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 lonely-lockley/archinsight --skill codexgit clone --depth 1 https://github.com/lonely-lockley/archinsightWrote 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/lonely-lockley/archinsight/codex)<a href="https://agentmods.dev/skills/lonely-lockley/archinsight/codex"><img src="https://agentmods.dev/badge/skills/lonely-lockley/archinsight/codex.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.01510 |
| Opus 5 | $0.00039 | $0.00755 |
| Sonnet 5 | $0.00015 | $0.00302 |
| Haiku 4.5 | $0.00008 | $0.00151 |
Grade A, and why
archinsight 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 today.
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.
This is a copy
89% identical to archinsight — 44 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Archinsight
Use this skill when creating, analyzing, or repairing Insight .ai models, or
when importing an existing architecture description into Insight.
Insight is its own typed architecture-as-code language. Do not infer its syntax from YAML, Mermaid, PlantUML, Structurizr, or C4 DSL.
Codex Usage Notes
Treat this SKILL.md as the entrypoint and load routed reference files only
when the task needs them. Use shell access to run validation. Do not silently
install global npm packages or change machine configuration.
This package was generated by Archinsight CLI {{CLI_VERSION}}. Run
archinsight --version before editing. If the installed version differs,
regenerate the skill so its references, core sources, examples, and built-in
queries match the runtime.
Required Tool
Use the Archinsight CLI as the validation source of truth:
archinsight --help
archinsight link . --format text
If archinsight is not available, read references/cli.md. Do not install
or update packages without the user's authorization.
Task modes
Choose one mode before acting:
- Analyze: stay read-only and follow
references/analysis.md. Inspect sources, runstructure,link, and the relevant query, then separate authored facts, derived relationships, deployment projections, and rendered presentation in the findings. - Repair: reproduce the defect first. For a visual defect, inspect
archinsight query ... --format jsonbefore treating the image as evidence that the model is wrong. Request the current image or rendered output when it is not available, make the smallest model or query change, and validate the same semantic path again before rendering. - Build or rebuild: perform discovery before editing, then model from the outside inward and validate after each architectural layer.
- Extend an existing model: inventory existing ids, imports, edges, and view scope; preserve stable identities and compare linked/query results before and after each focused change.
- Import an existing model: follow
references/importing-models.md. Establish which source artifacts are authoritative, translate facts rather than drawing layout, record uncertain mappings, and validate one architectural layer at a time.
What ships with it
1 file 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.
- today First seen · 136 lines · 77 tokens per session scan A 3fac0468f0cb
archinsight is a skill published in the GitHub repository lonely-lockley/archinsight (87 stars, last pushed yesterday), licensed Apache-2.0. It adds 77 tokens to every session and 1,510 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to archinsight, differing in 44 lines, and is treated as a copy.
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