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Same codebase task. One route goes straight to the model. The other goes through a21e first.
Basic prompt
Prompt
Fix the prompt detail page crash from undefined toLocaleString values.
Output
Added a null check around toLocaleString and returned 0 when missing. No additional tests added.
Full prompt + memory + enhancements
Source
A21E internal draft prompt (not live): response-contract-hardening-v2
Full prompt
You are a staff engineer responsible for API response safety. Task: 1. Audit every external JSON boundary for unknown values. 2. Parse payloads into typed runtime guards before render. 3. Replace unsafe formatter calls with safe helpers. 4. Add regression tests for unauthorized and malformed payloads. Constraints: - Preserve existing UX behavior. - Do not use any. - Block regressions in CI.
Memory
+Team standard: strict TypeScript and runtime parsing for external payloads
+Prior bug: prompt page crashed on undefined toLocaleString in estimate fields
+Convention: formatter helpers must accept unknown and return safe defaults
Enhancements
+Injected response-shape safety checklist before generation
+Applied risk scoring to force malformed-payload test coverage
+Added quality-gate verification for formatter safety at UI boundaries
Output
Shipped patch: 1. Added parseCreditEstimate() guard with unknown input narrowing 2. Replaced direct toLocaleString() calls with formatIntegerSafe() 3. Added 401 and malformed-response tests for /api/estimate and prompt detail parsing 4. Added CI guard rule preventing unsafe formatter usage on unvalidated payloads
a21e builds this context from your preferences, decisions, and corrections. It starts working from your first developer session.
What changes on day one
Your AI remembers your stack
Preferences, decisions, and corrections persist. Stop re-explaining your codebase every morning.
Ship code, not prompts
Agents that write PRs, plan multi-step work, and review code — already aware of your conventions.
Tested prompts for real workflows
Production-tested prompts for repos, PRs, and CI/CD. Pin a version. Get consistent results.
How it works
From plain-language input to production-quality output in five steps.
We find the best prompts
Your intent is matched against a curated catalog of production-tested prompts.
Review the match
See which prompts were selected, why they scored well, and what it costs.
Execute with one click
We run the prompts against your chosen LLM. Prompt IP stays on our servers.
The system gets smarter
Your feedback improves routing, scoring, and prompt quality for everyone.
Built for how you already work
Solo developers
Move from idea to output faster
Set your style once. Run in your IDE, CLI, or browser. Your AI remembers tomorrow what you told it today.
Developer teams
Keep quality consistent across people
Share context and standards across contributors. New team members get AI that already knows your repos and PR expectations.
Engineering leads
Govern AI without slowing shipping
Policy controls, audit logging, and memory governance — without slowing down the teams shipping code.
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