goal-driven-execution
Skill216 starsChanged 6 months agoMITView on GitHub
Transforms imperative instructions into declarative goals with verifiable success criteria. Enables autonomous looping until verified completion.
We split this skill into the rules it gives an agent
10 rules. About 0 are common, 10 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈10
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 17
Before starting any multi-step task
- Distinctive: line 18
When a task has been described imperatively ("do X, then Y, then Z")
- Distinctive: line 19
When you're unsure how you'll know when you're "done"
- Distinctive: line 26
Read the full request.
- Distinctive: line 27
Ask: *What is the user trying to achieve, not just what they asked for?*
- Distinctive: line 28
Write the goal as: "The task is complete when [observable, verifiable outcome]."
- Distinctive: line 48
Verify: Every criterion can be checked without the original author.
- Distinctive: line 64
Follow the plan step-by-step.
- Distinctive: line 66
If a check fails: diagnose, fix, re-verify.
- Distinctive: line 67
When all checks pass: report completion with evidence.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Overview
Andrej Karpathy's key insight: "LLMs are exceptionally good at looping until they meet specific goals. Don't tell it what to do — give it success criteria and watch it go."
This skill converts vague imperative instructions ("make the login work") into declarative goals with concrete, testable success criteria. Agents with clear goals self-correct autonomously. Agents with vague goals produce vague results and require constant intervention.
When to Use
- Before starting any multi-step task
- When a task has been described imperatively ("do X, then Y, then Z")
- When you're unsure how you'll know when you're "done"
- For long-running or complex implementations
Process
Step 1: Extract the Underlying Goal
- Read the full request.
- Ask: What is the user trying to achieve, not just what they asked for?
- Write the goal as: "The task is complete when [observable, verifiable outcome]."
Example transformation:
- ❌ Imperative: "Add error handling to the API."
- ✅ Goal: "The task is complete when: all API endpoints return structured error responses for 4xx/5xx cases, error responses include a
code,message, andrequestId, and the existing tests pass."
Verify: The goal statement is observable and testable by a third party.
Step 2: Define Success Criteria
- List 3–7 specific, binary success criteria:
Success when: - [ ] All existing tests pass - [ ] New behavior X is demonstrated by test Y - [ ] No regressions in file Z - [ ] Manual check: [describe what to look for] - Each criterion must be falsifiable — you can clearly state when it passes or fails.
Verify: Every criterion can be checked without the original author.
Step 3: Define the Execution Plan
- Break the goal into ordered steps, each with its own verify check:
1. [Step] → verify: [command or check] 2. [Step] → verify: [command or check] 3. [Step] → verify: [command or check] - Identify the first failure mode — what's most likely to go wrong? Plan for it.
Verify: The plan is readable and each step is independently verifiable.
Step 4: Execute and Loop
- Follow the plan step-by-step.
- At each verify checkpoint — actually run the check. Do not skip.
- If a check fails: diagnose, fix, re-verify. Do not proceed past a failing check.
- When all checks pass: report completion with evidence.
Common Rationalizations (and Rebuttals)
| Excuse | Rebuttal |
| "The goal is obvious" | Obvious goals still need explicit success criteria. What's obvious to you is ambiguous to an agent. |
| "I'll know when it's done" | That's not a verifiable criterion. Write it down. |
| "The tests will tell me" | Which tests? What do they cover? What don't they cover? |
| "It's too simple for a plan" | Simple tasks rarely fail. Complex tasks without a plan always do. |
Red Flags
- The task is described as a to-do list, not a goal
- You don't know how you'll verify completion
- You're 80% through and realize the original framing was wrong
- "It seems to work" is your verification strategy
Verification
- Goal is stated as an observable, testable outcome
- Success criteria are listed and binary (pass/fail)
- Execution plan has verify steps for each phase
- All verify checks have been run (not just assumed passing)
- Evidence of completion is documented
References
- think-before-coding skill
- task-decomposition skill
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- liushikuan63/Qoder-Optimization★ 2same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository34 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- agent-qa-run-reviewSkill
- ai-output-validationSkill
- api-designSkill
- ci-cd-pipelinesSkill
- citruscoastaiSkill
- code-explanationSkill
- code-reviewSkill
- context-loadingSkill
- debugging-methodologySkill
- documentationSkill
- error-handlingSkill
- frontend-engineeringSkill
- git-workflowSkill
- hallucination-preventionSkill
- idea-to-specSkill
- incremental-codingSkill
- integration-testingSkill
- meeting-notes-to-tasksSkill
- multi-agent-orchestrationSkill
- observabilitySkill
- performance-optimizationSkill
- production-deploymentSkill
- prompt-injection-defenseSkill
- rag-and-memorySkill
- refactoringSkill
- research-and-summarizeSkill
- security-hardeningSkill
- simplicity-firstSkill
- surgical-changesSkill
- task-decompositionSkill
- test-driven-developmentSkill
- think-before-codingSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
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