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 Cleo-Labs-IA/skills_library --skill substance-screeninggit clone --depth 1 https://github.com/Cleo-Labs-IA/skills_libraryWrote 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/cleo-labs-ia/skills_library/substance-screening)<a href="https://agentmods.dev/skills/cleo-labs-ia/skills_library/substance-screening"><img src="https://agentmods.dev/badge/skills/cleo-labs-ia/skills_library/substance-screening/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/cleo-labs-ia/skills_library/substance-screening"><img src="https://agentmods.dev/badge/skills/cleo-labs-ia/skills_library/substance-screening.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 | $0.00057 | $0.02706 |
| Opus 5 | $0.00028 | $0.01353 |
| Sonnet 5 | $0.00011 | $0.00541 |
| Haiku 4.5 | $0.00006 | $0.00271 |
Grade A, and why
substance-screening 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 11d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Substance Screening
Deep ingredient/material screening against 13 regulatory databases. Input: ingredient list. Output: per-substance, per-jurisdiction verdict with concentration limits and margin calculations.
MCP Tools
# Primary: batch substance check across markets
mcp__claude_ai_CLEO_LEGAL_API__compliance/check
product_description: "anti-aging face serum"
ingredients: ["retinol", "niacinamide", "salicylic acid", "titanium dioxide"]
target_markets: ["EU", "US", "UK", "CA", "JP", "KR"]
# Cross-reference recent substance ban signals
mcp__claude_ai_Cleo_Insight__search_signals(q="substance ban", risk_level="critical", limit=25)
mcp__claude_ai_Cleo_Insight__search_signals(q="SVHC candidate list", limit=25)
# Get regulation details for any flagged substance
mcp__claude_ai_Cleo_Insight__get_regulation(id="<regulation-id>")
# List all tracked regulations to find substance-specific ones
mcp__claude_ai_Cleo_Insight__list_regulations(limit=100)
Screening Workflow
digraph {
rankdir=TB; node [shape=box style=rounded fontsize=10];
input [label="Raw ingredient list\n(INCI, trade names, or CAS)"];
resolve [label="Step 1: Resolve\neach entry to CAS number"];
decompose [label="Step 2: Decompose\nmixtures to individual substances"];
batch [label="Step 3: Batch check\nagainst 13 databases"];
conc [label="Step 4: Concentration\ncomparison (actual vs limit)"];
verdict [label="Step 5: Verdict matrix\nCOMPLIANT / FLAG / FAIL"];
input -> resolve -> decompose -> batch -> conc -> verdict;
}
Step 1: INCI Name to CAS Number Resolution
| Input Type | Resolution Method | Example |
|---|---|---|
| INCI name | CosIng database lookup | RETINOL -> CAS 68-26-8 |
| Trade name | Supplier SDS -> active substance -> CAS | Parsol MCX -> Ethylhexyl Methoxycinnamate -> CAS 5466-77-3 |
| Botanical extract | Map to marker compounds + CAS | CAMELLIA SINENSIS LEAF EXTRACT -> EGCG (CAS 989-51-5) |
| Fragrance blend | IFRA certificate -> individual allergens + CAS | PARFUM -> LIMONENE (CAS 5989-27-5), LINALOOL (CAS 78-70-6), etc. |
| CI number | Colorant index to CAS | CI 77891 -> Titanium Dioxide -> CAS 13463-67-7 |
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.
- 11d ago First seen · 176 lines · 57 tokens per session scan A 8487ad7f8a72
substance-screening is a skill published in the GitHub repository Cleo-Labs-IA/skills_library (1 stars, last pushed 3mo ago), licensed MIT. It adds 57 tokens to every session and 2,706 once invoked, about $0.0003 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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