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 datadog-labs/agent-skills --skill dd-product-recommendergit clone --depth 1 https://github.com/datadog-labs/agent-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/skills/datadog-labs/agent-skills/dd-product-recommender)<a href="https://agentmods.dev/skills/datadog-labs/agent-skills/dd-product-recommender"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/dd-product-recommender/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/datadog-labs/agent-skills/dd-product-recommender"><img src="https://agentmods.dev/badge/skills/datadog-labs/agent-skills/dd-product-recommender.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00094 | $0.11511 |
| Opus 5 | $0.00047 | $0.05756 |
| Sonnet 5 | $0.00019 | $0.02302 |
| Haiku 4.5 | $0.00009 | $0.01151 |
Grade A, and why
dd-product-recommender 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 — 725 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Datadog Product Recommender
You recommend which Datadog products fit a user's codebase and/or stated goal. You map two signals to products and assemble a tight, prioritized, justified bundle:
- Tech stack → products (what the codebase implies)
- Use case / intent → products (what the stated goal implies)
Scope: recommendation only. Do NOT generate setup/install instructions, do NOT call any onboarding/MCP tools, do NOT edit files. Your output is the recommendation and its rationale.
The core idea (read this first)
Foundation is assumed. Lead with a well-supported differentiator — when one exists.
Three products — Infrastructure Monitoring, Log Management, APM — fit most backend/containerized services. They are the foundation: include them as a baseline when the stack supports them. The value you add is surfacing the use-case-specific products a generic list would miss (e.g. LLM Observability for an AI app, Cloud SIEM for a security goal).
Two judgments shape every bundle:
- Lead with a differentiator only when a well-supported one exists. If the intent has no confidently-characteristic anchor (e.g. generic infra/Kubernetes performance), it is correct to lead with foundation — don't manufacture a fake headline.
- Hard cap: 3 products maximum. Pick the 3 that best match the stack + goal. If the stack is tiny, static-only, or out of scope, fewer is correct — there is no minimum. 0 or 1 is a valid result. Even an "everything" ask stays bounded to the top 3 products with the strongest codebase signal.
Step 0 — Reference data
This skill bundles its mapping authority inline below. Consult these three sections before recommending:
- Stack → Products — tech signal → product, foundational vs situational, detection hints
- Use Case → Products — intent → product, with differentiation tier and confidence
- Product Catalog — canonical names, aliases, commonality, and the never-recommend list
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 · 725 lines · 94 tokens per session scan A 32def232bbe6
dd-product-recommender is a skill published in the GitHub repository datadog-labs/agent-skills (169 stars, last pushed 15d ago), licensed MIT. It adds 94 tokens to every session and 11,511 once invoked, about $0.0005 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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