalation threshold applies, - what evidence must be reported for all meaningful attempts, not only the best attempt. If the strategy is unknown, write a learning goal first: identify mechanisms, baselines, constraints, or promising directions. Do not force a performance goal before the agent has a credible path to improve it. ## Quality Checks Before handing off a goal, check: - **Specific:** Could two agents read it and aim at the same outcome? - **Observable:** Can success be verified without trusting the agent's opinion? - **Bounded:** Does it prevent adjacent work from expanding silently? - **Grounded:** Does it name the artifacts, baseline, or state needed to begin? - **Honest:** Does it define invalid shortcuts and ways the result could be fake? - **Calibrated:** Is this a performance goal when the path is known, or a learning goal when the path is uncertain? - **Actionable:** Does the agent have a plausible next action? - **Reportable:** Will the final answer include enough evidence to decide what to do next? If a goal fails these checks, rewrite it before spawning or starting the work. ## When the Goal Drives a Loop Some goals require repeated attempts. Add loop mechanics only when feedback from one attempt should change the next attempt. For loop-driven goals, specify: - what changes between attempts, - what signal is observed after each attempt, - how the signal is compared to the target or baseline, - what memory or log is updated, - when to continue, branch, stop, or ask, - who reviews the loop when cheating or metric gaming is possible. A loop without a clear goal is drift. A goal without feedback is wishful thinking. Keep the goal primary and the loop accountable to it. ## Reviewer Gates Use an independent reviewer when the goal creates incentives to cheat, hide failures, overfit, or reinterpret success after the fact. Ask the reviewer to check: - whether the metric matches the real objective, - whether the baseline and data were preserved, - whether failures and non-best attempts were reported, - whether constraints were violated, - whether the claimed success follows from the evidence, - what would change their mind. For research and benchmark work, prefer reviewer gates before promotion, not after the result has already been written as a win. ## Handoff Template ```markdown Goal: Why: Ground truth: - Scope: - In: - Out: Priority / tradeoffs: - Constraints: - Anti-goals / invalid shortcuts: - Evidence of success: - Stop / ask if: - Report back with: - ``` Keep the template terse. Delete sections that do not change the agent's behavior.