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agently-request

Use when the user is shaping Agently request-side behavior: model setup, settings files, prompt management, structured output, response reuse, streaming consumption, session memory, embeddings, knowledge-base indexing, retrieval, or retrieval-backed answers within one request family.

60

Quality

69%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/agently-request/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%

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

This skill functions well as a routing and policy document for Agently's request-side features, with excellent progressive disclosure and clear navigation to reference files. Its main weaknesses are the lack of executable code examples (relying on referenced files for implementation details) and a somewhat verbose Native-First Rules section that could be tightened. The anti-patterns section adds value but the overall document would benefit from at least one concrete code snippet demonstrating a common pattern.

Suggestions

Add 1-2 minimal executable code examples for the most common patterns (e.g., a basic model request with `.output()` or `.image()`) to improve actionability without bloating the overview.

Tighten the Native-First Rules section by merging related bullets (e.g., combine the three bullets about model-based evaluation/routing into one concise rule) to improve conciseness.

Consider adding a brief 'typical workflow' showing the sequence: setup → prompt → output contract → consume result, with a validation checkpoint, to improve workflow clarity.

DimensionReasoningScore

Conciseness

The skill is moderately efficient but includes several verbose bullet points that over-explain concepts Claude would already understand (e.g., explaining when to use model judges vs keyword checks, detailed arithmetic delegation rules). The routing table and anti-patterns are reasonably lean, but the 'Native-First Rules' section is quite long with some redundancy and could be significantly tightened.

2 / 3

Actionability

The skill provides specific API method names (`.image()`, `.output()`, `get_result()`, `workspace.build_context()`) and clear routing guidance, but lacks executable code examples. Most guidance is directive prose rather than copy-paste-ready code snippets, making it closer to architectural guidance than concrete implementation instructions.

2 / 3

Workflow Clarity

The routing table provides a clear decision tree for which reference file to consult, and the skill distinguishes between request-side and orchestration-side work. However, there are no explicit multi-step workflows with validation checkpoints — the skill is primarily a routing/rules document without sequenced processes or feedback loops.

2 / 3

Progressive Disclosure

The skill excels at progressive disclosure: it serves as a clear routing overview with a well-organized 'Route Inside This Skill' section pointing to six specific reference files, a 'Read Next' section listing all references, and appropriate separation between the overview and detailed content. References are one level deep and clearly signaled.

3 / 3

Total

9

/

12

Passed

Description

82%

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 provides good completeness with an explicit 'Use when' clause and a comprehensive list of topic areas. Its main weakness is that the listed capabilities read as noun phrases rather than concrete actions (e.g., 'prompt management' vs 'create and manage prompts'), and some terms like 'embeddings' and 'retrieval' are generic enough to potentially conflict with non-Agently skills. The Agently-specific scoping helps but could be stronger.

Suggestions

Rephrase topic areas as concrete verb-led actions (e.g., 'configure model settings', 'manage prompts', 'index knowledge bases') to improve specificity.

Add a brief 'what this does' clause before the 'Use when' to more clearly separate capability description from trigger guidance, e.g., 'Guides configuration and usage of Agently's request-side features including...'

DimensionReasoningScore

Specificity

The description names a specific domain (Agently request-side behavior) and lists several actions/areas like 'model setup, settings files, prompt management, structured output, response reuse, streaming consumption, session memory, embeddings, knowledge-base indexing, retrieval, retrieval-backed answers.' However, these read more as a list of topic areas than concrete actions (no verbs like 'configure', 'create', 'manage').

2 / 3

Completeness

The description explicitly starts with 'Use when...' providing clear trigger guidance for when Claude should select this skill. It also answers 'what' by listing the specific areas of Agently request-side behavior it covers. Both what and when are clearly addressed.

3 / 3

Trigger Term Quality

The description includes many natural keywords a user working with Agently would use: 'model setup', 'settings files', 'prompt management', 'structured output', 'streaming', 'session memory', 'embeddings', 'knowledge-base indexing', 'retrieval'. These are terms users would naturally mention when seeking help with these features.

3 / 3

Distinctiveness Conflict Risk

The description is specific to 'Agently request-side behavior' which helps distinguish it, and the 'within one request family' qualifier adds scoping. However, terms like 'prompt management', 'embeddings', 'retrieval', and 'streaming' are quite broad and could overlap with other AI/LLM-related skills that aren't Agently-specific.

2 / 3

Total

10

/

12

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.

Validation11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository
AgentEra/Agently-Skills
Reviewed

Table of Contents

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