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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.

55

Quality

62%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Risky

Do not use without reviewing

Fix and improve this skill with Tessl

tessl review fix ./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 thorough, actionable skill with executable curl and Python examples covering the full range of config targeting operations. Its main weaknesses are verbosity from duplicated examples across curl/Python/common patterns sections, and the lack of explicit validation steps after making targeting changes. The content would benefit from splitting the Python implementation and reference tables into separate files.

Suggestions

Add explicit validation steps after PATCH operations (e.g., 'GET targeting again to verify the rule was applied correctly') to create a feedback loop for error recovery.

Reduce redundancy by removing the Common Patterns section or the Python class—both largely repeat the curl examples with minor variations.

Move the Python implementation and reference tables (operators, instructions) into separate bundle files and reference them from the main SKILL.md to improve progressive disclosure.

DimensionReasoningScore

Conciseness

The skill is fairly comprehensive but includes some redundancy—the Python class duplicates what the curl examples already show, and the common patterns section largely repeats the API examples with minor variations. The core concepts section is reasonably lean, but the overall document could be tightened significantly.

2 / 3

Actionability

Provides fully executable curl commands and a complete Python class with concrete method signatures, real API endpoints, headers, and JSON payloads. The examples are copy-paste ready with clear placeholder values, and the operator/instruction reference tables give specific, usable details.

3 / 3

Workflow Clarity

The 3-step workflow (get targeting → edit default rule → add rules) is clearly sequenced, but there are no explicit validation checkpoints or error-recovery feedback loops between steps. For an API-based operation that could misconfigure targeting rules, the skill should include verification steps (e.g., GET after PATCH to confirm changes) to catch errors.

2 / 3

Progressive Disclosure

The document has clear section headers and references to related skills and external docs, but it's quite long with inline content (full Python class, multiple JSON examples, reference tables) that could be split into separate files. Without bundle files, all content is monolithically presented in one document.

2 / 3

Total

9

/

12

Passed

Description

60%

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 a good job listing specific capabilities related to config targeting and rollout strategies, providing clear domain-specific actions. However, it lacks an explicit 'Use when...' clause which caps completeness, and the trigger terms lean toward internal/technical jargon rather than natural user language. Adding common synonyms like 'feature flags' and explicit trigger guidance would significantly improve skill selection accuracy.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when the user wants to set up feature flags, configure targeting rules, or manage rollout strategies.'

Include natural trigger terms users would say, such as 'feature flags', 'feature toggles', 'A/B testing', 'canary release', or 'gradual rollout'.

Specify the platform or system if applicable (e.g., LaunchDarkly, ConfigCat) to improve distinctiveness and reduce conflict risk with generic configuration skills.

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' with specific capabilities, but lacks an explicit 'Use when...' clause or equivalent trigger guidance. The 'when' is only implied by the actions described.

2 / 3

Trigger Term Quality

Includes relevant domain terms like 'targeting rules', 'percentage rollouts', 'segment targeting', and 'guarded rollouts', but misses common user-facing terms like 'feature flags', 'feature toggles', 'A/B testing', 'canary releases', or 'gradual rollout'. Users may not naturally say 'config targeting rules'.

2 / 3

Distinctiveness Conflict Risk

The terms 'targeting rules', 'percentage rollouts', 'segment targeting', and 'guarded rollouts' are fairly specific to feature flag / config management systems, but 'config' is generic and could overlap with general configuration skills. Without naming the specific platform (e.g., LaunchDarkly, Split), there's some overlap risk.

2 / 3

Total

9

/

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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