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 datit309/supergraph --skill architecturegit clone --depth 1 https://github.com/datit309/supergraphWrote 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/datit309/supergraph/architecture)<a href="https://agentmods.dev/skills/datit309/supergraph/architecture"><img src="https://agentmods.dev/badge/skills/datit309/supergraph/architecture/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/datit309/supergraph/architecture"><img src="https://agentmods.dev/badge/skills/datit309/supergraph/architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.00721 |
| Opus 5.5 | $0.00021 | $0.00288 |
| Sonnet 5.5 | $0.00010 | $0.00144 |
| Haiku 4.5 | $0.00005 | $0.00072 |
Grade A, and why
architecture 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 1mo 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/supergraph:architecture
Three phases: explore → HTML report → grilling loop.
Announce: "🏛️ /supergraph:architecture — mapping codebase structure..."
Phase 1 — Explore
1a. Read CONTEXT.md:
cat CONTEXT.md 2>/dev/null || echo "No CONTEXT.md"
1b. Codebase Memory overview (optional):
Use CBM_PROJECT with get_architecture aspects overview, layers,
boundaries, clusters, and hotspots. After get_graph_schema, run shared
contract recipes hubs, bridges, cross-boundary, and test-gaps.
If codebase-memory-mcp is unavailable, label graph evidence unavailable, use
Serena/filesystem evidence, and generate Mermaid diagrams from imports.
1c. Serena structure (optional):
mcp__serena__get_symbols_overview()
1d. Read 3-5 hub node files — understand actual structure, naming, patterns.
Phase 2 — Generate HTML Report
Write self-contained docs/supergraph/architecture-review-<YYYY-MM-DD>.html with Tailwind+Mermaid header, architecture graph TD, per-candidate card (Problem/Proposal/Before/After Mermaid/Impact/Trade-offs with badge Strong/Worth exploring/Speculative), plus tables for Test Gaps and Unexpected Coupling.
Open: open docs/supergraph/architecture-review-<date>.html || xdg-open ... || echo "Report saved: ..."
Phase 3 — Grilling Loop
For each Strong candidate, ask one focused question:
"Candidate N proposes [X]. Is this consistent with [constraint from CONTEXT.md / known business rule]?"
Incorporate answers to refine the candidate cards. Mark dismissed candidates as Rejected — [reason].
After grilling, present final prioritized list:
Strong candidates (ready for /supergraph:plan):
1. [Name] — [one-line rationale]
Worth exploring (needs spike first):
2. [Name] — [open question to resolve]
Speculative (park for later):
3. [Name] — [what would need to be true]
Report
✅ /supergraph:architecture complete
- Report: docs/supergraph/architecture-review-<date>.html
- Communities: N | Hub nodes: N | Bridge nodes: N
- Candidates: N Strong, N Worth exploring, N Speculative
- Next: /supergraph:plan (for Strong candidates) or /supergraph:prototype (for uncertain ones)
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.
- 1mo ago First seen · 78 lines · 52 tokens per session scan A 1f42e8606423
architecture is a skill published in the GitHub repository datit309/supergraph (22 stars, last pushed 2d ago), licensed MIT. It adds 52 tokens to every session and 721 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 2026-08-30.
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