Developer ships fix-proposal Skill that makes agents state testable fix expectations
11/06/2026 — 11/06, 14:18·1 sources (all)·1 reports (all)
Key takeaways
- A developer noticed that Claude Code agents often report a bug fix without saying what should measurably improve afterward, so they built a…
- with the skill it marked that line as failed and only explained where the numbers came from.
Background & analysis
On 2026-11-06 at 14:01, the AI section of DEV Community published a post about a new Claude Code skill called fix-proposal, created by developer ESeungJun. The code lives at https://github.com/ESeungJun/fix-proposal .
The author describes doing most of his work with Claude Code, where the agent frequently spots problems on its own and asks whether to fix them. His usual reply is the same question each time: what goes wrong if the issue is left alone, and what gets better if it is fixed? The agent rarely answers either part on its own, so he built a skill to force the point. The fix-proposal skill requires the agent to state an expected result that can be verified by running the code, rather than simply reporting that something looks broken.
To show the difference, the author ran the same scenario against the same model, Haiku, once without the skill and once with it. Without the skill, the agent acknowledged that the request count only dropped from 12 to 2 and then added conditions it had not actually measured. With the skill, the agent marked that line as ❌ and stuck to explaining where the numbers came from. The author notes that he wrote the post with help from Claude and checked every number himself, and the project is published as a public repository so other developers can reuse the approach.
AI-generated from 1 reports · updated 1 hour ago
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Reports on this story headlines open the original
The author, who works mostly in Claude Code, got tired of agents flagging bugs without saying what a fix would improve, so they built a skill called fix-proposal that forces the agent to write a runnable expected outcome. In a side-by-side with the same Haiku model, the agent without the skill admitted the request count only fell from 12 to 2 and then added an unmeasured condition; with the skill it marked that row ❌ and only explained where the number came from.
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