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configs-variations

Experiment with configs by creating and managing variations. Helps you test different models, prompts, and parameters to find what works best through systematic experimentation.

61

10.00x
Quality

71%

Does it follow best practices?

Impact

100%

10.00x

1 of 3 eval scenarios. Add 2 more for a full score.

SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/agentcontrol/configs-variations/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

67%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 a well-structured, actionable guide with a clear sequenced workflow, explicit verification steps, and strong safety guardrails for the baseline variation. Its main weaknesses are redundancy between the 'What NOT to Do' section and earlier sections, and the lack of a concrete example tool call.

Suggestions

Consolidate the 'What NOT to Do' section by removing bullets that verbatim repeat the Safety section, Step 3, and the modelConfigKey format section, or move the section earlier and drop the duplicates.

Add one example clone tool call (sourceVariationKey, key, and a single override) to make the common case copy-paste ready.

Add an explicit error-recovery loop after Step 4, e.g. 'If verification shows the wrong fields differ, delete the bad variation and recreate it with only the intended override.'

DimensionReasoningScore

Conciseness

The body is mostly efficient and free of concept over-explanation, but the 'What NOT to Do' section substantially repeats earlier guidance — 'Don't pass unchanged fields when cloning' restates Step 3, two baseline bullets restate the Safety section, and the modelConfigKey warning restates the format section — so it could be meaningfully tightened.

3 / 5

Actionability

Concrete guidance throughout: specific MCP tool names, required fields ('sourceVariationKey', 'key', 'name'), an example identifier ('gpt4o-mini-cost-test'), and exact modelConfigKey formats ('OpenAI.gpt-4o', 'Anthropic.claude-sonnet-4-5'). Per the rubric's code-vs-instruction note, absence of code is not penalized, though no example tool payload is shown.

4 / 5

Workflow Clarity

A clear Identify -> Design -> Create -> Verify sequence with explicit verification checkpoints ('use get-ai-config to confirm', 'Flag any issues') and a practical note on interpreting API responses. It falls short of a 5 because there is no explicit verify -> fix -> retry error-recovery loop.

4 / 5

Progressive Disclosure

No bundle files exist; the body is well organized into scannable sections with one clearly signaled external docs link and Related Skills pointers, and no nested references. It is slightly over the ~50-line clean-skill range and parts of the tool reference could live in a separate file, leaving minor organization gaps.

4 / 5

Total

15

/

20

Passed

Description

61%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 clearly states what the skill does with mostly concrete domain-specific language, but it omits any explicit 'when to use' trigger guidance and slips into second-person voice ('Helps you test'), capping both completeness and specificity. Trigger term coverage is good though not comprehensive.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user wants to experiment with AI configs, compare models or prompts, or A/B test variations.'

Rewrite in third person: replace 'Helps you test different models...' with 'Test different models, prompts, and parameters...' to match the rubric's voice requirement.

Include natural synonyms users would say such as 'A/B test', 'tune', or 'compare models' to broaden trigger term coverage.

DimensionReasoningScore

Specificity

Quotes several concrete actions ('creating and managing variations', 'test different models, prompts, and parameters') that fit the anchor-4 level, but 'Helps you test' is second-person voice, which the rubric penalizes by reducing the specificity score by 1.

3 / 5

Completeness

The 'what' is clear (create and manage variations to test models, prompts, and parameters), but there is no 'Use when...' clause or equivalent explicit trigger guidance, which per the judging guidelines caps completeness at 3.

3 / 5

Trigger Term Quality

Good keyword coverage ('configs', 'variations', 'models', 'prompts', 'parameters', 'experiment', 'test', 'find what works best'), but common natural synonyms like 'A/B test', 'tune', or 'optimize' are missing, so it does not reach comprehensive synonym-level coverage.

4 / 5

Distinctiveness Conflict Risk

The 'configs' + 'variations' + 'models/prompts/parameters' pairing carves out a fairly distinct experimentation niche with only minor overlap risk against generic prompt-tuning skills; it is not fully unambiguous because the LaunchDarkly/agent-config context is not named.

4 / 5

Total

14

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
launchdarkly/ai-tooling
Reviewed

Table of Contents

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