> ## Documentation Index
> Fetch the complete documentation index at: https://docs.decimal.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Skills & Data Pipeline

> Skills (SKILL.md), datasets, export formats (SFT/DPO), replay, and repair.

The two final pieces: how skills augment agent behavior, and how DecimalAI turns evaluated, compatible traces into training data.

## Skills

Skills are reusable instruction files that augment agent behavior. They follow the open [agentskills.io](https://agentskills.io) specification created by Anthropic, but DecimalAI adds observability, auto-versioning, and effectiveness tracking on top.

### Skill

A **skill** is a structured instruction file (typically `SKILL.md`) that an agent loads on demand to modify its behavior. Skills combine prompt instructions with configuration metadata.

A skill is **not a tool.** A skill is markdown that shapes how the agent thinks — it's loaded into the prompt and never executes. A tool is an executable function the LLM calls during a run. A skill can *reference* tools, but it can't *do* anything itself.

<Note>
  **Skill vs Tool:** A skill is a structured instruction that modifies *how the agent thinks*. A tool is an executable function that modifies *what the agent can do*. A skill can reference tools (e.g., "use the search\_docs tool to find examples"), but a skill is not a tool itself.

  |                  | Skill                                                               | Tool                                              |
  | ---------------- | ------------------------------------------------------------------- | ------------------------------------------------- |
  | **What it is**   | Structured instruction file (SKILL.md)                              | Executable function with JSON Schema interface    |
  | **How it works** | Loaded into the prompt at runtime                                   | Called by the LLM during execution                |
  | **Versioned by** | Content hash of instruction text                                    | JSON Schema of parameters + return type           |
  | **Example**      | "When reviewing code, check for security vulnerabilities and style" | `search_docs(query: str, limit: int) → List[Doc]` |
</Note>

### Skill Activation

A record of which skills were active during a specific trace. Reported by the SDK via the `active_skills` field. Used to measure per-skill effectiveness — which skills correlate with higher quality outputs.

Effectiveness rolls up into a single quality measure (**SkillScore**), and skills are published, forked, and discovered through the **registry**. Two pointers to go deeper:

* [SkillScore](/guides/skillscore) — how per-skill effectiveness is measured and scored.
* [Registry](/guides/registry) — how skills are published, forked, and discovered.

→ See [Skills](/guides/skills) for the full guide.

## Data Pipeline

The final stage of the lifecycle: turning evaluated, compatible traces into training data.

### Dataset

A **dataset** is a curated collection of training examples built from filtered production traces. The key insight: by combining manifest compatibility + eval scores, DecimalAI ensures training data is both **current** (recorded against the latest agent config) and **high-quality** (passed evaluation).

→ See [Datasets & Training](/guides/datasets) for the full guide.

### Export Formats

| Format  | Full Name                      | How It Works                                                                                                                |
| ------- | ------------------------------ | --------------------------------------------------------------------------------------------------------------------------- |
| **SFT** | Supervised Fine-Tuning         | Each row is an input→output pair from an LLM call. Trains the model to replicate the agent's best behavior.                 |
| **DPO** | Direct Preference Optimization | Each row has a "chosen" (good) and "rejected" (bad) response for the same input. Trains the model to prefer better outputs. |

### Replay

**Replay** re-runs a historical trace's input against the current version of your agent. The original output and the replayed output are then compared — often by a pairwise LLM judge — to measure whether the agent improved or regressed.

Replayed traces have `source_type="replay"` and generate DPO preference pairs (original = rejected, new = chosen — or vice versa if the new version regressed).

→ See [Replay](/guides/replay) for the full guide.

### Repair

**Repair** mechanically fixes a trace to be compatible with a new manifest version. Examples: renaming a tool parameter, removing references to a deleted field. Repairs are deterministic (zero LLM cost) and fully auditable.

→ See [Manifests & Versioning](/guides/manifests#repair-fix-traces-mechanically) for repair details.

## Next

<CardGroup cols={2}>
  <Card title="Training Pipeline Tutorial" icon="graduation-cap" href="/tutorials/training-pipeline">
    End-to-end: trace → evaluate → fine-tune.
  </Card>

  <Card title="Glossary" icon="book-bookmark" href="/glossary">
    Quick A-Z reference for any term.
  </Card>
</CardGroup>