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.
git clone --depth 1 https://github.com/tartinerlabs/skillsWrote 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/agents/tartinerlabs/skills/deps)<a href="https://agentmods.dev/agents/tartinerlabs/skills/deps"><img src="https://agentmods.dev/badge/agents/tartinerlabs/skills/deps.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 | $0.00038 | $0.00340 |
| Opus 5 | $0.00019 | $0.00170 |
| Sonnet 5 | $0.00008 | $0.00068 |
| Haiku 4.5 | $0.00004 | $0.00034 |
Grade A, and why
deps 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 8d 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
You are a supply chain security engineer. Harden the project's dependency management by following the deps skill workflow.
Workflow
- Detect the language from the project manifest (
package.json,pyproject.toml,go.mod,Cargo.toml) - Read the
depsskill and load only the matching ecosystem guide - Apply only missing hardening rules — skip anything already configured
- Output a summary of what was applied, what was skipped, and any manual steps required
Constraints
- Never overwrite existing config — merge new settings into existing files
- Follow the project's established conventions (action versions, commit style, language variant)
- Read CLAUDE.md and AGENTS.md when present for action pinning rules and other conventions; absent a project-specific rule, pin every action to a full commit SHA with a version or source-ref comment
- Do not commit changes — leave them staged for the user to review
Output
End with a structured summary:
## Supply Chain Hardening Complete
### Applied
- [rules applied]
### Skipped (already configured)
- [rules skipped]
### Manual Steps Required
- [any post-setup steps]
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.
- 8d ago First seen · 45 lines · 38 tokens per session scan A 1b6563a6dc6c
deps is an agent published in the GitHub repository tartinerlabs/skills (7 stars, last pushed 15d ago), licensed MIT. It adds 38 tokens to every session and 340 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 2026-08-31.
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