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datadog-automation

Automate Datadog tasks via Rube MCP (Composio): query metrics, search logs, manage monitors/dashboards, create events and downtimes. Always search tools first for current schemas.

59

Quality

69%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills/skills/datadog-automation/SKILL.md

The canonical home for this skill is datadog-automation in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

68%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is well-structured, actionable, and largely concise, with concrete tool sequences and parameter guidance across all six workflows. Its main weakness is the absence of validation/verification feedback loops around destructive and batch operations.

Suggestions

Add explicit validation checkpoints for destructive and batch operations (e.g., confirm a monitor_id exists via DATADOG_GET_MONITOR before DELETE/UPDATE, verify dashboard_id before DELETE, and confirm connection status is ACTIVE before any state-changing call).

De-duplicate the pitfalls: keep per-workflow pitfalls tightly scoped and consolidate the cross-cutting ones (timestamps, query syntax, rate limits) into a single Known Pitfalls section.

Consider moving the Quick Reference table and verbose parameter details into a reference file to tighten SKILL.md into a leaner overview.

DimensionReasoningScore

Conciseness

The body is largely efficient and assumes Datadog/MCP knowledge, but pitfalls recur across per-workflow sections, Common Patterns, and Known Pitfalls, and the Quick Reference table repeats the tool slugs — minor redundancy that could be trimmed.

4 / 5

Actionability

Concrete tool sequences, parameter lists, and query-syntax examples (e.g., avg:system.cpu.user{host:web01}) give actionable guidance; it falls short of 5 because guidance is tool-slug/parameter based rather than fully copy-paste executable snippets.

4 / 5

Workflow Clarity

Each workflow has a numbered tool sequence, but destructive/batch operations (DELETE dashboard, mute/unmute, downtime creation) lack validation or verify-checkpoint feedback loops, which per the rubric caps workflow_clarity at 3.

3 / 5

Progressive Disclosure

The file is well-organized into clear sections, but at ~240 lines it inlines a Quick Reference table and Common/Known Pitfalls that could live in separate reference files; with no bundle files present the structure is good but not optimally split.

4 / 5

Total

15

/

20

Passed

Description

70%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and distinctive, naming a strong set of Datadog capabilities, but it omits any explicit "when to use" guidance, which limits its completeness. Adding a concrete trigger clause would round it out.

Suggestions

Add an explicit "Use when..." clause naming the situations or user phrases that should trigger this skill (e.g., querying Datadog metrics, searching Datadog logs, managing monitors or dashboards).

Include a few more natural trigger variations such as "alerts", "infrastructure/hosts", or "APM/traces" to broaden keyword coverage.

Replace the generic "manage monitors/dashboards" with two distinct concrete actions (e.g., "create and update monitors" and "edit dashboards") to push specificity toward the top anchor.

DimensionReasoningScore

Specificity

Lists several concrete actions — "query metrics, search logs, manage monitors/dashboards, create events and downtimes" — giving broad coverage; "manage" is slightly generic so it stops short of the comprehensive 5 anchor.

4 / 5

Completeness

It clearly answers "what" but has no "Use when..." clause or explicit trigger guidance for when to invoke it, which per the guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

Natural terms like Datadog, metrics, logs, monitors/dashboards, events, and downtimes are present, but a few common variations a user might say (e.g. "alerts", "infrastructure", "APM/traces") are missing.

4 / 5

Distinctiveness Conflict Risk

Datadog is a clearly distinct niche with specific triggers (Datadog, monitors, metrics, logs) and minimal overlap risk with other skills.

5 / 5

Total

16

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

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