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 test-valuegit 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/test-value)<a href="https://agentmods.dev/skills/tt-a1i/archify/test-value"><img src="https://agentmods.dev/badge/skills/tt-a1i/archify/test-value/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/test-value"><img src="https://agentmods.dev/badge/skills/tt-a1i/archify/test-value.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 | $0.00055 | $0.01682 |
| Opus 5.5 | $0.00022 | $0.00673 |
| Sonnet 5.5 | $0.00011 | $0.00336 |
| Haiku 4.5 | $0.00006 | $0.00168 |
Grade A, and why
test-value 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Value
Protect outcomes worth preserving at a cost proportionate to their risk. Test counts, coverage percentages and green runs are evidence, not substitutes for product judgment. When an AI writes both implementation and tests, check whether they share the same mistaken assumption.
Choose the scope
Preserve the user's existing authorization and operational boundaries.
- Audit existing tests: investigate cost, false alarms and protection within the requested scope. An audit request is read-only; an optimization request permits scoped changes. Read audit.md.
- Implementation or pre-commit review: examine the behavior being changed and its affected tests. Decide whether to add, adapt, reuse or remove tests. Reviewing before a commit does not itself authorize committing or pushing.
- Diagnose a failure: identify the outcome the failing check protects, then distinguish a product regression, obsolete assertion, environment failure or unstable test. Keep a narrowly requested fix narrow.
Read decision-examples.md when dealing with automatic-layout snapshots, shared matrices, source assertions, asynchronous waits, test doubles, benchmarks or coverage gaps.
Establish an independent basis for correctness
Explain what must remain true using user requirements, public contracts, compatibility commitments or a real failure. The current implementation and its own generated output cannot, by themselves, establish the correct expectation.
For tests being added or changed, answer three questions in the reasoning or a short change explanation:
- Which concrete mistake would fail this test, and what loss would it cause a user or maintainer?
- Would an equivalent refactor or a better valid result also fail? Which restrictions are actually contractual?
- Which existing test, type check or other check already catches this mistake? Does the additional protection justify its execution and maintenance cost?
What ships with it
4 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 · 93 lines · 55 tokens per session scan A a47f7d9c739c
test-value is a skill published in the GitHub repository tt-a1i/archify (84,044 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 1,682 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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