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 DanWahlin/ai-agent-board --skill architectural-proposalsgit clone --depth 1 https://github.com/DanWahlin/ai-agent-boardWrote 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/danwahlin/ai-agent-board/architectural-proposals)<a href="https://agentmods.dev/skills/danwahlin/ai-agent-board/architectural-proposals"><img src="https://agentmods.dev/badge/skills/danwahlin/ai-agent-board/architectural-proposals/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/danwahlin/ai-agent-board/architectural-proposals"><img src="https://agentmods.dev/badge/skills/danwahlin/ai-agent-board/architectural-proposals.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 | $0.00018 | $0.01429 |
| Opus 5 | $0.00009 | $0.00714 |
| Sonnet 5 | $0.00004 | $0.00286 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade A, and why
architectural-proposals 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 11d 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.
This is a copy
100% identical to architectural-proposals — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context
Proposals create alignment before code is written. Cheaper to change a doc than refactor code. Use this pattern when:
- Architecture shifts invalidate existing assumptions
- Product direction changes require new foundation
- Multiple waves/milestones will be affected by a decision
- External dependencies (Copilot CLI, SDK APIs) change
Patterns
Proposal Structure (docs/proposals/)
Required sections:
- Problem Statement — Why current state is broken (specific, measurable evidence)
- Proposed Architecture — Solution with technical specifics (not hand-waving)
- What Changes — Impact on existing work (waves, milestones, modules)
- What Stays the Same — Preserve existing functionality (no regression)
- Key Decisions Needed — Explicit choices with recommendations
- Risks and Mitigations — Likelihood + impact + mitigation strategy
- Scope — What's in v1, what's deferred (timeline clarity)
Optional sections:
- Implementation Plan (high-level milestones)
- Success Criteria (measurable outcomes)
- Open Questions (unresolved items)
- Appendix (prior art, alternatives considered)
Tone Ceiling Enforcement
Always:
- Cite specific evidence (user reports, performance data, failure modes)
- Justify recommendations with technical rationale
- Acknowledge trade-offs (no perfect solutions)
- Be specific about APIs, libraries, file paths
Never:
- Hype ("revolutionary", "game-changing")
- Hand-waving ("we'll figure it out later")
- Unsubstantiated claims ("users will love this")
- Vague timelines ("soon", "eventually")
Wave Restructuring Pattern
When a proposal invalidates existing wave structure:
- Acknowledge the shift: "This becomes Wave 0 (Foundation)"
- Cascade impacts: Adjust downstream waves (Wave 1, Wave 2, Wave 3)
- Preserve non-blocking work: Identify what can proceed in parallel
- Update dependencies: Document new blocking relationships
Example (Interactive Shell):
- Wave 0 (NEW): Interactive Shell — blocks all other waves
- Wave 1 (ADJUSTED): npm Distribution — shell bundled in cli.js
- Wave 2 (DEFERRED): SquadUI — waits for shell foundation
- Wave 3 (ADJUSTED): Public Docs — now documents shell as primary interface
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.
- 11d ago First seen · 152 lines · 18 tokens per session scan A 45719db4cb9b
architectural-proposals is a skill published in the GitHub repository DanWahlin/ai-agent-board (58 stars, last pushed 16d ago), licensed MIT. It adds 18 tokens to every session and 1,429 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to architectural-proposals, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
deploy-docker-compose
Run the Omnigent server as a Docker compose stack (server + Postgres) on any Docker host — your laptop, a VPS, EC2 by hand, or as the base layer of any container-platform deploy. Invoke when the user wants to build the image, bring up the compose stack, debug the stack on a host they already have, or extend the stack…
haiku
When writing a haiku for this bot, follow these conventions.
fastapi-router-py
Create FastAPI routers with CRUD operations, authentication dependencies, and proper response models. Use when building REST API endpoints, creating new routes, implementing CRUD operations, or adding authenticated endpoints in FastAPI applications.
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.