cappasoft/web-dev-estimation
Estimates implementation time for web development tasks (frontend and/or backend) by analyzing the existing codebase and calibrating for an AI coding agent as executor — not a human developer. Use when the user asks about effort, sizing, or feasibility: 'how long', 'how much work', 'estimate this', 'what is the effort', 'breakdown this task', 'can we do this in X days', 'is this a big task', 'how complex is', 'what's involved in', 'fits in the sprint', 'rough sizing', 't-shirt size', 'story points'. Also use when the user describes a feature and implicitly wants to know scope — e.g. 'we need to add X to the app', 'thinking about building Y', 'is this feasible by Friday'. Supports batch estimation from any structured source (BMAD output, spec folders, PRDs, backlogs, task lists) — use when the user mentions 'estimate the stories', 'estimate the epic', 'scan the backlog', 'estimate all tasks', 'estimate the specs', or points to a folder of task/story/spec files.
95
94%
Does it follow best practices?
Impact
98%
1.40xAverage score across 5 eval scenarios
Passed
No known issues
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an exemplary skill description that excels across all dimensions. It provides specific capabilities, comprehensive trigger terms covering both explicit and implicit user requests, clear 'Use when' guidance, and a distinctive niche that differentiates it from general development or project management skills. The description is thorough without being padded with fluff.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple concrete actions: 'Estimates implementation time', 'analyzing the existing codebase', 'calibrating for an AI coding agent', 'Supports batch estimation from any structured source'. Clearly specifies domain (web development, frontend/backend) and executor context. | 3 / 3 |
Completeness | Clearly answers WHAT (estimates implementation time for web dev tasks, batch estimation from structured sources) AND WHEN with explicit 'Use when' clauses covering both direct requests and implicit scope questions. Multiple trigger scenarios are enumerated. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms users would say: 'how long', 'how much work', 'estimate this', 'effort', 'sizing', 'feasibility', 't-shirt size', 'story points', 'fits in the sprint', 'rough sizing', 'is this a big task', 'how complex is'. Also includes batch-specific triggers like 'estimate the stories', 'scan the backlog'. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche: AI agent time estimation for web development. The specific focus on 'AI coding agent as executor — not a human developer' and the extensive list of estimation-specific triggers make it unlikely to conflict with general coding or project management skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
85%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-crafted skill with excellent actionability and workflow clarity. The executable bash commands, structured output templates, and explicit validation steps make it highly usable. Minor verbosity in explanatory sections and some repetition of the agent-vs-human calibration concept prevent a perfect conciseness score, but overall this is a strong, production-ready skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably efficient but includes some redundant explanations (e.g., explaining why agents differ from humans multiple times) and could tighten the workflow descriptions. The batch estimation section adds significant length but is justified by the complexity. | 2 / 3 |
Actionability | Provides fully executable bash commands for stack detection and codebase reading, concrete classification tables, specific output formats with exact templates, and clear step-by-step workflows. All guidance is copy-paste ready. | 3 / 3 |
Workflow Clarity | Excellent multi-step workflow with explicit validation (Step 4.5 self-check), clear sequencing (Steps 0-5), feedback loops for error correction, and specific checkpoints. The batch workflow also includes clear sequencing with consolidated output. | 3 / 3 |
Progressive Disclosure | Well-structured with clear overview and one-level-deep references to calibration.md, patterns.md, and honesty-rules.md. Content is appropriately split between the main skill and reference files, with clear signaling of when to consult each reference. | 3 / 3 |
Total | 11 / 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.
Validation — 11 / 11 Passed
Validation for skill structure
No warnings or errors.
Reviewed
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