skill-creator: parallel trigger-eval workers share one commands dir, collapsing measured trigger rate to ~1/num_workers

## Summary

`skill-creator`'s trigger-eval harness writes every worker's temp command file into a **single shared directory**, so parallel workers see each other's files. Each `claude -p` process is presented with N identical skills differing only by UUID, picks one, and is scored as "triggered" only if it happens to pick its own.

The measured trigger rate therefore collapses toward `1/num_workers` **independently of the description being tested**. Since `--num-workers` defaults to 10, `run_loop.py` optimizes against roughly 10%-scaled noise.

## Affected code

`skills/skill-creator/scripts/run_eval.py`, `run_single_query()`:

```python
project_commands_dir = Path(project_root) / ".claude" / "commands"
command_file = project_commands_dir / f"{clean_name}.md"
```

`project_root` comes from `find_project_root()`, which walks up from cwd. It is the *same path for every worker*, and workers run concurrently via `ProcessPoolExecutor`. Detection then requires the run's own `clean_name`:

```python
if clean_name in accumulated_json:
return True
```

A run where the model triggers correctly but selects a sibling worker's identical skill is recorded as a non-trigger.

## Evidence

Same eval set (20 queries), same skill, same description, same model, varying only `--num-workers`:

| `--num-workers` | Recall | Precision |
|---|---|---|
| 10 (default) | 6% | 100% |
| 3 | 20%, then 0% | 100% |
| **1** | **80%** | 90% |

Recall tracks `1/N`. The description was never the variable.

Two independent confirmations:

1. An isolated manual probe — one command file, one `claude -p`, no concurrency — triggered on the first attempt for the query `"boot the whole ineedabaker stack…"`. Replaying `run_eval.py`'s own stream-parsing logic over that transcript returns `True`. The harness scored that identical query `0/3` under default settings, twice.
2. During a `--num-workers 3` run, the host surfaced two simultaneously registered skills differing only by UUID (`run-ineedabaker-skill-5d2f8649`, `run-ineedabaker-skill-e9edc6a8`). The directory is empty after each run, so cleanup works — the collision is purely concurrent.

## Impact

`run_loop.py` selects `best_description` by comparing scores that are dominated by this race. In our run it preferred a rewrite that was not better, and reported 6% recall for a description that measures 80% when evaluated correctly. Any conclusion drawn from the default configuration is unreliable.

## Reproduction

```bash
cd skills/skill-creator
python -m scripts.run_eval --eval-set evals.json --skill-path \
--num-workers 10 --runs-per-query 1 --verbose # recall collapses
python -m scripts.run_eval --eval-set evals.json --skill-path \
--num-workers 1 --runs-per-query 1 --verbose # recall recovers
```

## Suggested fix

Isolate each worker rather than sharing one commands directory — give every invocation its own temporary project root (`tempfile.mkdtemp()` containing `.claude/commands/`) and pass it as `cwd` to the subprocess. That keeps parallelism while guaranteeing exactly one candidate skill is visible per run.

Serializing (`--num-workers 1`) also works but makes the 5-iteration loop far slower than the docs imply.

Worth noting separately: `--timeout` defaults to 30s while 10 workers contend, which independently truncates slower runs. Isolation fixes the race; the timeout default may deserve its own look.

## Environment

- `anthropics/skills` @ `3b3fad96af16a10759d930941b4520ba0c40edae`
- Claude Code 2.1.251, macOS 15 (Darwin 25.6.0), Python 3.13
- Model: `claude-opus-5`

Start in skills/skill-creator/scripts/run_eval.py at run_single_query(), then inspect find_project_root() and the subprocess invocation. Reproduce the recall difference with --num-workers 10 versus 1 from the issue; done means concurrent runs no longer expose sibling command files, preserve parallelism, and clean up their temporary directories.

Written by the indexing model from the issue text.