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researching-codebases

Use when answering complex questions about a codebase that require exploring multiple areas or understanding how components connect - coordinates parallel sub-agents to locate, analyze, and synthesize findings

62

Quality

72%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Advisory

Suggest reviewing before use

Fix and improve this skill with Tessl

tessl review fix ./researching-codebases/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

This is a well-structured coordination skill that efficiently communicates a multi-step research workflow. Its strengths are clear sequencing, good scoping (when to use/not use), and concise writing that respects Claude's intelligence. The main weakness is that actionability depends heavily on external files for concrete details (script invocations, agent types, output formats), and without those bundle files present, the skill alone doesn't provide fully executable guidance.

Suggestions

Add a brief concrete example of a `task` tool invocation showing the subagent_type parameter and a sample task description, so the skill is actionable even without reading agent-selection.md

Include at least one example script invocation with arguments (e.g., `python .research/scripts/search-research.py 'authentication flow'`) to make the research-tools references immediately actionable

DimensionReasoningScore

Conciseness

The content is lean and efficient. It avoids explaining concepts Claude already knows (like what sub-agents are or how codebases work), uses terse bullet points, and every section earns its place. The 'When NOT to Use' section efficiently scopes the skill.

3 / 3

Actionability

The workflow provides clear steps and references to scripts (list-research.py, search-research.py, etc.), but lacks concrete executable examples of how to invoke these scripts or the `task` tool. The guidance is specific in structure but relies heavily on external files (agent-selection.md, research-tools.md, output-format.md) for the actual executable details.

2 / 3

Workflow Clarity

The workflow is clearly sequenced (steps 0-5) with explicit ordering constraints ('Read mentioned files first BEFORE spawning agents', 'Wait for ALL agents to complete before synthesizing'). Validation checkpoints are present (read context first, wait for all agents, synthesize only after completion). The common mistakes section reinforces the critical ordering requirements.

3 / 3

Progressive Disclosure

The skill references several external files (agent-selection.md, research-tools.md, output-format.md) which is good progressive disclosure structure, but since no bundle files were provided, we cannot verify these references exist or are well-structured. The references are clearly signaled but some are mentioned multiple times and the navigation could be more consolidated (e.g., a single reference section rather than inline mentions).

2 / 3

Total

10

/

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 has a solid structure with an explicit 'Use when' clause and clearly communicates the multi-agent coordination aspect. However, it leans toward abstract language ('exploring multiple areas', 'synthesize findings') rather than concrete actions, and the trigger terms could better match natural user language. The distinctiveness is moderate since the complexity threshold for triggering this skill over simpler code analysis tools is unclear.

Suggestions

Add more natural trigger terms users would say, such as 'code architecture', 'how does X connect to Y', 'trace through the code', 'cross-module dependencies', 'understand the codebase'.

Replace abstract phrases like 'locate, analyze, and synthesize findings' with concrete actions such as 'trace call chains, map component dependencies, identify shared interfaces, and summarize cross-module interactions'.

DimensionReasoningScore

Specificity

It names the domain (codebase analysis) and describes some actions ('locate, analyze, and synthesize findings', 'exploring multiple areas', 'understanding how components connect'), but these are somewhat abstract rather than listing multiple concrete, specific actions like 'trace call chains, map dependencies, identify shared interfaces'.

2 / 3

Completeness

Explicitly answers both what ('coordinates parallel sub-agents to locate, analyze, and synthesize findings') and when ('Use when answering complex questions about a codebase that require exploring multiple areas or understanding how components connect') with a clear 'Use when' clause.

3 / 3

Trigger Term Quality

Includes some relevant terms like 'codebase', 'complex questions', 'components connect', but misses many natural user phrases like 'how does this work', 'find where X is used', 'code architecture', 'trace through', 'understand the code', 'code exploration'. The phrase 'coordinates parallel sub-agents' is implementation jargon, not a user trigger term.

2 / 3

Distinctiveness Conflict Risk

The description carves out a niche around complex multi-area codebase questions with sub-agent coordination, which is somewhat distinctive. However, 'answering questions about a codebase' could overlap with simpler code search or code explanation skills, and the boundary of 'complex' vs simple questions is ambiguous.

2 / 3

Total

9

/

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
joshuadavidthomas/agent-skills
Reviewed

Table of Contents

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