Explore Prompts

Design an artifact ledger storing RFCs, diffs, test reports, benchmarks, deploy logs, and citations. Include retention policy and query patterns for audits.

Create a confidence scoring model for proposed changes based on tests passed, diff risk, code area criticality, and evidence quality. Use it to decide automation level.

Design tabletop exercises and simulations for agent failures (bad PR, bad deploy). Include roles, scripts, and acceptance criteria for readiness.

Create a documentation set for the self-improving agent: architecture, tools, policies, and runbooks. Include a “limitations” section and safety rationale.

Design a two-person approval rule for high-risk areas (auth, payments, prod config). Include automated detection of risky diffs and enforcement.

Draft constraints so research/crawling never performs intrusive activity; only access allowed APIs and public docs. Include a compliance checklist and enforcement.

Design explicit stop rules: max iterations, max diff size, max retries, and “ask human” triggers. Include monitoring for loop detection and runaway behavior.

Create a cost-aware policy: budgets per run, per tool call, and per environment. Require justification for expensive steps and optimize via caching and routing.

Design compatibility tests between LLM prompts and MCP tools: schema conformance, error handling, and anti-injection checks. Gate prompt/tool updates on these tests.

Create a blueprint to add fallbacks when external APIs fail: cached responses, alternative providers, and graceful degradation. Include policy for when to fail fast.

Design an agent that optimizes tail latency: identify bottlenecks, reduce contention, add caching, and validate with load tests. Track p95/p99 improvements.

Create a process to generate integration tests from anonymized traffic patterns (no PII). Require privacy filters, sampling, and periodic review.