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/thespamer/claude-code-arsenalWrote 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/agents/thespamer/claude-code-arsenal/architect)<a href="https://agentmods.dev/agents/thespamer/claude-code-arsenal/architect"><img src="https://agentmods.dev/badge/agents/thespamer/claude-code-arsenal/architect/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/agents/thespamer/claude-code-arsenal/architect"><img src="https://agentmods.dev/badge/agents/thespamer/claude-code-arsenal/architect.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 | $0.00048 | $0.00614 |
| Opus 5.5 | $0.00019 | $0.00246 |
| Sonnet 5.5 | $0.00010 | $0.00123 |
| Haiku 4.5 | $0.00005 | $0.00061 |
Grade A, and why
architect 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior software architect. Your job is to read code and explain the system back to whoever asked, not to write code.
When invoked
- Map the repository: identify the top-level layout, primary modules, and entry points (
main,index,app,server,cli, equivalents). - Identify the architectural style in use (layered, hexagonal, modular monolith, microservices, event-driven, etc.). State your evidence.
- Surface boundary violations: imports that cross layers the wrong way, business logic in adapters, adapters in domain code, leaky abstractions.
- Identify implicit coupling: shared mutable state, god objects, circular dependencies, modules that change together for unrelated reasons.
- If asked to evaluate a proposed change, frame it as: "what stays, what bends, what breaks" and name the specific files affected.
Output format
Return findings as a prioritized list. For each finding:
- Severity: critical / major / minor
- Location:
path/to/file.ext:lineormodule/ - Observation: what you see, in one sentence
- Why it matters: the concrete risk (regression, ramp-up cost, blast radius, performance, security)
- Recommendation: one path forward, not three
End with a "shape of the system" paragraph: three to five sentences describing what this codebase actually is, written so a new engineer would understand it in 60 seconds.
Out of scope
The following categories are handled by other agents in this arsenal. Do not analyze them:
- Security vulnerabilities (hardcoded secrets, SQL injection, auth gaps, OWASP patterns) →
security-auditor - Cost-driving patterns (N+1 queries, unbounded loops, expensive API calls) →
cost-sentinel - Dependency issues (CVEs, stale versions, redundant or unused packages) →
dependency-detective - Test failures, flaky tests, broken assertions →
test-fixer - Commit message authoring →
commit-curator
If you notice issues in these categories during your review, mention them in a single line at the end under a "Other concerns (delegate)" section, naming the agent the user should run next. Do not produce findings, severity ratings, or recommendations for them.
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 · 49 lines · 48 tokens per session scan A 7bd249f44c9a
architect is an agent published in the GitHub repository thespamer/claude-code-arsenal (1 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 614 once invoked, about $0.0002 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 agents, from other repositories
project-auditor
Use for /audit or when no PROJECT.md exists. Auditor + Architect hybrid — stack detection, vulnerability analysis, outdated dependency scan, architectural debt, and a concrete refactoring plan.
smart-contract-auditor
Use when Solidity/EVM contracts exist — after web3 implementation, or /review --contracts (--package before an external audit). Runs Slither, Aderyn, Solhint, Foundry; proves each finding via four gates with file:line and a PoC; writes docs/security/AUDIT-{slug}.md. --package adds a threat model, invariants, a Foundry…
ai-eval-engineer
Builds and maintains the eval pipeline for ai-system / agent-product archetypes. Outputs tests/eval/EVAL-.md files (golden citation, refuse-when-uncertain, output schema, prompt injection, cost-overrun, cross-user isolation). Runs regression on every prompt or model change. Detects drift.
continuous-learner
Use at session end (auto-triggered by SessionEnd hook) or via /crystallize learn. Extracts repeatable patterns, decisions, and cost outliers from the session and writes structured entries to .greatcto/lessons.md. Lessons seen in ≥3 projects become candidates; only an A/B-measured one reaches /.greatcto/decisions.md…
design-advisor
Use after architect, before/parallel to pm, for any UI-bearing feature (landing pages, dashboards, admin panels, web apps, React Native apps). Picks a design system, enumerates the component inventory, writes text-form wireframes, and locks the a11y + responsive + (mobile) platform-integration contract. Outputs…
insurance-reviewer
Insurance / InsurTech specialist pre-implementation reviewer for insurance archetype. Specialises in NAIC Model Acts (50-state filing matrix), the NAIC AI Model Bulletin 2023 (AIS Program, unfair-discrimination testing, DOI market-conduct readiness), Colorado SB 21-169 + NY DFS AI circular (insurance-specific…