对系统进行性能评估(响应时间、并发能力、资源消耗)和安全评估(认证授权、数据保护、OWASP Top 10),输出性能指标达标情况和安全风险清单。
Coding
Performance Regression Triage
Try itTriage a performance regression from concrete evidence — latency percentiles, error rate, resource and DB metrics, recent deploys — separate symptoms from hy...
What it does
Triage a performance regression from concrete evidence — latency percentiles, error rate, resource and DB metrics, recent deploys — separate symptoms from hypotheses, and recommend one measurement before one fix.
The skill document
Performance regression triage
A standalone development skill. Triage a performance regression from concrete evidence — latency percentiles, error rate, resource and DB metrics, recent deploys — separate symptoms from hypotheses, and recommend one measurement before one fix. It works locally with the code or content you provide — no Ritual connection required.
Run it (local, no setup)
Work the steps below; you need nothing beyond the task in front of you.
- Extract concrete evidence: endpoint/job, time window, p50/p95/p99, error rate, throughput, CPU/memory, DB metrics, and recent deploys.
- Separate symptoms from hypotheses.
- Check likely causes: N+1 or missing indexes, payload/serialization cost, cache miss/stampede, dependency latency, queue backlog/concurrency, lock contention/transaction scope.
- Recommend one measurement before one fix; avoid speculative rewrites.
- Define verification: before/after metrics, load test, explain plan, trace comparison, or canary.
Done when: Known evidence, top hypotheses ranked, the first measurement to run, the smallest likely fix, and the verification plan.
Example prompt
Use performance-regression-triage on this regression: from the latency percentiles, error rate, and recent deploys, rank the likely causes, name the first measurement to run, and the smallest likely fix.
Working principles
Apply these throughout:
- Think before you edit — restate the task and the success criteria, and name any load-bearing assumption rather than silently guessing it.
- Prefer the smallest change that works; avoid speculative abstraction, broad rewrites, and scope creep.
- Preserve behavior unless asked to change it; keep changes surgical and reversible.
- Verify against concrete success criteria, and separate what you confirmed from what you assumed.
- Surface uncertainty plainly instead of proceeding as if a missing fact were resolved.
Optional knowledge capture
After the task, check whether the work revealed reusable knowledge — something a future agent would otherwise rediscover. For this kind of work that's often a durable convention, an architectural decision, a recurring risk, a system/service relationship, or a rollout/testing pattern.
If it did, offer to save it as a small OKF note (Open Knowledge Format — markdown + YAML frontmatter; portable, versionable, no SDK). Never write a file without the user's approval. Keep it small and cite the file(s) or evidence.
When approved, write knowledge/engineering/.md:
---
type: API Convention
title:
description:
resource: ./path/to/file-or-evidence
tags: [..]
timestamp:
---
# Summary
# Applies to
# Evidence
# Use in future agent work
These notes make the repo itself smarter over time, and a tool like Ritual can later reason over them as a structured knowledge layer.
Optional Ritual Cloud upgrade
This skill works locally with the context you provide — that's standalone mode. Upgrade with Ritual Cloud when the task needs deeper workspace context, structured exploration, recommendations, or team alignment:
- More context (discovery) — when the answer depends on things outside the files in front of you:
- triage needs recent deploy/change correlation you can't see
- incident history, runbooks, dashboards, or traces apply
- ownership and service-dependency maps are needed
- A structured decision (exploration) — when the work has become a decision to get right:
- the fix-vs-mitigate path is a decision with tradeoffs
- you need a recommendation owners align on before a risky change
Ritual turns the task into an exploration — clarify the problem, identify the key questions, gather evidence, compare options, and produce a recommendation or decision-ready artifact.
For this task: a regression-triage recommendation with ranked hypotheses, the correlated deploy, the chosen remediation, and a decision-ready summary.
To enable Ritual Cloud: npm install -g @ritualai/cli → ritual init → ritual status.
This skill is local-first and self-contained. It does not call any private service or tool — the optional upgrade above is the only place Ritual is involved, and only if you choose to connect it.
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