perf
Performance optimizer for loops, DB queries, rendering, and batch operations.
Skill by epicsagas
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/epicsagas-perf
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
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Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
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- Install scripts
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/perf
What's inside
- Skill name
perf- Description
- Performance optimizer for loops, DB queries, rendering, and batch operations. Catches N+1 queries, missing indexes, and unnecessary re-renders.
- License
- Apache-2.0
- Instructions
- 68 lines in SKILL.md · view on GitHub
Agent Skills spec issues
- files: referenced but missing: references/performance.md
Files (1)
- SKILL.md
SKILL.md (first lines)
# Perf — Performance Review ## Process 1. Identify performance-sensitive code paths from the diff (loops, queries, rendering, batch ops) 2. Run the Checklist below, marking each item as pass, fail, or N/A with reason 3. For each failure, cite the file and line number with a one-line fix hint 4. Report findings using the Evidence Required section ## When to Trigger - Database queries inside loops - Large data set processing - Rendering or UI update logic - API endpoint that could receive high traffic - File I/O in hot paths ## Checklist ### Database - [ ] No N+1 queries (use JOIN, eager loading, or batch) — N+1 queries cause latency to scale linearly with row count, turning a 1ms query into seconds under load. - [ ] Indexes exist for frequently queried columns — without indexes, the database performs full table scans, collapsing read throughput. - [ ] Pagination for large result sets — fetching all rows at once exhausts memory and increases response time for every consumer. - [ ] Connection pooling configured — opening a new connection per request adds network round-trip overhead and can exhaust database connection limits. ### Memory - [ ] No unbounded arrays or caches growing indefinitely — unbounded growth causes gradual memory exhaustion and eventual OOM kills with no warning. - [ ] Event listeners properly removed / unsubscribed — leaked listeners hold references to their context, preventing garbage collection of entire object graphs. - [ ] Large objects released after use — holding references to large payloads keeps them in the heap, increasing GC pressure and pause times. - [ ] Streams used for large file processing (not loading entire file) — loading a multi-GB file into RAM blocks other allocations and risks crashing the process. ### Computation - [ ] Expensive calculations memoized where appropriate — recomputing pure results on every call wastes CPU cycles that compound in hot paths. - [ ] No redundant re-renders (React: useMemo, useCallback) — unnecessary re-renders cascade through the component tree, causing layout thrash and dropped frames. - [ ] Async operations don't block the main thread — blocking the main thread stalls all concurrent requests, degrading throughput for every user. - [ ] Debounce/throttle on frequent events (scroll, input) — unthrottled handlers fire hundreds of times per second, overwhelming the event loop. ### Network - [ ] Responses compressed (gzip/brotli) — uncompressed JSON/HTML can be 5-10x larger, wasting bandwidth and increasing time-to-first-byte. - [ ] Static assets cached with proper headers — missing cache headers force the browser to re-download unchanged assets on every page load. - [ ] No unnecessary API calls (cache, dedupe) — duplicate requests waste server resources and increase latency for legitimate traffic. See `references/performance.md` for the full checklist. ## Anti-Rationalization | Excuse | Rebuttal | What to do instead | |--------|----------|--------------------| | "Premature optimization is the root of all evil" | Knuth said "about 97% of the time" — the other 3% matters. N+1 queries are never premature. | Check for N+1 queries and missing indexes before merging. | | "It works fine on my machine" | Your machine is not production. Profile under realistic conditions. | Run the perf checklist against realistic data volumes. | | "We can optimize later" | Performance debt is invisible until it's catastrophic. Measure now. | Add a benchmark or load test for the critical path today. | ## Evidence Required Before claiming performance review is complete, show ALL applicable: - [ ] No N+1 queries: show the query plan or eager-loading code - [ ] Pagination present: show limit/offset or cursor implementation - [ ] No unbounded collections: show size caps or streaming for large data - [ ] Async I/O in request paths: show `await` or stream usage (no sync fs/net) **"Looks fine" is not a review. Show the query or the code path.** ## Red Flags - Loading all records when only count is needed - Synchronous file I/O in request handlers - Missing pagination on list endpoints
From epicsagas/epic-harness, Apache-2.0.
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Signals
- Stars
- 19
- Forks
- 5
- Open issues
- 3
- Last commit (this folder)
- Jun 25, 2026 (4 months ago)
- Last release
- Oct 23, 2026 (6 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/epicsagas/epic-harness
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
perf skill. Agent Mart. https://agentmart.dev/skills/epicsagas-perf (last updated 2026-10-24)