Archify is a system that turns codebases or system descriptions into interactive architecture, workflow, sequence, data-flow, and lifecycle diagrams. Developers use it to inspect, review, compare, and share system designs, while its catalogue skill provides the agent workflow for creating and validating those diagrams.
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 tt-a1i/archify --skill archify-tuninggit clone --depth 1 https://github.com/tt-a1i/archifyWrote 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/tt-a1i/archify/archify-tuning)<a href="https://agentmods.dev/skills/tt-a1i/archify/archify-tuning"><img src="https://agentmods.dev/badge/skills/tt-a1i/archify/archify-tuning/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/tt-a1i/archify/archify-tuning"><img src="https://agentmods.dev/badge/skills/tt-a1i/archify/archify-tuning.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 | $0.00049 | $0.01766 |
| Opus 5.5 | $0.00020 | $0.00706 |
| Sonnet 5.5 | $0.00010 | $0.00353 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
archify-tuning 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 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.
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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Archify Tuning
Users describe a system and an agent authors the diagram. Optimize that complete journey, from understanding the source to delivering a usable artifact. Keep this priority:
- Accurate: facts, relationships, directions, conditions, ownership and sources match the system.
- Readable and attractive: a clear reading path, distinguishable connections, legible text and balanced composition.
- Efficient: short complete delivery time, few failed repairs and little repeated work.
Tests and validators serve these outcomes and are themselves candidates for improvement. A passing check does not establish a good diagram; a failing check can expose an unsuitable rule. Judge the artifact and authoring experience before choosing what to change.
Product requirements and adjustable rules
- Protect source facts and supported user behavior. First drafts can be wrong: retain originals and record source-backed corrections or additions. Counts of deleted elements flag a semantic review; they do not decide whether an edit is correct.
- Treat spacing, preferred direction, bend counts and single-screen fit as design strategies to assess in context. An extra bend or readable scrolling can improve the whole diagram. Preserve explicit geometry and supported contracts unless an agreed change covers them.
- Before repairing a failed check, identify the user harm it is meant to prevent and whether that harm is present. For an unsuitable rule, revise its scope, threshold or severity; update or retire obsolete tests with the rationale and actual before/after evidence. Keep the original failure visible. Changing an expected value alone proves no benefit.
- Early diagnostics and browser checks should agree where they measure the same requirement and state their different scopes elsewhere. Neither is an unquestionable oracle. Taste that cannot be judged reliably can remain advice or visual review instead of a hard failure.
Measures
| Role | Measure | Evidence |
|---|---|---|
| Outcome | Accurate, visually acceptable final delivery | Source audit and actual screenshots; separate machine pass from visual acceptance |
| Outcome | Complete elapsed time, failed repairs and repeated work | Timed trace from task start through reading, first draft, repairs, viewing and delivery |
| Process | First-draft quality and time to first usable result | Preserve the first attempt, every failure and the last verified usable artifact |
| Regression | Fixed-input pass/fail and changed output | replay.mjs, affected tests and visual comparisons; render pass excludes finalize/browser acceptance |
| Diagnostic | Bends, crossings, route length, text size and CLI time | Explain an observed benefit or tradeoff; none is a substitute for whole-diagram quality |
What ships with it
8 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.
- 2d ago First seen · 72 lines · 49 tokens per session scan A a9dd29b0c317
archify-tuning is a skill published in the GitHub repository tt-a1i/archify (84,044 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 1,766 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 2026-10-14.
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