Agent Skill for Claude Code — Agent-calibrated web development estimation

Estimates implementation time for web development tasks (frontend and/or backend) by reading the actual codebase and applying multipliers calibrated for AI coding agents, not human developers.

Most estimation tools assume a human developer. AI coding agents have a radically different performance profile:

• 10× faster on mechanical work (boilerplate, CRUD, wiring)
• Comparable or slower on ambiguous, exploratory, or poorly-documented work
• Higher cost of wrong direction — agents move fast in the wrong direction when specs are vague

This skill encodes that difference into a structured, honest workflow.

1. Detects your project stack automatically
2. Reads the codebase before estimating (non-negotiable)
3. Decomposes the task into sub-tasks of 5–120 agent-minutes
4. Applies agent-calibrated multipliers from a documented calibration table
5. Outputs a structured estimate with confidence level, assumptions, risks, and T-shirt size
6. Batch mode: scans BMAD Method stories, spec folders, PRDs, or backlogs and produces a consolidated estimate table

web-dev-estimation/
├── SKILL.md                    # Workflow + invocation modes (load first)
├── references/
│   ├── calibration.md          # Agent vs. human multiplier table + stack notes
│   ├── patterns.md             # Common pattern reference times + T-shirt sizing
│   └── honesty-rules.md        # Non-negotiable rules + escalation thresholds
├── evals/
│   └── evals.json              # Test scenarios for skill validation
├── bin/
│   └── install.js              # npx installer
└── package.json                # npm package for npx distribution

Progressive disclosure: only SKILL.md loads automatically. Reference files load on demand.

Recommended — via the skills CLI:

npx skills add ecappa/web-dev-estimation

Installs the skill using the open Agent Skills ecosystem. Works with Claude Code, Cursor, GitHub Copilot, Gemini CLI, and any compatible agent. Supports global (-g) and project-scoped installs.

Via Tessl registry:

tessl install cappasoft/web-dev-estimation

Versioned, evaluated skill with quality scores. Includes MCP integration for on-demand context loading.

Alternative — standalone installer:

npx web-dev-estimation

Detects your platform (Claude Code, Cursor, etc.) and installs to the right directory. Interactive prompt lets you choose the target.

From GitHub directly (no npm required):

npx github:ecappa/web-dev-estimation

Manual install:

# Claude Code
mkdir -p ~/.claude/skills/web-dev-estimation
cp -r . ~/.claude/skills/web-dev-estimation/

# Cursor
mkdir -p .cursor/skills/web-dev-estimation
cp -r . .cursor/skills/web-dev-estimation/

Any Agent Skills-compatible tool: Copy the skill folder into the tool's skill directory. See agentskills.io for details.

Automatic — Claude detects estimation intent and loads the skill:

"How long would it take to add Stripe webhooks to the app?" "Is this a big task? We need to refactor the auth layer." "Can we fit a user dashboard in this sprint?"

Direct invocation:

/estimate Add a CSV export to the orders table with date range filtering

Batch estimation (BMAD, specs, backlogs):

"Estimate all the stories in the BMAD output" "Scan the specs folder and give me a consolidated estimate" "Here are 6 tasks, estimate each one" Works natively with BMAD Method story files, spec folders, PRDs, or any task list. Produces a consolidated table with per-task sizing, totals, dependencies, and implementation order.

Re-estimation after scope change:

"Actually, skip the email notification for now." Claude applies a delta estimate without re-running the full workflow.

Full table and correction factors in references/calibration.md.

The skill auto-detects TypeScript, Python, Go, Ruby, PHP, Rust, and monorepos. Stack-specific notes in references/calibration.md cover:

• TypeScript / Next.js / React (RSC, App Router, shadcn/ui)
• Python / FastAPI / Django
• Go
• Node.js / NestJS

To calibrate for your specific codebase, add observed agent failure patterns to references/patterns.md under "Known Agent Failure Patterns".

An honest high estimate is more useful than a low estimate that misses.

The skill enforces:

• Ranges, never point estimates
• Explicit confidence levels
• Declared assumptions
• Top risk per estimate
• Escalation when scope is too vague to estimate reliably

This skill follows the Agent Skills open standard.

Calibration data gets better with real-world usage. Contributions welcome:

• Add observed agent times to references/patterns.md — the more data points, the tighter the ranges
• Add stack-specific failure patterns under "Known Agent Failure Patterns" in references/patterns.md
• Report calibration misses — open an issue when an estimate was significantly off and describe the task, expected vs. actual time, and stack context
• Open a PR with the task type, observed time, and stack context

Created by Eric Cappannelli.

• LinkedIn: linkedin.com/in/ecappannelli
• GitHub: ecappa

Crafted with love in Baie-Saint-Paul, Quebec, Canada.

If this skill saved you time, consider starring the repo or sharing it with your team.

Apache 2.0

The first agent-calibrated estimation skill in the Agent Skills ecosystem. Crafted with love in Baie-Saint-Paul, Quebec, Canada.