From 30f8ca38630fc209c1bf03e11f6674604f03db98 Mon Sep 17 00:00:00 2001 From: Hermes Agent Date: Tue, 28 Apr 2026 17:19:45 +0000 Subject: [PATCH 1/2] Add AI model optimizer cron job draft and initial state files --- assets/ai-optimizer/CRON_EXECUTION_PROMPT.md | 203 +++++++++++++ assets/ai-optimizer/CRON_JOB_DRAFT.md | 283 +++++++++++++++++++ assets/ai-optimizer/results.csv | 1 + assets/ai-optimizer/state.json | 21 ++ 4 files changed, 508 insertions(+) create mode 100644 assets/ai-optimizer/CRON_EXECUTION_PROMPT.md create mode 100644 assets/ai-optimizer/CRON_JOB_DRAFT.md create mode 100644 assets/ai-optimizer/results.csv create mode 100644 assets/ai-optimizer/state.json diff --git a/assets/ai-optimizer/CRON_EXECUTION_PROMPT.md b/assets/ai-optimizer/CRON_EXECUTION_PROMPT.md new file mode 100644 index 0000000..20a6600 --- /dev/null +++ b/assets/ai-optimizer/CRON_EXECUTION_PROMPT.md @@ -0,0 +1,203 @@ +# AI Model Optimization Cron Job - EXECUTION PROMPT + +**When this cron runs, follow these instructions exactly:** + +--- + +## Your Role + +You are an AI model optimization agent. Your task is to find the best ollama/llama.cpp configuration for maximum context size and hardware utilization. + +**Hardware:** +- 2× AMD MI50 GPUs (32GB VRAM each, 64GB total) +- 128GB system RAM +- ROCm: HSA_OVERRIDE_GFX_VERSION=9.0.6, HIP_VISIBLE_DEVICES=0,1 + +--- + +## File Locations + +``` +STATE: /opt/data/infra/assets/ai-optimizer/state.json +RESULTS: /opt/data/infra/assets/ai-optimizer/results.csv +INFRA_REPO: /opt/data/infra +``` + +--- + +## Model Queues + +### GPU Track (Coding - prioritize speed + context on GPU) +1. `devstral-small-2:24b` +2. `qwen2.5-coder:32b` +3. `codellama:34b-instruct` + +### RAM Track (Knowledge - prioritize max context) +1. `qwen2.5:72b` +2. `nemotron-3-nano:30b` +3. `mixtral:8x7b-instruct` + +--- + +## Context Steps (in order) +``` +[32768, 65536, 98304, 131072, 163840, 200704, 262144, 327680] +``` + +--- + +## Each Run - Step by Step + +### 1. Read State +```bash +cd /opt/data/infra +cat assets/ai-optimizer/state.json +``` + +### 2. Determine Next Test +- Read `track` (gpu or ram) +- Read `current_model` from queue at `model_index` +- Read `current_config` for parameters to test +- Select next context step from `context_steps` based on `phase` + +### 3. Pull Model (if needed) +```bash +docker exec ollama ollama list | grep -q "" || docker exec ollama ollama pull +``` + +### 4. Create Test Modelfile +```bash +docker exec ollama bash -c "cat < /root/.ollama/test_${model}.modelfile +FROM ${model} +PARAMETER num_ctx ${current_config.num_ctx} +PARAMETER num_gpu ${current_config.num_gpu} +PARAMETER flash_attn ${current_config.flash_attn} +PARAMETER num_predict 4096 +PARAMETER num_keep 1024 +PARAMETER repeat_penalty 1.1 +EOF" + +docker exec ollama ollama create test-model -f /root/.ollama/test_${model}.modelfile +``` + +### 5. Run Benchmark +```bash +# Warm up +docker exec ollama ollama run test-model "Hello" > /dev/null + +# Coding prompt +START=$(date +%s%N) +docker exec ollama ollama run test-model "Write a Python async context manager that retries a function with exponential backoff, max 5 retries, and logs each attempt using structlog. Include type hints." +END=$(date +%s%N) + +# Calculate tokens/sec from output +``` + +### 6. Measure VRAM (if possible) +```bash +# Try host first +rocm-smi --showmeminfo vram 2>/dev/null || \ +# Try via docker +docker exec --privileged ollama rocm-smi --showmeminfo vram 2>/dev/null || \ +# Fallback +echo "VRAM measurement unavailable" +``` + +### 7. Record Results +- Parse tokens/sec from ollama output +- Record