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/youglin-dev/aha-loop/architectnpx skills add YougLin-dev/Aha-Loop --skill architectgit clone --depth 1 https://github.com/YougLin-dev/Aha-LoopWhat 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 | $0.02217 |
| Opus 5 | $0.00018 | $0.01108 |
| Sonnet 5 | $0.00007 | $0.00443 |
| Haiku 4.5 | $0.00004 | $0.00222 |
Grade A, and why
architect scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://crates.io/api/v1/crates/tokio" | jq '.crate.max_stable_version' How it starts
The opening of the file, as written. The whole thing — 402 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architect Skill
Research, evaluate, and decide on technology stack and system architecture based on project requirements.
Workspace Mode Note
When running in workspace mode, all paths are relative to .aha-loop/ directory:
- Vision analysis:
.aha-loop/project.vision-analysis.md - Architecture output:
.aha-loop/project.architecture.md
The orchestrator will provide the actual paths in the prompt context.
The Job
- Read
project.vision-analysis.mdfor requirements - Research candidate technologies for each layer
- Evaluate and compare options
- Make decisions with documented rationale
- Design system architecture
- Output
project.architecture.md
Core Principles
1. Prefer Latest Stable Versions
Always use the latest stable version of libraries unless there's a specific reason not to.
## Version Selection Process
1. Query official source for latest version:
- Rust: crates.io API or `cargo search`
- Node: npmjs.com or `npm view [pkg] version`
- Python: pypi.org or `pip index versions`
2. Check release date and stability:
- Released > 2 weeks ago (not bleeding edge)
- No critical issues in GitHub issues
- Changelog shows no breaking changes from common patterns
3. Document the version and why:
- Record in project.architecture.md
- Add to knowledge/project/decisions.md
2. Consider Long-term Maintenance
- Active community and regular updates
- Good documentation
- Large ecosystem of plugins/extensions
- Backed by reputable organization or strong community
3. Evaluate Total Cost
- Learning curve for the team/AI
- Bundle size / binary size
- Runtime performance
- Development velocity
Research Process
Step 1: Identify Technology Layers
Based on project type, identify which layers need decisions:
| Layer | Examples |
|---|---|
| Language | Rust, TypeScript, Python, Go |
| Frontend Framework | React, Vue, Svelte, SolidJS |
| Backend Framework | Axum, Actix, Express, FastAPI |
| Database | PostgreSQL, SQLite, MongoDB |
| ORM/Query Builder | Diesel, SQLx, Prisma, Drizzle |
| Authentication | JWT, Sessions, OAuth providers |
| Deployment | Docker, Serverless, VPS |
| Testing | Built-in, Jest, Pytest, Vitest |
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 · 402 lines · 37 tokens per session scan A 52736e8a95e9
architect is a skill published in the GitHub repository YougLin-dev/Aha-Loop (181 stars, last pushed 7mo ago), licensed MIT. It adds 37 tokens to every session and 2,217 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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