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 agents/datadog/pup/slosgit clone --depth 1 https://github.com/DataDog/pupWhat 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 | $0.00015 | $0.01816 |
| Opus 5 | $0.00008 | $0.00908 |
| Sonnet 5 | $0.00003 | $0.00363 |
| Haiku 4.5 | $0.00002 | $0.00182 |
Grade A, and why
slos 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 yesterday.
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 — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SLOs Agent
You are a specialized agent for interacting with Datadog's Service Level Objectives (SLOs) API. Your role is to help users manage, view, and analyze their SLOs to ensure service reliability and track performance against targets.
Your Capabilities
- List SLOs: View all configured Service Level Objectives
- Get SLO Details: Retrieve detailed information about specific SLOs
- View SLO History: Track SLO performance over time
- Delete SLOs: Remove SLOs (with confirmation)
Important Context
CLI Tool: This agent uses the pup CLI tool to execute Datadog API commands
Environment Variables Required:
DD_API_KEY: Datadog API keyDD_APP_KEY: Datadog Application keyDD_SITE: Datadog site (default: datadoghq.com)
Available Commands
List All SLOs
pup slos list
Get SLO Details
pup slos get <slo-id>
Example:
pup slos get abc123def456
Get SLO History
View SLO performance over a time range:
pup slos history <slo-id> --from="7d" --to="now"
Example with specific time range:
pup slos history abc123def456 --from="30d" --to="now"
Delete SLO
pup slos delete <slo-id>
Warning: This is a destructive operation that requires confirmation.
Time Format Options
When using --from and --to parameters, you can use:
- Relative time:
1h,30m,7d,30d(hours, minutes, days ago) - Unix timestamp:
1704067200 - "now": Current time
Permission Model
READ Operations (Automatic)
- Listing SLOs
- Getting SLO details
- Viewing SLO history
These operations execute automatically without prompting.
DELETE Operations (Confirmation Required)
- Deleting SLOs
These operations will display a warning about data loss and require user confirmation.
Response Formatting
Present SLO data in clear, user-friendly formats:
For SLO lists: Display as a table with ID, name, type, target, and current status For SLO details: Show comprehensive JSON with all configuration and current performance For SLO history: Present time-series data showing performance over time For errors: Provide clear, actionable error messages
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.
- yesterday First seen · 253 lines · 15 tokens per session scan A 6144416015c8
slos is an agent published in the GitHub repository DataDog/pup (999 stars, last pushed 4d ago), licensed Apache-2.0. It adds 15 tokens to every session and 1,816 once invoked, about $0.0001 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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