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 agentmods add skills/chemaclass/agnostic-ai/initnpx skills add Chemaclass/agnostic-ai --skill initgit clone --depth 1 https://github.com/Chemaclass/agnostic-aiWrote 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/chemaclass/agnostic-ai/init)<a href="https://agentmods.dev/skills/chemaclass/agnostic-ai/init"><img src="https://agentmods.dev/badge/skills/chemaclass/agnostic-ai/init.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 | $0.00044 | $0.00499 |
| Opus 5 | $0.00022 | $0.00249 |
| Sonnet 5 | $0.00009 | $0.00100 |
| Haiku 4.5 | $0.00004 | $0.00050 |
Grade A, and why
init 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 5d 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.
What it actually says
init
Turns a project with no shared AI config into one where a single set of specs drives every tool.
Steps
-
Confirm the CLI is present:
agnostic-ai --version. If missing, run theinstallskill first. -
Check whether the project already has AI config worth keeping:
CLAUDE.md,AGENTS.md,GEMINI.md,.cursor/rules/,.github/copilot-instructions.md. If any exist, stop and use theimportskill instead; it captures them as specs rather than overwriting them. -
Scaffold:
agnostic-ai init --all # every target, no prompt agnostic-ai init --demo # plus one example spec per kindinitalone opens a target picker when stdin is a TTY. In a non-interactive session, pass--allor pipe a list:echo "claude,codex" | agnostic-ai init. -
Ask which AI tools the team actually uses, then narrow
targets:inagnostic-ai.yamlto those. Fewer targets means fewer generated files to review. -
Write the project's real conventions as rules under
.agnostic-ai/rules/, one file per rule. Keep each rule to a single concern with adescriptionthat says when it applies. -
Run
agnostic-ai sync, then show the user what appeared withgit status --short.
Notes
initwritesgitignore.enabled: trueby default: generated files stay out of git and each contributor runssync. Pass--gitignore=falseto commit them instead, which suits a team where not everyone has the CLI.- One spec per file.
agnostic-ai new rule <name>(alsoagent,skill,hook,mcp) scaffolds the right frontmatter for the kind. - Never hand-edit a generated file such as
CLAUDE.mdor.cursor/rules/*.mdc. The nextsyncoverwrites it. Edit the spec under.agnostic-ai/instead.
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.
- 5d ago First seen · 31 lines · 44 tokens per session scan A 2858a9c15c5a
init is a skill published in the GitHub repository Chemaclass/agnostic-ai (11 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 499 once invoked, about $0.0002 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.
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