Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Focuses on routing, CSRF protection, context handling, and template usage within the internal handlers directory.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
ctx.Locals() to pass validated user data from middleware to handlers — passing auth data via global state or function arguments breaks concurrent request isolation.| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Skipping CSRF middleware on "safe" routes | Attackers escalate via chained requests; partial protection = no protection | Apply csrf.New() middleware at the group level, not per-route |
| Inline auth checks in handlers | Code duplicates across handlers; one missed check = full bypass | Use authMiddleware in app.Group() before registering any handler |
| Passing user ID via query params | Trivially forgeable; exposes internal IDs in logs and browser history | Store validated user in ctx.Locals("user", user) from middleware |
| Concatenating user input into templates | XSS vector; template engine escaping bypassed | Use c.Render() with template variables; never fmt.Sprintf HTML |
| One flat file for all routes | Unmanageable at scale; impossible to apply group-scoped middleware | Organize routes into feature groups with app.Group("/feature") |
Before starting:
cat .claude/context/memory/learnings.md
After completing: Record any new patterns or exceptions discovered.
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
中文简介正在准备中。当前显示原文说明。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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