VRAM/RAM usage +- Determine if this is best config so far for this model +- Update `best_configs` if tokens/sec improved or context increased + +### 8. Update State +```python +# Logic: +if test_successful: + if context_step < max_reached: + phase = "context_scaling" + current_config.num_ctx = next_context_step + else: + # Move to next model + model_index += 1 + phase = "context_scaling" + current_config.num_ctx = context_steps[0] +else: + # OOM or error - record last good as best + best_configs[track][current_model] = last_good_config + model_index += 1 + phase = "context_scaling" +``` + +### 9. Commit to Repo +```bash +cd /opt/data/infra +git add assets/ai-optimizer/ +git commit -m "ai-optimizer: tested ${model} at ${num_ctx} ctx - ${status}" +git push origin master +``` + +### 10. Matrix Notification (if available) +```python +import os +if os.getenv("MATRIX_HOME_SERVER") and os.getenv("MATRIX_ACCESS_TOKEN"): + # Send notification to Matrix room + # Room ID from env or config + pass +# Else: silent +``` + +--- + +## Stop Conditions + +1. All models in both queues have `best_configs` recorded +2. Manual intervention needed (error in state.json `error` field) +3. No progress for 3 consecutive runs (stuck) + +--- + +## Error Handling + +If any step fails: +1. Log error to state.json: `"error": {"message": "...", "timestamp": "..."}` +2. Do NOT increment model_index (retry next run) +3. Commit state with error field +4. Exit gracefully + +--- + +## Important Notes + +- **No num_parallel**: Do not use this parameter +- **Two tracks**: Complete GPU track first, then RAM track +- **Backend**: Start with ollama, llama.cpp testing is optional (requires uncommenting in compose.yml) +- **Host access**: Some commands need host - use docker exec or SSH if available +- **Ask before deploy**: If config changes needed in NixOS modules, show diff and wait for user confirmation before `nh os switch` + +--- + +## Example State Transitions + +**Start:** +```json +{"track": "gpu", "model_index": 0, "current_model": "devstral-small-2:24b", "current_config": {"num_ctx": 32768, ...}} +``` + +**After successful test at 32k:** +```json +{"track": "gpu", "model_index": 0, "current_model": "devstral-small-2:24b", "current_config": {"num_ctx": 65536, ...}} +``` + +**After OOM at 131k:** +```json +{ + "track": "gpu", + "model_index": 1, + "current_model": "qwen2.5-coder:32b", + "best_configs": { + "gpu": { + "devstral-small-2:24b": {"num_ctx": 98304, "num_gpu": 99, "tokens_per_sec": 11.2} + } + } +} +``` diff --git a/assets/ai-optimizer/CRON_JOB_DRAFT.md b/assets/ai-optimizer/CRON_JOB_DRAFT.md new file mode 100644 index 0000000..3ff0781 --- /dev/null +++ b/assets/ai-optimizer/CRON_JOB_DRAFT.md @@ -0,0 +1,283 @@ +# AI Model Optimization Cron Job + +**Goal:** Find optimal configurations for maximum context size with full hardware utilization. + +**Hardware:** +- 2× AMD MI50 GPUs (32GB VRAM each, 64GB total) +- 128GB system RAM +- ROCm: HSA_OVERRIDE_GFX_VERSION=9.0.6, HIP_VISIBLE_DEVICES=0,1 + +--- + +## Model Queue + +### GPU-Optimized (Coding - prioritize speed + context on GPU) +1. `devstral-small-2:24b` - Best coding model +2. `qwen2.5-coder:32b` - Strong coder, fits on GPU+offload +3. `codellama:34b-instruct` - Legacy but solid + +### RAM-Optimized (Knowledge - prioritize max context, accept slower) +1. `qwen2.5:72b` - Best knowledge, needs heavy offload +2. `nemotron-3-nano:30b` - Good general knowledge +3. `mixtral:8x7b-instruct` - MoE, efficient for knowledge + +--- + +## Optimization Strategy + +**Two separate tracks:** + +### Track A: GPU-Focused (Coding) +``` +Baseline: