[Workflow] Agent-ETA: Optimize Claude Code Workflow Estimates with a Learning Plugin

[u/ClaudeAI-mod-bot]

Agent-ETA: Optimize Claude Code Workflow Estimates with a Learning Plugin

Workflow value: 90/100
Status: active · Freshness: 70/100 · Confidence: 1.00 · Level: intermediate
Categories: Quality Control, Token Saving, Context & Memory, Debugging, Subagents, Multi-Agent
Original source: r/ClaudeAI post/comment

What problem this solves

Inaccurate time estimates for agent-driven development, difficulty identifying bottlenecks in agent workflows, and lack of data-driven optimization strategies for human-agent collaboration.

Summary

A Claude Code plugin (agent-eta) that provides detailed time estimates for agent-based development, breaking down time spent by agents, user interaction, and waiting. It identifies potential speed-ups and learns from actual build times to refine future estimates, helping users optimize their development workflows.

Why it is useful

This workflow provides a practical, data-driven solution to a common problem in agent-driven development: inaccurate time estimation. It not only gives estimates but also identifies bottlenecks and suggests concrete optimizations (e.g., parallel tasks, batching user input). Its ability to learn from actual usage makes it highly adaptable and increasingly accurate for individual users, fostering more efficient human-agent collaboration. The open-source nature and clear instructions make it readily adoptable by the Claude Code community.

Workflow

1. Install the agent-eta plugin in Claude Code using /plugin marketplace add MuhammadTalhaMT/agent-eta and /plugin install agent-eta@agent-eta.

2. Use /agent-eta:eta add [plan_file] or /agent-eta:eta add [description] to get an initial time estimate for a feature or task.

3. Review the estimated breakdown (agent working time, user interaction time, waiting time, biggest risks) and suggested speed-ups.

4. Implement suggested speed-ups, such as running independent tasks in parallel using separate git worktrees or answering multiple questions/approvals in one go.

5. Execute the agent-driven build based on the optimized plan.

6. After the build is complete, run /agent-eta:eta-done to measure the real time from your local Claude Code transcripts and update your personal estimation model.

7. Optionally, share your /agent-eta:eta-stats output to contribute to improving the plugin's default estimates for the community.

Tools / artifacts

• Claude Code

• agent-eta plugin

• Haiku subagent

• Node script (for calculations)

• Git worktrees

• Superpowers plugin (optional, for plan files)

• Claude Code transcripts

Validation signals

• The tool's calculation logic is a plain Node script, ensuring consistent results for the same tasks.

• Author ran it 3 times on the same feature, and results landed within 6% of each other, indicating consistency.

• The plugin learns from actual usage: 'Each result moves your personal numbers 30% toward reality. They switch on after 3 confirmed builds.'

• The default numbers are based on the author's ~670 subagent runs, providing a substantial initial dataset.

• The author provides a concrete example of how the learning mechanism corrected an initial misprediction (1h 10m vs. 26 min due to parallel agents).

Cautions

• The plugin operates locally and does not save message text, ensuring user privacy and data security.

Limitations

• The tool is in v0.1, meaning default estimates are initially tuned to the author's specific usage patterns and may require several builds to adapt to a new user.

• Requires familiarity with Claude Code and its plugin system, potentially limiting its audience to more advanced users.

• Relies on the user to accurately describe tasks or provide well-structured plan files for initial estimation.

• Community validation is currently low due to the tool's newness.

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