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configs-targeting

Configure config targeting rules to control which variations serve to different users. Enable percentage rollouts, attribute-based rules, segment targeting, and guarded rollouts.

57

Quality

66%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Risky

Do not use without reviewing

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/agentcontrol/configs-targeting/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

64%

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

This is a solid, actionable skill with comprehensive API coverage and executable examples for config targeting. Its main weaknesses are verbosity (the Python class and common patterns sections largely duplicate the curl examples) and the lack of explicit validation/verification steps after making targeting changes. The content would benefit from splitting reference material into separate bundle files.

Suggestions

Add a verification step after each targeting change (e.g., 'GET targeting again and confirm the rule appears in the response') to create a proper feedback loop.

Move the full Python class implementation and reference tables (operators, instructions) into separate bundle files, keeping only a concise overview and key examples in SKILL.md.

Remove the 'Common Patterns' section or consolidate it with Step 3, as the Python examples there repeat the same concepts already shown in the curl examples.

DimensionReasoningScore

Conciseness

The skill is fairly comprehensive but includes some redundancy—the Python class duplicates what the curl examples already demonstrate, and the common patterns section largely repeats the API examples with minor variations. The instruction reference tables and operator reference are useful but the overall document could be tightened significantly.

2 / 3

Actionability

The skill provides fully executable curl commands and a complete Python class with concrete method signatures, real API endpoints, proper headers, and realistic JSON payloads. The examples are copy-paste ready with clear placeholder values.

3 / 3

Workflow Clarity

Steps 1-3 provide a clear sequence (get targeting → edit default rule → add rules), and the important callout about turnTargetingOn not working is valuable. However, there are no explicit validation checkpoints—after making targeting changes, there's no step to verify the targeting was applied correctly or a feedback loop for error recovery.

2 / 3

Progressive Disclosure

The document has good section organization and references related skills and external docs. However, the Python implementation (~120 lines) and the extensive reference tables could be split into separate files, as the document is quite long for a SKILL.md overview. No bundle files exist to offload this content to.

2 / 3

Total

9

/

12

Passed

Description

67%

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 does well at listing specific capabilities and occupying a clear niche around config targeting and rollout strategies. However, it lacks an explicit 'Use when...' clause and could benefit from more natural trigger terms that users would actually say when needing this skill, such as 'feature flags' or 'gradual rollout'.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user wants to set up feature flag targeting, control which users see a variation, or configure rollout percentages.'

Include more natural trigger terms users might say, such as 'feature flags', 'A/B testing', 'canary release', 'gradual rollout', or 'user targeting'.

DimensionReasoningScore

Specificity

Lists multiple specific concrete actions: 'configure config targeting rules', 'percentage rollouts', 'attribute-based rules', 'segment targeting', and 'guarded rollouts'. These are distinct, actionable capabilities.

3 / 3

Completeness

Clearly answers 'what does this do' (configure targeting rules with various rollout strategies), but lacks an explicit 'Use when...' clause. The 'when' is only implied by the actions described, which caps this at 2 per the rubric guidelines.

2 / 3

Trigger Term Quality

Includes some relevant terms like 'targeting rules', 'percentage rollouts', 'segment targeting', and 'guarded rollouts', but these are somewhat domain-specific jargon. Missing more natural user phrases like 'feature flags', 'A/B testing', 'canary release', or 'gradual rollout' that users might actually say.

2 / 3

Distinctiveness Conflict Risk

The description carves out a clear niche around config targeting rules with specific rollout strategies (percentage, attribute-based, segment, guarded). This is distinct enough to avoid conflicts with general configuration or deployment skills.

3 / 3

Total

10

/

12

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (508 lines); consider splitting into references/ and linking

Warning

Total

10

/

11

Passed

Repository
launchdarkly/ai-tooling
Reviewed

Table of Contents

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