Add skill-level spans to OpenTelemetry traces

## Describe the feature or problem you'd like to solve

When a Copilot CLI agent invokes a project skill, the resulting tool calls (bash, glob, etc.) are emitted as flat children of the root `invoke_agent` span. There is no intermediate span representing the skill invocation itself, and no `skill.name` attribute on the child tool spans. This makes it impossible to attribute tool calls to the skill that triggered them using the OTEL trace data.

Current trace structure:

```
invoke_agent (root)
├── execute_tool glob ← no skill attribution
├── execute_tool bash ← no skill attribution
├── execute_tool bash ← no skill attribution
├── chat model
└── chat model
```

The skill name only appears in `github.copilot.context.skills` on the `invoke_agent` span, which lists all **available** skills — not the one that was actually invoked.

## Proposed solution

Introduce an `execute_skill` (or similar) span that:

1. **Wraps the tool calls** triggered by the skill invocation, so they become children of the skill span rather than the root agent span.
2. **Carries a `skill.name` attribute** identifying which skill was executed.
3. **Is a child of the `invoke_agent` span**, preserving the existing hierarchy.

Example desired trace:

```
invoke_agent (root)
├── chat model
├── execute_skill "my-skill" ← NEW
│ ├── execute_tool glob ← child of skill span
│ ├── execute_tool bash ← child of skill span
│ └── execute_tool bash ← child of skill span
├── chat model
└── execute_tool bash ← not part of a skill
```

Alternatively (minimal version): if nesting is not feasible, adding a `skill.name` attribute to each `execute_tool` span that was triggered within a skill invocation context would also solve the problem.

**How will it benefit GitHub Copilot CLI and its users?**

- **Skill-level latency measurement**: Users can measure how long a skill takes end-to-end, rather than manually summing individual tool call durations.
- **Tool call attribution**: Clearly distinguish which tool calls belong to a skill invocation vs. general agent reasoning, enabling targeted debugging and optimization.
- **Faster failure diagnosis**: When a skill fails, users can immediately identify which child tool call failed without reading through command arguments.
- **Aggregated dashboards**: Teams can build per-skill usage and performance dashboards across sessions, which is essential for monitoring custom skill reliability at scale.

## Example prompts or workflows

1. **Debugging a failed skill**: A custom skill fails intermittently. The user exports OTEL traces and filters for `execute_skill` spans with error status. They drill into the child `execute_tool` spans to see exactly which bash command failed — without reading every tool call in the session.

2. **Measuring skill performance over time**: A team ships a custom skill and wants to track its p50/p95 latency across sessions. They query their trace backend for `execute_skill` spans where `skill.name = "my-skill"` and chart duration over time. Today this is impossible without manually parsing tool call arguments.

3. **Attributing token/tool usage to skills**: A user runs a session where the agent invokes three different skills. They want to see how many tool calls each skill made and how much time each consumed. With skill-level spans, this is a simple trace query. Without them, all tool calls are indistinguishable siblings under `invoke_agent`.

4. **Building an observability dashboard**: A team sends OTEL traces to their backend and builds a dashboard showing skill invocation frequency, success rate, and latency. This requires a reliable `skill.name` attribute or dedicated span — inferring skill boundaries from bash command content is fragile and breaks when scripts change.

5. **Auditing skill usage in CI/automation**: In cloud agent jobs, a team wants to verify that the correct skills were invoked and completed successfully. Skill-level spans would make this a simple trace query rather than log parsing.

## Additional context

- Hooks (`preToolUse`/`postToolUse`) also don't carry skill context — `toolName` is `"bash"` / `"glob"` etc., with no reference to the parent skill. Adding `skillName` to hook payloads would be a complementary improvement.
- The `skill` tool invocation itself may appear as an `execute_tool skill` span, but subsequent tool calls triggered by the skill are not linked to it via `parentSpanId`.
- Observed on Copilot CLI v1.0.60.

首先追踪在 Copilot CLI v1.0.60 中调用 skill 期间 `execute_tool` span 的创建位置，然后检查 `parentSpanId` 和 skill 上下文如何通过 `skill` 工具以及 `preToolUse`/`postToolUse` hook 传递。完成的标准是：trace 中显示带有 `skill.name` 的 `execute_skill` span 以及正确嵌套的子工具 span；如果无法实现嵌套，则对这些工具 span 进行一致的归属。

由索引模型根据 Issue 内容生成。