estimate-before-build
Run the following command in DeepSeek Harness:
dsh plugin install ChenneyZhuang/estimate-before-build
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install ChenneyZhuang/estimate-before-build in the DeepSeek Harness terminal to install this plugin, sourced from https://github.com/ChenneyZhuang/estimate-before-build
About this plugin
"It'll take a few minutes" is where scope creep pays rent. The agent starts editing, the request turns out to be three times the size anyone imagined, and stopping now looks like failure. The expensive failure is never the wrong estimate — it is the unmade decision. estimate-before-build turns that silent slide into a choice: this scope for this budget, a smaller scope, or a timeboxed spike first.
The core mechanism replaces hours with four size bands (S / M / L / XL). Hours invite false precision and calendar promises; bands communicate shape. For M and above you must name the uncertainty driver — risk lives where the unknown lives, and that is where a spike belongs. The estimate does not pick for you. It puts full scope, a reduced scope that fits the budget, and a timeboxed probe on the table so the decision is yours to make.
Record the estimate, then compare it against actuals at the finish line, task by task. That reconciliation is the only mechanism that makes the next estimate better. Skip it and calibration never happens.
Built for engineers and teams driving AI agents on coding or build tasks who want the scope decision laid bare before the first line of code is written.
Use Cases
- Run a band estimation before the agent starts editing, putting the scope decision on the table
- For M and above, name the uncertainty driver and decide between a spike and a full build
- Reconcile estimates against actuals per task to sharpen the next round
Best For
- Engineers driving AI agents on build tasks who want scope decisions made explicit
- Teams repeatedly hit by scope creep where "a few minutes" turned into days
- Individual developers who treat estimate calibration as a continuous practice
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