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 skills/cloudai-x/opencode-workflow/analyzing-projectsnpx skills add CloudAI-X/opencode-workflow --skill analyzing-projectsgit clone --depth 1 https://github.com/CloudAI-X/opencode-workflowWhat 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 | $0.00037 | $0.01712 |
| Opus 5 | $0.00018 | $0.00856 |
| Sonnet 5 | $0.00007 | $0.00342 |
| Haiku 4.5 | $0.00004 | $0.00171 |
Grade A, and why
analyzing-projects 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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Projects
Systematic approaches to understanding codebases, identifying patterns, and mapping system architecture.
When to Use This Skill
- Onboarding to a new codebase
- Understanding unfamiliar code before making changes
- Investigating how features are implemented
- Mapping dependencies between modules
- Identifying architectural patterns in use
Core Analysis Framework
The 5-Layer Discovery Process
Layer 1: Surface Scan
└─ Entry points, config files, directory structure
Layer 2: Dependency Mapping
└─ Package managers, imports, module relationships
Layer 3: Architecture Recognition
└─ Patterns (MVC, hexagonal, microservices)
Layer 4: Flow Tracing
└─ Request paths, data flow, state management
Layer 5: Quality Assessment
└─ Test coverage, code health, technical debt
Phase 1: Surface Scan
Entry Point Discovery
Start by identifying how the application launches:
-
Look for standard entry files:
main.*,index.*,app.*,server.*cmd/directory (Go)src/main/(Java)bin/scripts
-
Check configuration files:
package.json(scripts.start, main)Makefile,Taskfile- Docker/Compose files
- CI/CD configs (
.github/workflows/)
-
Map directory structure:
Quick heuristics: ├── src/ → Source code ├── lib/ → Internal libraries ├── pkg/ → Public packages (Go) ├── internal/ → Private packages (Go) ├── tests/ → Test files ├── docs/ → Documentation ├── scripts/ → Build/deploy scripts └── config/ → Configuration
Initial Questions to Answer
- What language(s) and framework(s)?
- What's the build system?
- How is the app deployed?
- Where are the main entry points?
Phase 2: Dependency Mapping
Package Manager Analysis
| File | Ecosystem | Key Sections |
|---|---|---|
package.json |
Node.js | dependencies, devDependencies |
requirements.txt / pyproject.toml |
Python | direct dependencies |
go.mod |
Go | require blocks |
Cargo.toml |
Rust | dependencies |
pom.xml / build.gradle |
Java | dependencies |
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 · 300 lines · 37 tokens per session scan A 2a150b2475ae
analyzing-projects is a skill published in the GitHub repository CloudAI-X/opencode-workflow (274 stars, last pushed 7mo ago), licensed MIT. It adds 37 tokens to every session and 1,712 once invoked, about $0.0002 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 2026-08-30.
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