Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/deepfusionlabs/deep-initnpx agentmods add commands/deepfusionlabs/deep-init/checkWrote 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/commands/deepfusionlabs/deep-init/check)<a href="https://agentmods.dev/commands/deepfusionlabs/deep-init/check"><img src="https://agentmods.dev/badge/commands/deepfusionlabs/deep-init/check/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/commands/deepfusionlabs/deep-init/check"><img src="https://agentmods.dev/badge/commands/deepfusionlabs/deep-init/check.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 | $0.00036 | $0.00363 |
| Opus 5.5 | $0.00014 | $0.00145 |
| Sonnet 5.5 | $0.00007 | $0.00073 |
| Haiku 4.5 | $0.00004 | $0.00036 |
Grade A, and why
check 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 1mo 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
Run the deep-init skill's staleness check — no LLM, no network, 0 tokens (any model call here is a bug). This is the unified front door over the two free checks (the --status and --lint flags stay available for hooks / CI):
- Status (deterministic keystone — always runs first). Execute the status keystone and report its output verbatim:
python .ai/deepinit_status.py # if setup-hooks has installed it into this repo
# else, from the plugin: python skills/deep-init/assets/deepinit_status.py
It compares .ai/docs/current/.file_hashes.json against the working tree (modified / removed via the Step-0 symmetric set-diff / pending). Exit 0 = fresh, 1 = stale.
- Lint (citation + coverage audit — when generated docs exist AND
--statuswas NOT passed). Followreferences/update.md's--lintflow: per-component fresh/stale/critical staleness, broken-reference (deadfile:line) detection, ID consistency, and coverage — all by hash/citation comparison, still 0 tokens. Non-zero exit on a critical/dead citation (the CI gate).
If it reports STALE, offer to run /deep-init:refresh. Report the result; do not modify anything. See references/triggers.md and references/update.md.
$ARGUMENTS
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.
- 1mo ago First seen · 22 lines · 36 tokens per session scan A b2d5c6bd3ee4
check is a command published in the GitHub repository deepfusionlabs/deep-init (6 stars, last pushed 23d ago), licensed MIT. It adds 36 tokens to every session and 363 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
context-guard
Install, uninstall, or check status of Context Guard hooks for AI context file freshness: $ARGUMENTS.
context-verify
Validate AI context file quality — signal-to-noise ratio, line budgets, stale paths, AGENTS-to-bridge consistency, and context health scoring: $ARGUMENTS.
silk
A command for managing Claude Code project memory, including CLAUDE.md and related memory files.
recall-save
Generate / overwrite .recall/context.md with Recall's local offline summarizer.
verify
An independent recheck of a security assessment report that derives conclusions from its own evidence instead of accepting the original findings.
ai-act-ask
Answer an EU AI Act question grounded in the bundled knowledge base — verbatim statute text, obligation paraphrases, and the compound-risk taxonomy. Offline and deterministic by default; cites the articles it relies on.