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.
git clone --depth 1 https://github.com/HKTITAN/cursor-best-practicesWrote 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/commands/hktitan/cursor-best-practices/analyze-deps)<a href="https://agentmods.dev/commands/hktitan/cursor-best-practices/analyze-deps"><img src="https://agentmods.dev/badge/commands/hktitan/cursor-best-practices/analyze-deps/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/commands/hktitan/cursor-best-practices/analyze-deps"><img src="https://agentmods.dev/badge/commands/hktitan/cursor-best-practices/analyze-deps.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 | $0.00000 | $0.00446 |
| Opus 5.5 | $0.00000 | $0.00178 |
| Sonnet 5.5 | $0.00000 | $0.00089 |
| Haiku 4.5 | $0.00000 | $0.00045 |
Grade A, and why
analyze-deps 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 1mo 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze the project's dependency tree, identify unused or duplicate dependencies, and suggest optimizations.
Steps
-
Analyze dependency tree
- Use appropriate tools (e.g.
npm ls,depcheck,pipdeptree,cargo tree,go mod graph). - Build dependency graph showing all direct and transitive dependencies.
- Use appropriate tools (e.g.
-
Identify unused dependencies
- Check for dependencies listed in package files but not imported/used in code.
- Use tools like
depcheck(npm),pip-autoremove(Python), or manual analysis. - Verify false positives (some deps are used at build time or in configs).
-
Find duplicate dependencies
- Identify multiple versions of the same package.
- Check for conflicting versions that could cause issues.
- Look for packages that provide similar functionality.
-
Check bundle size impact (if applicable)
- For frontend projects, analyze bundle size contribution of each dependency.
- Identify large dependencies that could be replaced or tree-shaken.
- Check for duplicate code across dependencies.
-
Suggest optimizations
- For each issue, provide:
- Dependency name and version(s)
- Issue type (unused, duplicate, large, etc.)
- Impact (bundle size, security, maintenance)
- Suggested action (remove, update, replace, consolidate)
- Prioritize by impact:
- High — Large unused deps, security vulnerabilities, major duplicates
- Medium — Small unused deps, minor duplicates
- Low — Optimization opportunities
- For each issue, provide:
-
Report
- Summary of findings (unused, duplicates, large deps)
- List of suggested removals/updates
- Estimated impact (bundle size reduction, security improvements)
- Warnings about potentially risky removals
Rules
- Apply project rules from
.cursor/rulesorAGENTS.mdwhen relevant. - Be cautious with removals; verify dependencies aren't used indirectly or at build time.
- Consider security implications of outdated dependencies.
- For removals, suggest testing thoroughly after changes.
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.
- 1mo ago First seen · 47 lines · 0 tokens per session scan A 5848f559f808
analyze-deps is a command published in the GitHub repository HKTITAN/cursor-best-practices (7 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 446 tokens. 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
performance-optimization
Orchestrate end-to-end application performance optimization from profiling to monitoring.
refactor-clean
Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.
config-validate
Validate application configuration with schemas, per-environment rules, runtime checks, and secure handling of sensitive values.
smart-debug
AI-assisted smart debugging — parse error messages, stack traces, and failure patterns to identify root causes and produce a fix with automated observability steps.
team-debug
Debug issues using competing hypotheses with parallel investigation by multiple agents.
fix-issue
Fetch a GitHub issue, find the relevant code, implement the fix, verify, and open a PR.