# Explain Results

> Explain training results in plain English — translates metrics into actionable insights for any audience.

- **Type:** Skill
- **Install:** `agentstack add skill-macroman5-autotrain-yolo-explain-results`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [MacroMan5](https://agentstack.voostack.com/s/macroman5)
- **Installs:** 0
- **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [MacroMan5](https://github.com/MacroMan5)
- **Source:** https://github.com/MacroMan5/autotrain-yolo/tree/master/.claude/skills/explain-results

## Install

```sh
agentstack add skill-macroman5-autotrain-yolo-explain-results
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Explain Results

Translates YOLO training metrics into plain-English explanations. Two audience modes: **non-technical** (default) and **technical**.

## Data

Find the experiment to explain:

1. If user specifies a path or experiment ID, use that
2. Otherwise, find the latest `experiments/exp_NNN_*` directory (highest NNN)

Read from the experiment directory:
- `report.md` — experiment summary with metrics and deltas
- `metrics.yaml` — structured metrics, per-class AP, overrides, config
- `train/results.csv` or `results.csv` (if available) — epoch-by-epoch training log

Also read:
- `experiments/summary.md` — for historical context (how this compares to other runs)
- `experiments/dataset_profile.yaml` — for dataset size, class counts, image counts
- Reference thresholds from `skills/analyze-results/resources/metrics-guide.md`

## Report Structure

Generate a plain-English report with these sections:

### 1. What Happened

Summarize the training run in one paragraph a project manager could understand.

Template: "We trained a [model] for [X epochs] on [Y images] across [Z classes]. The training [completed normally / stopped early at epoch N / showed signs of overfitting]. This took approximately [time if available]."

Include what changed vs baseline if this is not the baseline run (e.g., "This run increased the image size from 640 to 1280 pixels").

### 2. How Good Is It

Translate the core metrics using analogies, not jargon. Use the metrics-guide.md thresholds to provide context.

| Quality | mAP50-95 | Plain English |
|---------|----------|---------------|
| Great   | >0.60    | "The model reliably finds and precisely locates objects — ready for production use" |
| Good    | 0.45-0.60| "The model catches most objects and places boxes reasonably well — solid for many applications" |
| Fair    | 0.30-0.45| "The model finds some objects but often misses or misplaces them — needs more work" |
| Poor    |  "correctly identifies the object 85 out of 100 times it makes a detection")
- **Recall**: "Out of all the objects actually present, the model finds X%" (e.g., recall=0.70 -> "catches 70 out of every 100 objects in the images")
- **mAP50**: "How well it draws boxes around objects (at a forgiving threshold)" — use this to explain detection quality
- **mAP50-95**: "The overall score — combines finding objects AND precisely outlining them"

If there is a baseline comparison, translate the delta: "Compared to our starting point, this run [improved/decreased] overall detection by [X points]. In practical terms, [concrete impact]."

### 3. What It's Best At

List the top-performing classes by per-class AP (from metrics.yaml `per_class_ap`).

Use plain language: "The model is strongest at detecting [class] — it correctly finds and outlines [X]% of them."

Group into tiers using metrics-guide.md per-class thresholds:
- **Nailed it** (AP > 0.80): These classes are well-learned
- **Getting there** (AP 0.60-0.80): Decent but room to improve
- **Needs work** (AP 0.40-0.60): Struggling — likely needs more training data
- **Not yet** (AP .md`.

## Guidelines

- Keep it practical — no AI hype, no buzzwords
- Use "the model" not "the AI" or "the neural network"
- Percentages and fractions over decimal scores: "85 out of 100" not "0.85"
- Frame struggles as opportunities: "needs more examples" not "fails at"
- When in doubt, explain less and recommend more
- Always ground quality judgments in the metrics-guide.md thresholds — don't invent your own scale
- If data is missing (no results.csv, no per-class AP), say so plainly and skip that section rather than guessing

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [MacroMan5](https://github.com/MacroMan5)
- **Source:** [MacroMan5/autotrain-yolo](https://github.com/MacroMan5/autotrain-yolo)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-macroman5-autotrain-yolo-explain-results
- Seller: https://agentstack.voostack.com/s/macroman5
- Browse the marketplace: https://agentstack.voostack.com/browse

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