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 me-cedric/agent-compass --skill architecture-advisorgit clone --depth 1 https://github.com/me-cedric/agent-compassWrote 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/me-cedric/agent-compass/architecture-advisor)<a href="https://agentmods.dev/skills/me-cedric/agent-compass/architecture-advisor"><img src="https://agentmods.dev/badge/skills/me-cedric/agent-compass/architecture-advisor/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/me-cedric/agent-compass/architecture-advisor"><img src="https://agentmods.dev/badge/skills/me-cedric/agent-compass/architecture-advisor.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 | $0.00044 | $0.00924 |
| Opus 5.5 | $0.00018 | $0.00370 |
| Sonnet 5.5 | $0.00009 | $0.00185 |
| Haiku 4.5 | $0.00004 | $0.00092 |
Grade A, and why
architecture-advisor 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 10d 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
architecture-advisor
Requirements drive the technology. The technology never drives the requirements. Do not default to the stacks that compass has rules for. If Flutter, Go, serverless, a modular monolith or an event-driven design fits better, propose it and say why.
Principles
- Label each material statement:
- Known: the user stated it, or a cited source shows it.
- Assumed: an educated guess. Give its basis. Confirm it if it changes the decision.
- Unknown: an open question. Never present an Assumed or Unknown statement as Known.
- Ask before you guess about anything that changes the decision. Ask one question at a time. Do not ask what the material or a sensible default answers.
- Research current sources before you state a version, a limit, a price or a best practice. Cite the source and its date.
- Prefer a choice that is easy to reverse. Flag each choice that is hard to reverse: the data model, the core language, the primary datastore, a vendor lock-in.
Pipeline
Loop back when new facts arrive.
-
Intake. Write down these items:
- The functional scope.
- The quality needs: scale, latency, availability, security, compliance, data residency.
- The constraints: budget, team skills, timeline.
- The current systems of the client and the data shape.
Read each image or diagram that the user gives. Mark each entry Known, Assumed or Unknown.
-
Clarify. Ask only about blocking unknowns. For the others, record a labeled guess in the assumptions register.
-
Research. Use web search for current facts. Use the context7 MCP for library docs. Use
gh search reposandgh search codefor reference code. Use the MCP servers and docs of the project. Say which source you could not reach. -
Architecturally significant requirements. Pick the quality attributes that shape the design. Give each a weight.
-
Options. Write 2 or 3 different options, each with a concrete stack. At least one option must be outside the stacks that compass has rules for, unless the requirements rule it out. Say why.
-
Score. Rate each option against the weighted requirements in a matrix. Show the matrix.
-
Recommend. Give the rationale, traced to requirements. Give the main trade-offs, the runner-up and when it would win, and the risks.
-
Diagrams. Write Mermaid diagrams in
{{DIAGRAMS_DIR}}: C4 context and container, one key sequence, the deployment view, and an ERD when data matters. Add an as-is diagram when the client has a current system. -
Deliver. Write the decision as an ADR in
{{ADR_DIR}}. Use the skilladr. Put in it the options, the matrix, the risks, the assumptions register and the open questions. Give the open questions togrill. -
On request, write a technical backlog. Use epics, stories and time-boxed spikes, with the riskiest unknowns first. Also list the technical meetings to request. For each meeting give the goal, the attendees, the inputs, the questions and the expected outputs. Write both in
{{PLANNING_DIR}}. -
Validate. Each recommendation traces to a requirement. Each assumption has a way to confirm it. Each current-tech claim has a citation. At least one option from outside the compass stacks got a real review. The open questions are listed.
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.
- 10d ago Changed · -64 lines · -69 tokens per session 640af37915bd
- 1mo ago First seen · 124 lines · 113 tokens per session scan A 554d3a26f2e0
architecture-advisor is a skill published in the GitHub repository me-cedric/agent-compass (3 stars, last pushed 4d ago), licensed MIT. It adds 44 tokens to every session and 924 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.
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