MedAdemBHA/mythical-agent-month

Your agent said 5 months. Agents built it in 35 hours.
An estimation skill for Claude Code, Codex, Cursor, Gemini CLI and Copilot (plus a paste-in prompt for anything else) that stops AI from quoting human-team timelines for agent-built software.
> Brooks, 1975: "Adding manpower to a late software project makes it later."
> 2026: your agent still estimates like it's 1975.
## The problem
Ask an AI "how long will this roadmap take?" and you get "3-5 months", sprint plans, story points. That's human-team pace, learned from human tickets.
We ran the real thing: a 106-task, 7-repo SaaS roadmap built by autonomous agents (worktrees, parallel lanes, tests first, reviewer agents, phase-end suites).
| | Naive AI estimate | Real run |
| Time | "about 5 months" (60-100 days) | 35.3 h agent wall-clock |
| Agent-hours | - | 64.2 |
| Usage | - | ~35-40% of a weekly limit |
| Ready to merge | - | ~3 days |
The code wasn't the slow part. Tests, review, decisions and the usage limit were.
### How accurate is the skill? Honestly:
| Test | Raw "likely" wall-clock | Plan (likely × 1.3, the bold number it prints) |
| In-sample fit: defaults measured on this run, run on its own task graph | 31.1 h vs 35.3 h real (-12%) | 40 h (+14%) |
| Held-out: fit on phases 0-3, predict phases 4-9 blind | 12.4 h vs 19.8 h real (-37%) | 16 h (-19%) |
So it gets the order of magnitude right: hours, not months. The raw model leans optimistic, so until you calibrate, the number it prints in bold is the plan (likely × 1.3), with the likely value and the range beside it. It's still n = 1: one project, one laptop, one setup. The method is in examples/holdout.md. A second, independent run is the missing evidence. Yours would help.
## What it does
It never gives one big number. It separates:
Machine time: agent wall-clock and agent-hours, using the task graph's critical path and the parallelism you really get.
Human effort: prompting, review per commit, local setup, click-through.
Waiting: decisions and usage-limit resets. They're calendar time, never effort.
Usage: the % of your weekly limit, so you know whether the run fits this week.
There are two modes:
| Mode | When | Output |
| A: Developer + AI | You drive an assistant task by task | Focus hours and days, with a calendar range |
| B: Autonomous agents | A plan split into tasks that agents build, often in parallel | Wall-clock, agent-hours, critical path, usage %, calendar to ready-to-merge |
Both modes show the no-AI pace as a comparison line only.
## Numbers from real runs, not vibes
The defaults are measured on 398 subagent runs from one real build, not hand-tuned:
| Agent kind | Median | p75 |
| Code task, high risk (big model) | 30 min | 39 min |
| Code task, normal risk | 11 min | 24 min |
| Code task, low risk (small model) | 3.5 min | 8 min |
| Reviewer agent | 8 min | 12 min |
| Merge | / /subagents --tasks tasks.json
It's read-only: it uses timestamps, labels, model names and token counts, and prints no content. You get: