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 techygarg/lattice --skill architecture-refinergit clone --depth 1 https://github.com/techygarg/latticeWrote 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/techygarg/lattice/architecture-refiner)<a href="https://agentmods.dev/skills/techygarg/lattice/architecture-refiner"><img src="https://agentmods.dev/badge/skills/techygarg/lattice/architecture-refiner.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 24 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00114 | $0.03376 |
| Opus 5 | $0.00057 | $0.01688 |
| Sonnet 5 | $0.00023 | $0.00675 |
| Haiku 4.5 | $0.00011 | $0.00338 |
Grade A, and why
architecture-refiner 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 8d 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Refiner
Step 0: Style Selection
Before anything else, ask the user which architecture style their team uses:
"What architecture style does your team use?
- Clean Architecture (default) — layers (Domain, Application, Interface, Infrastructure), dependency inversion, command/query separation
- Hexagonal / Ports & Adapters — core domain surrounded by ports, adapters on the outside
- Modular Monolith — vertical slices, each module owns its own layers
- Custom / Define from scratch — you describe the layers and rules"
Branching:
- Option 1 → proceed to the clean architecture flow below (existing interview). Template:
./assets/template-clean-arch.md. Output:.lattice/standards/architecture.md. Config key:paths.architecture. Noarchitecture_modekey needed (defaults toclean). - Options 2–4 → proceed to the generic architecture flow. Template:
./assets/template-generic.md. Output:.lattice/standards/architecture.md. Config key:paths.architecture. Additionally, setarchitecture_mode: customin.lattice/config.yaml.
The rest of this document describes the clean architecture flow (Option 1). For the generic flow (Options 2–4), read ./assets/template-generic.md and follow its <!-- INTERVIEW GUIDANCE: --> comments. The facilitation approach, conversation style, output assembly, and document quality checks below apply to both flows — substitute the appropriate template, output path, and config key.
What This Produces
For clean architecture (Option 1):
- Output:
.lattice/standards/architecture.md(or custom path from.lattice/config.yaml→paths.architecture) - Two modes:
- Overlay (
mode: overlay): A slim document containing only sections that differ from the defaults. The architecture atom reads its embedded clean-architecture defaults first, then applies this document's sections on top. This is the expected common case. - Override (
mode: override): A comprehensive standalone document that fully replaces the atom's embedded defaults. For teams that want to define clean architecture from scratch.
- Overlay (
- Default mode: Overlay -- produces only what the user wants to change
- Config key:
paths.architecturein.lattice/config.yaml - Template: Read
./assets/template-clean-arch.mdfor the full document structure, default content, and interview guidance comments
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 272 lines · 114 tokens per session scan A e9b78fa0bb2c
architecture-refiner is a skill published in the GitHub repository techygarg/lattice (185 stars, last pushed yesterday), licensed MIT. It adds 114 tokens to every session and 3,376 once invoked, about $0.0006 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.
Other skills, from other repositories
name-your-business
Generate, refine, compare, and when needed validate distinctive names for startups, AI products, developer tools, protocols, open-source projects, apps, product families, local businesses, services, companies, nonprofits, and other organizations. Use when asked to name or rename a business, brand, product, venture…
top-one-percent
Teach any topic deeply from first principles and build evidence-based paths toward exceptional capability. Use when a user asks to understand, explain, learn, or deep-dive into a topic; asks why or how something works, why it matters, how alternatives compare, or what different perspectives reveal; requests current…
paper-opportunity-radar
Run a cumulative daily or retrospective sweep of research papers on a chosen topic, audit their claims, methods, integrity signals, and independent support, then identify overlooked but feasible project or business opportunities in a detailed source-grounded report. Use when asked to monitor papers every day, mine…
repo-system-map
Analyze a software repository at the latest remote main commit and turn its implemented architecture into a citation-backed interactive isometric system map with a legend, selectable infrastructure buildings, dependency and payload flows, and plain-language learner explanations. For eligible public GitHub…
build-scenario-tests
Inspect an unfamiliar repository, turn a focused Markdown behavior scenario into a deterministic test in the repository's native test stack, run it, and preserve traceability between intent and code. Use when asked to add scenario tests, compile acceptance criteria or Given/When/Then Markdown into executable tests…
create-marketing-kit
Create truthful, human-centered marketing campaigns for an app or product, including positioning, channel copy, original artwork, editable layouts, README banners, and selective website integration. Use when asked to make launch materials, promotional artwork, social assets, campaign kits, ads, or marketing content…