Performance Regression Triage

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.

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.

1. Extract concrete evidence: endpoint/job, time window, p50/p95/p99, error rate, throughput, CPU/memory, DB metrics, and recent deploys.
2. Separate symptoms from hypotheses.
3. Check likely causes: N+1 or missing indexes, payload/serialization cost, cache miss/stampede, dependency latency, queue backlog/concurrency, lock contention/transaction scope.
4. Recommend one measurement before one fix; avoid speculative rewrites.
5. 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

text

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/<slug>.md:

markdown

---
type: API Convention
title: <one line>
description: <one line — what it is and that it was learned during this task>
resource: ./path/to/file-or-evidence
tags: [..]
timestamp: <ISO 8601>
---

# Summary
<the reusable rule, in one or two lines>

# Applies to
<where it holds>

# Evidence
<the files or observations it came from>

# Use in future agent work
<what a future agent should do with it>

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.

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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.