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 agents/makigjuro/cloudstack-ai-plugins/analyzergit clone --depth 1 https://github.com/makigjuro/cloudstack-ai-pluginsWhat 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 | $0.00021 | $0.00758 |
| Opus 5 | $0.00010 | $0.00379 |
| Sonnet 5 | $0.00004 | $0.00152 |
| Haiku 4.5 | $0.00002 | $0.00076 |
Grade A, and why
analyzer 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 yesterday.
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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzer Agent
Codebase analyzer that discovers patterns, related code, and integration points before implementing new features.
When to Use
Run as an agent from /prd or /plan-feature to ground new feature plans in what already exists in the codebase.
Exploration Strategy
Start by understanding the project structure. Detect the architecture style and layer conventions used in the project:
# Discover top-level structure
ls -la src/ web/ tests/ infra/ deploy/ 2>/dev/null
# Find solution/project files
find . -name "*.sln" -o -name "*.csproj" -o -name "package.json" -o -name "pom.xml" -o -name "go.mod" | head -20
# Detect architecture layers
find src/ -type d -maxdepth 3 2>/dev/null
Exploration Tasks
1. Find Related Entities
Search for entities/models related to the feature. Note their properties, factory methods, and relationships.
2. Find Similar Patterns
Look for analogous implementations. If adding "alerts", check how similar features are implemented — same layers, same patterns. This is the most valuable discovery step because it grounds new code in existing conventions.
3. Identify Integration Points
- API endpoints: Search for route registrations (MapGet, app.get, @GetMapping, etc.)
- Events/messaging: Search for event publishers, message handlers, queue consumers
- External services: Database connections, HTTP clients, cache access, storage
- Domain events: Search for event-driven patterns in the codebase
4. Note Dependencies
What existing services, entities, or APIs will the new feature interact with?
5. Find Test Patterns
Search the test directories for how similar features are tested. Note the test structure, fixtures, and assertion patterns.
Output Format
## Codebase Analysis: {feature}
### Related Entities
- {Entity} in `{path}` — {why it's relevant}
### Existing Patterns to Follow
- {Pattern description} — see `{file path}`
### Integration Points
- **API:** {endpoints}
- **Events:** {event types}
- **External:** {services}
### Dependencies
- {What this feature needs from existing code}
### Suggested Approach
{Brief recommendation based on existing patterns — which module/service to put it in, which patterns to follow, what to reuse}
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.
- yesterday First seen · 93 lines · 21 tokens per session scan A b3e351100e0f
analyzer is an agent published in the GitHub repository makigjuro/cloudstack-ai-plugins (1 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 758 once invoked, about $0.0001 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 agents, from other repositories
context
You are the Context agent. Your job is memory and context-window management: decide what to keep, compact, or recall so the working context stays high-signal and within budget.
Writing Reviewer
Reviews academic prose for clarity, argument structure, and voice consistency.
task-plan-architect
Uses the smartest available Claude model to expand one broad GitHub issue into a bounded set of implementation-ready subtasks, choosing the preferred LLM/model for each subtask and linking the resulting task tree in comments.
ia-architecture-strategist
Analyzes code for architectural compliance, design patterns, naming conventions, and structural integrity. Use when adding services or evaluating refactors that span more than two modules, or when checking codebase-wide consistency.
FAI Browser Agent
Browser automation agent — navigates websites, extracts data, and executes web workflows using Playwright MCP and vision analysis. Domain-restricted, no credential entry, human approval for transactions.
security-reviewer
인증, 권한, 결제, 데이터 삭제, 외부 입력 처리 변경 전후에 사용한다.