Auto-install uv for zero-configuration dependency management

- Automatically install uv if not found in container
- Eliminates manual dependency setup
- No fallback to python3 + manual jinja2 installation needed
- First run installs uv (~5 seconds), subsequent runs use cached version
- Update documentation to reflect automatic installation

Co-Authored-By: Claude <noreply@anthropic.com>

Author: tenpercent

.claude/skills/ck-build-analysis

@@ -166,29 +166,23 @@ echo "Generating analysis report..."
 docker cp "${SCRIPT_DIR}/analyze_build_trace.py" "${CONTAINER_NAME}:/tmp/analyze_build_trace.py"
 docker cp "${SCRIPT_DIR}/templates" "${CONTAINER_NAME}:/tmp/ck_build_analysis_templates"
 
-# Check if uv is available and use it for PEP 723 dependency management
-# Check both PATH and common install locations
-if docker exec "${CONTAINER_NAME}" bash -c "command -v uv >/dev/null 2>&1 || test -x \$HOME/.local/bin/uv"; then
-    echo "Using uv run for automatic dependency management..."
-    # Ensure uv is in PATH (handles ~/.local/bin installation)
-    docker exec "${CONTAINER_NAME}" bash -c "export PATH=\"\$HOME/.local/bin:\$PATH\" && uv run --no-project /tmp/analyze_build_trace.py \
-        ${TRACE_FILE} \
-        /workspace/${OUTPUT_FILE} \
-        ${TARGET} \
-        ${GRANULARITY} \
-        ${BUILD_TIME} \
-        /tmp/ck_build_analysis_templates"
-else
-    echo "uv not found, using python3 (requires python3-jinja2 pre-installed)..."
-    docker exec "${CONTAINER_NAME}" python3 /tmp/analyze_build_trace.py \
-        "${TRACE_FILE}" \
-        "/workspace/${OUTPUT_FILE}" \
-        "${TARGET}" \
-        "${GRANULARITY}" \
-        "${BUILD_TIME}" \
-        "/tmp/ck_build_analysis_templates"
+# Check if uv is available, install if needed, and use for PEP 723 dependency management
+if ! docker exec "${CONTAINER_NAME}" bash -c "command -v uv >/dev/null 2>&1 || test -x \$HOME/.local/bin/uv"; then
+    echo "uv not found, installing..."
+    docker exec "${CONTAINER_NAME}" bash -c "curl -LsSf https://astral.sh/uv/install.sh | sh" >/dev/null 2>&1
+    echo "uv installed successfully"
 fi
 
+echo "Using uv run for automatic dependency management..."
+# Ensure uv is in PATH (handles ~/.local/bin installation)
+docker exec "${CONTAINER_NAME}" bash -c "export PATH=\"\$HOME/.local/bin:\$PATH\" && uv run --no-project /tmp/analyze_build_trace.py \
+    ${TRACE_FILE} \
+    /workspace/${OUTPUT_FILE} \
+    ${TARGET} \
+    ${GRANULARITY} \
+    ${BUILD_TIME} \
+    /tmp/ck_build_analysis_templates"
+
 # Copy report back to host
 docker cp "${CONTAINER_NAME}:/workspace/${OUTPUT_FILE}" "${PROJECT_ROOT}/${OUTPUT_FILE}"
 

.claude/skills/ck-build-analysis.md

@@ -126,29 +126,18 @@ The analysis script (`analyze_build_trace.py`) is PEP 723 compliant with inline
 # ///
 ```
 
-**The skill automatically uses `uv run` if available**, which provides:
+**The skill automatically installs and uses `uv`**, which provides:
 - ✅ Zero-configuration dependency management
 - ✅ Automatic installation of jinja2 from PEP 723 metadata
 - ✅ Isolated dependency environment (no system pollution)
 - ✅ Fast caching for subsequent runs
 
-### Installation Options
+**No manual setup required!** The first time you run the skill, it will:
+1. Detect if `uv` is installed in the container
+2. If not, automatically install it (takes ~5 seconds)
+3. Use `uv run` to execute the analysis with auto-managed dependencies
 
-**Option 1: Install uv (Recommended)**
-```bash
-# Install uv in the Docker container (one-time setup)
-docker exec ck_<container_name> bash -c "curl -LsSf https://astral.sh/uv/install.sh | sh"
-```
-
-After installing `uv`, the skill will automatically use it for dependency management.
-
-**Option 2: Use system python3 + jinja2**
-```bash
-# If uv is not available, install jinja2 manually
-docker exec ck_<container_name> apt-get install -y python3-jinja2
-```
-
-The skill automatically detects which method is available and uses the appropriate one.
+On subsequent runs, `uv` will already be available and dependencies will be cached.
 
 ### Components
 
@@ -161,12 +150,9 @@ The skill automatically detects which method is available and uses the appropria
 The Python script can also be run independently:
 
 ```bash
-# With uv (recommended - auto-installs dependencies)
+# With uv (recommended - auto-installs dependencies from PEP 723 metadata)
 uv run .claude/skills/analyze_build_trace.py trace.json report.md target 100 22 templates/
 
-# With pipx (alternative - auto-installs dependencies)
+# With pipx (alternative - also auto-installs dependencies)
 pipx run .claude/skills/analyze_build_trace.py trace.json report.md target 100 22 templates/
-
-# With python3 (requires jinja2 pre-installed)
-python3 .claude/skills/analyze_build_trace.py trace.json report.md target 100 22 templates/
 ```