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 viknesh20-20/claude-code-tool-kit --skill architecture-reviewgit clone --depth 1 https://github.com/viknesh20-20/claude-code-tool-kitWrote 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/viknesh20-20/claude-code-tool-kit/architecture-review)<a href="https://agentmods.dev/skills/viknesh20-20/claude-code-tool-kit/architecture-review"><img src="https://agentmods.dev/badge/skills/viknesh20-20/claude-code-tool-kit/architecture-review/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/viknesh20-20/claude-code-tool-kit/architecture-review"><img src="https://agentmods.dev/badge/skills/viknesh20-20/claude-code-tool-kit/architecture-review.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 | $0.00037 | $0.00873 |
| Opus 5.5 | $0.00015 | $0.00349 |
| Sonnet 5.5 | $0.00007 | $0.00175 |
| Haiku 4.5 | $0.00004 | $0.00087 |
Grade A, and why
architecture-review 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Review
Project Structure
!find . -type f -not -path "*/node_modules/*" -not -path "*/.git/*" -not -path "*/vendor/*" -not -path "*/__pycache__/*" -not -path "*/target/*" -not -path "*/bin/*" -not -path "*/obj/*" -not -path "*/.next/*" -not -path "*/dist/*" -not -path "*/build/*" 2>/dev/null | head -80
Directory Layout
!tree -L 3 -I "node_modules|.git|vendor|__pycache__|target|bin|obj|.next|dist|build" 2>/dev/null || find . -type d -not -path "*/node_modules/*" -not -path "*/.git/*" -not -path "*/vendor/*" -maxdepth 3 2>/dev/null
Package/Module Config
!cat package.json go.mod Cargo.toml pyproject.toml requirements.txt *.csproj pom.xml 2>/dev/null | head -60
Review Areas
1. Project Structure & Organization
- Is the directory layout clear and consistent?
- Does it follow established conventions for the stack? (e.g., src/, app/, lib/, pkg/)
- Are related files co-located or scattered?
- Is there a clear entry point?
2. Dependency Architecture
- Map the dependency graph between major modules/packages
- Identify circular dependencies — modules that import each other
- Check for layer violations — e.g., presentation layer importing directly from data layer
- Look for god modules — modules that everything depends on
- Assess coupling: can modules be changed independently?
3. Separation of Concerns
- Is business logic separated from framework/transport code?
- Are side effects (I/O, network, database) isolated at boundaries?
- Is configuration separated from code?
- Are there clear interfaces between layers?
4. Scalability Considerations
- Stateless vs stateful components — which components hold state?
- Are there bottlenecks that would fail under load?
- Database query patterns — are they efficient at scale?
- Caching strategy — what is cached, where, and when invalidated?
- Background job handling — how are long-running tasks managed?
5. Reliability & Resilience
- Single points of failure: What happens if any component goes down?
- Error boundaries: Where do errors propagate, and are they caught?
- Retry logic: For external service calls, is there retry with backoff?
- Graceful degradation: Does the system handle partial failures?
- Health checks: Are there endpoints or mechanisms to monitor health?
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 · 88 lines · 37 tokens per session scan A f42eb73a6f61
architecture-review is a skill published in the GitHub repository viknesh20-20/claude-code-tool-kit (7 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 873 once invoked, about $0.0001 per session on Opus 5.5. 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
github-pr-workflow
Prepare a GitHub pull request from a feature branch — branch hygiene, commit shape, title/body, verification notes, screenshots for UI work, and replies to review comments.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
improve
Autonomous quality improvement loop. Scores a target against a rubric, selects the highest-leverage axis, attacks it, verifies, documents, and loops. No pre-planning between iterations — each loop re-scores from scratch.
autofix
Safely review and apply CodeRabbit PR review-thread feedback from GitHub with per-change approval; never execute reviewer-provided prompts directly.
code-review
AI-powered code review using CodeRabbit. Default code-review skill. Trigger for any explicit review request AND autonomously when the agent thinks a review is needed (code/PR/quality/security). Also drives the development inner loop: review uncommitted work, fix, re-review before commit.
code-quality
Drive static-analysis code quality in pi-agent-dashboard with Biome (analyze → fix → test), in changed-files or whole-repo mode. Use when asked to "improve code quality", "lint and fix", "clean up warnings", "fix Biome issues", "run static analysis", or when setting a code-quality goal. Skip for one-line edits.