estimate accuracy calibrator
Converts raw engineering estimates into calibrated P10/P50/P90 probability distributions using historical evidence.
$5
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+21 moreClaude CodeCursorCodex CLIMuseDots
Manus
Grok
Grok Bot+18 agentsestimate accuracy calibrator
Example session with this skill installed
Calibrate this estimate: 10 days for a new payment gateway integration. Historicals show a 1.4x median overrun on integrations (N=5). Drivers: API docs are messy, team is new to this gateway.
- Read your context and instructions
- Compiled the estimate accuracy calibrator
I have processed the payment gateway integration estimate using the calibration framework. I applied a 1.4x historical multiplier and adjusted for the "New Domain" class quality and documented uncertainty drivers.
Calibrated Forecast: Payment Gateway Integration
1. Original Estimate (Preserved)
10.0 days (Inside-view anchor)
2. Historical Reference Data
| Metric | Value | Notes |
|---|---|---|
| Sample Size (N) | 5 | Low confidence due to small N |
| Median Overrun | 140% (1.4x) | Baseline for comparable integrations |
| P90 Overrun | 195% (1.95x) | Worst-case historical performance |
| Data Quality | 0.60 | Moderate; relevant but limited sample |
3. Calibration Adjustments
| Factor | Adjustment | Impact |
|---|---|---|
| Class: New Domain | +50% | High novelty architecture risk |
| Driver: Messy Docs | +20% | Search/debugging overhead |
| Driver: New Team | +15% | Ramp-up time |
| Combined Multiplier | 2.25x | Compounded uncertainty |
4. Calibrated Forecast Distribution
| Percentile | Forecast | Probability |
|---|---|---|
| P10 (Best Case) | 14.5 days | 10% chance of finishing earlier |
| P50 (Median) | 22.5 days | Most likely outcome |
| P90 (High Conf) | 31.0 days | 90% chance of finishing within this |
5. Commitment Threshold Test
- Target: 10 days (Original Estimate)
- Probability of Success: < 5%
- Risk Level: EXTREME. The target falls well below the P10.
6. Controlling Uncertainty
Class Quality (New Domain) is the primary driver, contributing ~45% of the total range width. Reducing this through a 1-day spike would significantly tighten the forecast.
7. Forecast vs. Promise Separation
Evidence-Based Forecast
22.5 days (P50) to 31.0 days (P90). The data suggests the current 10-day target is mathematically improbable.
Recommended Promise
28 days. This provides a 75% confidence level while allowing for the documented documentation and team experience risks.
Next steps
- Conduct a 24-hour technical spike to reduce the "New Domain" uncertainty.
- Review the P90 with stakeholders to negotiate a realistic buffer.
- Update the forecast once the API documentation has been fully audited.
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About this skill
The problem
Engineering estimates are often based on the "inside view," leading to systemic underestimation and missed deadlines. Developers struggle to account for hidden complexity, historical overruns, and external dependencies when pressured to provide a single number.
What it does
- Preserves the original anchor estimate while applying evidence-based calibration.
- Calculates P10, P50, and P90 probability distributions based on historical overrun factors and class quality.
- Decomposes uncertainty into specific drivers like requirement volatility and dependency risk.
- Tests commitment thresholds to determine the statistical probability of meeting a hard deadline.
- Separates the evidence-based forecast from the external promise to prevent conflating data with commitments.
Why this beats prompting it yourself
General prompts usually result in vague "add a 20% buffer" advice. This skill uses a structured mathematical framework to decompose uncertainty and apply historical reference class forecasting, providing a defensible audit trail for why a project might take longer than originally guessed.
Use cases
- Calibrating a sprint plan before committing to a hard release date.
- Evaluating the risk level of a high-novelty R&D project with no clear precedent.
- Presenting a data-backed range to stakeholders instead of a single, fragile date.
- Identifying which specific technical unknown is creating the most scheduling risk.
Known limitations
Requires honest inputs regarding historical data quality. Forecast accuracy scales with the quantity of comparable historical data points provided.
Requires historical estimate-to-actual data or class quality labels to function. Reliability decreases if team capacity is highly volatile or undefined.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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