docs(install): recommend uv as the package manager

End-user installs now lead with `uv tool install mempalace`, with
`pip install mempalace` kept as a fallback. Dev/contributor docs lead
with `uv sync --extra dev` and `uv run` for tests/benchmarks/lint, with
the equivalent pip recipe kept inline. The shipped `/mempalace:init`
skill instructions (mempalace/instructions/init.md) try `uv tool install`
first when uv is on PATH, then fall back through the pip variants.

Adds a .python-version pin at 3.12 because the lockfile's
onnxruntime==1.24.3 only ships wheels for Python >=3.11; without the
pin, `uv sync` on a host where uv prefers 3.10 fails with no source
distribution available, which would make the documented command a
footgun. pyproject's `requires-python = ">=3.9"` is unchanged — pip
users on 3.9/3.10 are unaffected.

Files updated: README.md, CONTRIBUTING.md, CLAUDE.md, the gemini-cli
guide and example, the .claude-plugin / .codex-plugin READMEs, the
mempalace SKILL, the openclaw SKILL, tools/save.md, the three
benchmarks docs, and the corresponding website mirrors.

benchmarks/BENCHMARKS.md

@@ -344,7 +344,7 @@ The palace classifies each question into one of 5 halls. Pass 1 searches only wi
 ```bash
 git clone https://github.com/MemPalace/mempalace.git
 cd mempalace
-pip install -e ".[dev]"
+uv sync --extra dev   # or: pip install -e ".[dev]"
 mkdir -p /tmp/longmemeval-data
 curl -fsSL -o /tmp/longmemeval-data/longmemeval_s_cleaned.json \
   https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json
@@ -724,8 +724,8 @@ python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_clean
 The question: how much of the 96.6% → 99.4% improvement is the heuristics, and how much would come from just using a better embedding model?
 
 ```bash
-pip install fastembed
-python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_cleaned.json \
+uv pip install fastembed   # or: pip install fastembed
+uv run python benchmarks/longmemeval_bench.py /tmp/longmemeval-data/longmemeval_s_cleaned.json \
   --mode raw --embed-model bge-large
 ```
 

benchmarks/HYBRID_MODE.md

@@ -198,7 +198,7 @@ python benchmarks/longmemeval_bench.py data/longmemeval_s_cleaned.json --mode hy
 # Setup
 git clone https://github.com/MemPalace/mempalace.git
 cd mempalace
-pip install -e ".[dev]"
+uv sync --extra dev   # or: pip install -e ".[dev]"
 
 # Download data
 mkdir -p /tmp/longmemeval-data

benchmarks/README.md

@@ -7,7 +7,7 @@ Run the exact same benchmarks we report. Clone, install, run.
 ```bash
 git clone https://github.com/MemPalace/mempalace.git
 cd mempalace
-pip install -e ".[dev]"
+uv sync --extra dev   # or: pip install -e ".[dev]"
 ```
 
 ## Benchmark 1: LongMemEval (500 questions)