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 agentmods add commands/versoxbt/claude-initial-setup/optimizegit clone --depth 1 https://github.com/VersoXBT/claude-initial-setupWhat 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 | $0.00005 | $0.00447 |
| Opus 5 | $0.00003 | $0.00224 |
| Sonnet 5 | $0.00001 | $0.00089 |
| Haiku 4.5 | $0.00001 | $0.00045 |
Grade A, and why
optimize 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 2d 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization
Profile the application to identify bottlenecks, then apply targeted optimizations with before/after benchmarks.
Steps
-
Establish a baseline:
- Identify the slow operation or endpoint.
- Measure current performance (response time, execution time, memory usage).
- Record the baseline numbers for comparison.
-
Profile the application:
- For frontend: Check bundle size, render performance, and network requests.
- For backend: Profile API response times, database query times, and memory allocation.
- For general code: Use profiling tools to identify hot paths and bottlenecks.
-
Identify the top bottlenecks:
- Rank issues by impact (largest time or resource savings first).
- Focus on the top 3 bottlenecks — do not optimize everything at once.
- Verify each bottleneck is real, not a profiling artifact.
-
Apply optimizations one at a time:
- Database: Add missing indexes, fix N+1 queries, optimize slow queries, add pagination.
- Computation: Memoize expensive calculations, use caching (in-memory or Redis), debounce/throttle frequent operations.
- Rendering: Lazy-load components and routes, virtualize long lists, reduce unnecessary re-renders.
- Network: Compress responses, batch API calls, implement proper caching headers.
- Bundle: Code-split large bundles, tree-shake unused imports, lazy-load heavy dependencies.
-
Benchmark after each optimization:
- Re-run the same measurement from step 1.
- Compare against the baseline.
- If the optimization shows no measurable improvement, revert it.
- If the optimization introduces regressions, revert and try a different approach.
-
Run the full test suite:
- Verify no functionality was broken by optimizations.
- Performance improvements must not come at the cost of correctness.
If Optimization Introduces Bugs
- Revert the optimization immediately.
- Analyze why it broke — incorrect assumptions about data flow or state.
- Find an alternative approach that preserves correctness.
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.
- 2d ago First seen · 52 lines · 5 tokens per session scan A bd81ab3bcfc2
optimize is a command published in the GitHub repository VersoXBT/claude-initial-setup (4 stars, last pushed 3mo ago), licensed MIT. It adds 5 tokens to every session and 447 once invoked, about $0.0000 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.