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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.

58

Quality

68%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/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

61%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 body is well-structured and actionable, with concrete tool sequences and real query examples. Its weaknesses are redundant content across sections, missing validation checkpoints for destructive operations, and no use of separate reference files despite a sizable inline reference.

Suggestions

Add explicit validation/confirmation steps before destructive operations (e.g., 'Before DATADOG_DELETE_DASHBOARD, confirm the dashboard_id with the user and verify via DATADOG_GET_DASHBOARD').

De-duplicate the Known Pitfalls and Quick Reference sections against the per-workflow Pitfalls blocks to tighten token usage.

Move the detailed parameter reference and Quick Reference table into a separate references file linked from the body to improve progressive disclosure.

DimensionReasoningScore

Conciseness

Mostly efficient domain-specific guidance, but the Quick Reference table and Known Pitfalls section repeat content already covered in the per-workflow Pitfalls blocks, adding tokens that could be trimmed.

3 / 5

Actionability

Provides concrete tool sequences, named parameters, and real query-syntax examples (e.g., 'avg:system.cpu.user{host:web01}'), giving mostly executable guidance with only minor gaps.

4 / 5

Workflow Clarity

Numbered tool sequences and an ACTIVE-status setup checkpoint are present, but destructive/batch operations (DELETE dashboard, mute, downtime) lack explicit validate-before-confirm checkpoints, capping workflow clarity at 3 per the destructive-ops rule.

3 / 5

Progressive Disclosure

The single file is well-organized into clear sections with no nested references, but ~230 lines of inline reference material (detailed params and the full Quick Reference table) could be offloaded to a reference file, leaving minor organization gaps.

4 / 5

Total

14

/

20

Passed

Description

75%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 clear Datadog niche with multiple concrete actions. Its main weakness is the absence of an explicit 'when to use' trigger clause, which caps completeness.

Suggestions

Add an explicit 'Use when...' clause naming natural trigger phrases (e.g., 'Use when the user wants to query Datadog metrics, search logs, or manage monitors/dashboards').

Include common synonyms like 'alerts' and 'observability' to broaden trigger-term coverage.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'query metrics, search logs, manage monitors/dashboards, create events and downtimes' — giving comprehensive coverage of the Datadog domain, matching the score-5 anchor.

5 / 5

Completeness

Provides a clear 'what' (the enumerated Datadog actions) but lacks any 'Use when...' trigger clause; the closing sentence is an instruction rather than a when-condition, capping completeness at 3 per the guidelines.

3 / 5

Trigger Term Quality

Includes natural terms a user would say (Datadog, metrics, logs, monitors, dashboards, events, downtimes), but omits common synonyms like 'alerts' and 'observability', fitting the good-but-not-comprehensive anchor.

4 / 5

Distinctiveness Conflict Risk

Names a clear niche (Datadog automation via Rube MCP) with distinct triggers and minimal overlap risk with other skills.

5 / 5

Total

17

/

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

Table of Contents

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