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ai coding collaboration
Enforce senior-level engineering guardrails, task-level discipline, and evidence-based verification for AI coding.
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ai coding collaboration
Example session with this skill installed
Use $ai-coding-collaboration to fix a duplicate submission bug in my React + TypeScript application.
Users can click the Save button multiple times, causing duplicate API requests.
Here is the existing code
const [saving, setSaving] = useState(false);
async function handleSave() {
setSaving(true);
await api.updateProfile(form);
setSaving(false);
}
<button onClick={handleSave}>Save</button>
Requirements
- Prevent duplicate API calls, including rapid double-clicks.
- Restore the button state if the request fails.
- Keep the smallest possible code change.
- Do not modify backend APIs, routes, or database schemas.
- Do not install new dependencies.
- Do not refactor unrelated components.
- Classify the task and identify affected areas.
- Provide P0 validation steps and actual verification status.
- Finish with a concise executive summary.
Do not claim the fix is merge-ready unless core validation has been completed.
- Read your context and instructions
- Compiled the ai coding collaboration
AI Coding Guardrails — Bug Fix
1. Task Classification
Level: L1 — Small Feature / Bug Fix
Goal: Prevent duplicate Save requests without introducing unnecessary abstractions or unrelated changes.
Strategy: Minimal Diff + Impact Analysis + P0 Validation.
2. Impact Analysis
| Area | Impact | Action |
|---|---|---|
| Backend API | No | Keep existing endpoint |
| Service logic | No | No changes |
| DTO / Types | No | Preserve existing contracts |
| Database / Config | No | No changes |
| Frontend | Yes | Update the Save handler |
| Routing | No | No changes |
| Tests | Yes | Verify success and failure paths |
Risk: Rapid clicks can reach the handler before React has rendered the disabled state. An immediate synchronous guard is required.
3. Root Cause
The original implementation has two problems:
- There is no synchronous in-flight guard, so multiple Save requests can start before the UI updates.
- If
api.updateProfile()rejects,setSaving(false)is skipped, potentially leaving the button stuck in a loading state.
No backend changes are needed.
4. Minimal Fix
Add one useRef guard and use try/finally to guarantee state restoration.
const [saving, setSaving] = useState(false);
const saveInFlight = useRef(false);
async function handleSave() {
if (saveInFlight.current) return;
saveInFlight.current = true;
setSaving(true);
try {
await api.updateProfile(form);
} finally {
saveInFlight.current = false;
setSaving(false);
}
}
Update the existing button
<button
type="button"
disabled={saving}
onClick={handleSave}
>
{saving ? "Saving..." : "Save"}
</button>
Why this approach?
useRefblocks duplicate requests synchronously.disabledprevents additional user interaction.try/finallyrestores the button after success or failure.- No new packages, APIs, services, or architectural layers.
- Existing error-handling behavior remains outside this scoped fix.
5. Before vs. After
| Behavior | Before | After |
|---|---|---|
| Rapid double-click | Duplicate requests possible | Guard blocks duplicates |
| Request pending | Button remains clickable | Button disabled |
| Request failure | Loading state may remain stuck | State restored |
| New dependencies | None | None |
| Backend changes | None | None |
| Unrelated refactoring | Not required | None |
6. P0 Validation Checklist
| Check | Expected Result | Status |
|---|---|---|
| Single click | One API request | Not executed |
| Rapid double-click | Still one request | Not executed |
| Request succeeds | Button becomes enabled | Not executed |
| Request fails | Button becomes enabled | Not executed |
| Build / TypeScript check | No errors | Not executed |
Verification evidence: The provided snippet supports a static code review and proposed fix. A runnable repository and test environment were not provided, so no tests or builds have been claimed as passed.
P0 status: Pending execution.
7. Executive Summary
【目标】 Prevent duplicate Save submissions.
【改动文件】 Existing React component only; no backend or API changes.
【自测结果】 Static analysis completed. Runtime tests and typecheck remain pending.
【风险】 Existing rejected-request error feedback may need separate review.
【必须验收】 Double-click prevention, successful save, failed save, and TypeScript build.
【结论】 Needs validation before merge.
Value Delivered
One targeted fix. No unnecessary architecture. No fabricated test results.
AI Coding Guardrails keeps AI-generated changes small, traceable, and evidence-based—helping developers review and ship with greater confidence.
Connects securely to your tools. The creator never sees your data.
About this skill
AI coding agents often fail by overengineering simple fixes, introducing scope creep, or claiming success without verification. This skill forces agents to act like senior engineers by applying pragmatic guardrails to every task. It classifies work into four distinct levels to ensure the process matches the complexity, preventing unnecessary ceremony for micro-tasks while enforcing strict planning for architecture changes.
What it does
- Task classification categorizes work from L0 micro-fixes to L3 project-scale architecture to select the right workflow.
- Risk assessment triggers high-level planning and explicit scope confirmation for changes to auth, payments, or database schemas.
- Minimal diff enforcement prevents unrelated refactors, renames, or dependency additions to keep PRs clean and reviewable.
- Tiered validation mandates P0/P1/P2 testing protocols to ensure core flows work before claiming completion.
- Evidence-based reporting requires actual command output or manual paths instead of vague "tests passed" statements.
How it works
- Trigger the skill by asking for a bug fix, feature, or code review.
- The agent performs a pre-implementation check to assess impact across APIs, services, and databases.
- Implementation follows the chosen task level, adhering to strict rules on minimal diffs and pattern reuse.
- The agent runs a self-check and provides a standardized executive summary with evidence of verification.
Frameworks & tools
This skill is framework-agnostic and works with any repository structure. It provides specific support for OpenSpec workflows but can operate independently in any standard development environment.
Why this beats prompting it yourself
Writing prompts to stop an LLM from over-refactoring is notoriously difficult. This skill provides a structured decision matrix and standardized reporting format that forces the agent to prove its work through specific evidence categories.
Use cases
- Preventing a simple CSS fix from turning into a full component refactor.
- Enforcing strict safety checks for tasks involving database migrations or API contract changes.
- Standardizing the way agents report test results and validation evidence.
- Managing handoffs between different agents or between an agent and a human reviewer.
Known limitations
Does not automatically create testing environments or mock data. Requires the underlying agent to have access to terminal commands for P0 validation evidence.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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