ZhangHanDong

ZhangHanDong / agent-estimation

Public

Ready to estimate AI agent work effort using tool-call rounds as the base unit instead of human time anchoring.

57
1
100% credibility
Found Feb 17, 2026 at 41 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This repository offers a skill for AI coding agents to estimate task durations more accurately by decomposing work into agent-native rounds and applying risk factors before converting to real-world time.

How It Works

1
🔍 Discover Better Estimates

While chatting with your AI coding buddy, you realize it always guesses tasks will take days like a human would, and you hear about a helpful skill to fix that.

2
📦 Add the Skill

You quickly add this estimation skill to your AI helper so it can think more accurately about its own work pace.

3
✨ AI Gets Smarter

Your AI now automatically uses the skill whenever you ask about task times, breaking things down into its natural thinking cycles.

4
💭 Ask for a Project Plan

You describe a project, like building a simple tool, and ask how long it really takes.

5
📊 Review the Clear Breakdown

You see a neat table showing work split into small modules, with round counts, risks, and a final realistic time in minutes.

✅ Plan with Real Confidence

Now you have an honest timeline that matches how fast your AI actually works, making your projects smoother and faster.

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Star Growth

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AI-Generated Review

What is agent-estimation?

Agent-estimation is an open Agent Skill that helps AI coding agents like Claude Code or Cursor deliver realistic task estimates by using tool-call rounds as the base unit, rather than human developer hours. It breaks down projects into modules, applies risk factors, and only converts rounds to wallclock time at the end—fixing the common issue where agents inflate estimates based on training data from human forums. Install it via npx skills add for instant use in prompts like "estimate rounds to add JWT auth."

Why is it gaining traction?

It stands out by enforcing agent-native thinking: decompose into modules, estimate rounds with calibrated anchors (1-2 for boilerplate, up to 15 for uncertainty), and add integration buffers—delivering markdown tables with breakdowns that feel precise and actionable. Developers notice fewer "2-3 day" overestimates turning into 30-minute realities, plus anti-pattern blocks like vibe-based padding. Ready on GitHub with skills.sh compatibility across 35+ agents, it's a quick win for agent estimation workflows.

Who should use this?

AI agent operators scoping tasks for Claude Code, Cursor, or Copilot workspaces, especially when planning CLI tools, API features, or UI tweaks like dark mode. Backend devs estimating auth or validation modules, or indie hackers gauging "estimated ready date" for prototypes. Skip if you're not using tool-calling agents.

Verdict

Worth a 5-minute npx install to test on your next agent prompt—solid framework despite 41 stars and 1.0% credibility score signaling early maturity with thin tests. Pair it with real evals to build confidence; it's raw but fixes a real agent estimation pain point.

(187 words)

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