Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add 05-deepak-patidar/claude-skills --skill performance-improvementgit clone --depth 1 https://github.com/05-deepak-patidar/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/05-deepak-patidar/claude-skills/performance-improvement)<a href="https://agentmods.dev/skills/05-deepak-patidar/claude-skills/performance-improvement"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/performance-improvement/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/05-deepak-patidar/claude-skills/performance-improvement"><img src="https://agentmods.dev/badge/skills/05-deepak-patidar/claude-skills/performance-improvement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.01023 |
| Opus 5 | $0.00036 | $0.00511 |
| Sonnet 5 | $0.00014 | $0.00205 |
| Haiku 4.5 | $0.00007 | $0.00102 |
Grade A, and why
performance-improvement scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Improvement
The iron law: no optimization without a measurement, and no "it's faster now" without a second measurement. Guessed bottlenecks are wrong more than half the time; optimizing a guess makes code worse and no faster.
The loop (never skip a step)
- Define the target. "Fast" is not a spec. Get a number: "list endpoint p95 < 300ms", "page interactive < 2s on mid-range mobile", "report generates in < 30s". Without a target you cannot stop, and unstoppable optimization destroys codebases.
- Measure where the time actually goes. Profile or trace the real path with realistic data volume — 10 rows in dev hides everything. One request timed end-to-end, broken into segments: network / app CPU / DB / external calls / rendering.
- Fix the biggest segment only. One change at a time.
- Re-measure the same way. Keep the change only if the number moved meaningfully. Record before/after in the commit/PR.
- Repeat until the target is met, then stop — the target is the permission to stop.
Where the time usually is (check in this order)
Database — the culprit ~70% of the time in CRUD apps
- N+1 queries: the single most common backend perf bug. Detect by logging/counting queries per request (an ORM loop over rows, each lazy-loading a relation). Fix with joins/eager loading/batched IN queries. Any list endpoint doing >5 queries deserves suspicion.
- Missing indexes:
EXPLAIN ANALYZEthe slow query. Seq scan on a big table in a hot path → index the filter/sort columns (composite, leading with equality columns; tenant id first in multi-tenant). - Fetching too much: SELECT only needed columns for lists; paginate everything (keyset for deep pages); never load all rows to count them (
COUNT(*)) or to filter in app code what SQL can filter. - Chatty transactions: many round trips inside one request → batch into fewer statements; move multi-row inserts to bulk operations.
External calls
- Sequential awaits that could be parallel; missing timeouts (a "slow" system is often one hung dependency); calls in loops that need batching; synchronous calls that belong in a background job (see system-design).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 53 lines · 72 tokens per session scan A b57ec133026b
performance-improvement is a skill published in the GitHub repository 05-deepak-patidar/claude-skills (4 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 1,023 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
eval-hooks
Audit Claude Code hooks defined in settings.json files for validity, performance safety, and correctness. Resolves each command against the filesystem, checks exit-code strategy for blocking hooks, flags missing timeouts, and reviews interactive vs async patterns. Use when setting up hooks for the first time…
check-cache-bugs
Audit Claude Code setup for cache bugs (CC#40524): sentinel, --resume/--continue, attribution header + ArkNill B3/B4/B5.
investigate
Systematic root-cause debugging: find the cause before writing any fix.
sandbox-unblock
Diagnostic protocol to run before reporting a sandbox blocker or asking for a configuration change. Eight checks that eliminate false positives, then a report template the person holding the settings can act on. On one measured day, six of eight reported blockers turned out to be false, all from the same handful of…
qa
Systematic QA testing of a web application: diff-aware, tiered, with fix-and-verify loop.
optimize
Analyze and suggest performance improvements for code, queries, or systems.