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 recall-responsegit 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/recall-response)<a href="https://agentmods.dev/skills/cleo-labs-ia/skills_library/recall-response"><img src="https://agentmods.dev/badge/skills/cleo-labs-ia/skills_library/recall-response/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/recall-response"><img src="https://agentmods.dev/badge/skills/cleo-labs-ia/skills_library/recall-response.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 | $0.00049 | $0.02719 |
| Opus 5 | $0.00024 | $0.01359 |
| Sonnet 5 | $0.00010 | $0.00544 |
| Haiku 4.5 | $0.00005 | $0.00272 |
Grade A, and why
recall-response 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 — 240 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Recall Response
Handle product recall or safety alert from detection through close-out. Timelines are legal deadlines, not suggestions.
MCP Tools
# Monitor for recall signals affecting your product category
mcp__claude_ai_Cleo_Insight__search_signals(q="recall", risk_level="critical", limit=25)
mcp__claude_ai_Cleo_Insight__search_signals(q="safety alert", risk_level="high", limit=25)
mcp__claude_ai_Cleo_Insight__search_signals(q="RAPEX", limit=25)
# Get details on a specific recall signal
mcp__claude_ai_Cleo_Insight__get_signal(id="<signal-id>")
# Check if your product's substances are affected by new bans
mcp__claude_ai_CLEO_LEGAL_API__compliance/check
ingredients: ["<your-ingredients>"]
target_markets: ["EU", "US", "UK"]
# Get company profile for affected products list
mcp__claude_ai_Cleo_Insight__get_company_profile
mcp__claude_ai_Cleo_Insight__list_products
# Upload recall documentation as compliance evidence
mcp__bastion__upload-compliance-document(name="recall-report-2026.pdf", document="data:application/pdf;base64,...")
mcp__bastion__add-compliance-test-evidence(testId="<test-id>", name="Recall close-out report", description="Product recall corrective action documentation", evidenceDocumentId="<doc-id>")
Recall Decision Tree
digraph {
rankdir=TB; node [shape=box style=rounded fontsize=10];
trigger [label="Safety issue detected" shape=hexagon];
assess [label="Assess severity\n+ scope" shape=diamond];
serious [label="Serious risk?\nDeath, injury, chemical\nexposure above limit" shape=diamond];
notify_auth [label="NOTIFY AUTHORITIES\nwithin 10 business days (EU)\nwithin 24h (US CPSC)\nwithin 3 days (UK OPSS)"];
voluntary [label="Voluntary recall\nor corrective action"];
scope [label="Define scope:\nbatch numbers, markets,\nquantity sold, distribution"];
dist [label="Notify distributors\n+ retailers\nSTOP SALE immediately"];
consumer [label="Consumer notification\n+ return/refund process"];
correct [label="Corrective action:\nreformulate, repair,\ndestroy, rework"];
closeout [label="Close-out report\nto authorities"];
trigger -> assess -> serious;
serious -> notify_auth [label="YES"];
serious -> voluntary [label="NO -- minor issue"];
notify_auth -> scope -> dist -> consumer -> correct -> closeout;
voluntary -> scope;
}
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 · 240 lines · 49 tokens per session scan A 894c9cffbc93
recall-response is a skill published in the GitHub repository Cleo-Labs-IA/skills_library (1 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 2,719 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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