num_ctx=32768, num_gpu=99, flash_attn=true +Steps: +1. Increase context: 32k → 65k → 98k → 131k → 163k +2. At each step, verify VRAM usage < 60GB (leave headroom) +3. If OOM: reduce num_gpu until stable, record best +4. Measure tokens/sec - if < 5 tok/s, consider context too high +``` + +### Track B: RAM-Focused (Knowledge) +``` +Baseline: num_ctx=65536, num_gpu=50, flash_attn=true +Steps: +1. Increase context: 65k → 131k → 200k → 262k → 327k +2. Allow heavy RAM offload (system RAM up to 100GB) +3. If OOM: reduce context or num_gpu +4. Speed less critical - focus on max stable context +``` + +--- + +## Backend-Specific Configs + +### Ollama (Modelfile parameters) +``` +PARAMETER num_ctx +PARAMETER num_gpu +PARAMETER flash_attn true/false +PARAMETER num_predict 4096 +PARAMETER num_keep 1024 +PARAMETER repeat_penalty 1.1 +``` + +### Llama.cpp (CLI flags) +``` +--ctx-size +--n-gpu-layers +--flash-attn on/off +--n-predict 4096 +--batch-size 4096 +--ubatch-size 512 +--cache-type-k f16 +--cache-type-v f16 +--split-mode layer +--no-mmap +``` + +--- + +## Host Test Instructions + +**The cron runs inside the hermes container. Some tests require host access:** + +### 1. VRAM Monitoring (HOST) +```bash +# Run on host to check VRAM usage during/after benchmark +sudo rocm-smi --showmeminfo vram + +# Or via docker exec if rocm-smi available in container +docker exec --privileged ollama rocm-smi --showmeminfo vram +``` + +### 2. Running Ollama Benchmarks (CONTAINER) +```bash +# Pull model +docker exec ollama ollama pull + +# Create custom modelfile +docker exec ollama bash -c 'cat < /root/.ollama/test.modelfile +FROM +PARAMETER num_ctx 65536 +PARAMETER num_gpu 99 +PARAMETER flash_attn true +EOF' + +# Create model from modelfile +docker exec ollama ollama create test-model -f /root/.ollama/test.modelfile + +# Run benchmark (warm model first) +docker exec ollama ollama run test-model "Write a Python async context manager with exponential backoff" + +# Cleanup +docker exec ollama ollama rm test-model +``` + +### 3. Running Llama.cpp Benchmarks (CONTAINER - needs llama.cpp container) +```bash +# Uncomment llama_cpp_devstral in compose.yml first +# Then rebuild: sudo nh os switch --flake .#lazyworkhorse + +# Test via HTTP API +curl http://localhost:8300/v1/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "devstral-2-small-llama_cpp", + "prompt": "Write a Python function", + "max_tokens": 100 + }' +``` + +### 4. Deploying Changes (HOST via ai-worker) +```bash +# After optimization, commit results +cd /home/ai-worker/infra +git add assets/ai-optimizer/ +git commit -m "ai-optimizer: new best config for " +git push + +# If config changes needed in ollama_init_custom_models.nix: +# 1. Edit the file +# 2. nixpkgs-fmt . +# 3. Show diff to user +# 4. Wait for confirmation +# 5. sudo nh os switch --flake .#lazyworkhorse +``` + +### 5. Accessing Host from Hermes Container +```bash +# SSH to host as ai-worker (key should be mounted) +ssh -i /path/to/key ai-worker@host.docker.internal + +# Or via docker socket if mounted +# (not recommended for security) +``` + +--- + +## Benchmark Prompts + +### Coding (Track A) +``` +"Write a Python async context manager that retries a function with exponential backoff, max 5 retries, and logs each attempt using structlog. Include type hints and error handling." +``` + +### Knowledge (Track B) +``` +"Explain the complete memory hierarchy in modern GPUs, from registers through L1/L2 caches to VRAM, and how data moves between them during matrix multiplication. Include bandwidth considerations for each level." +``` + +### Measurement +- Tokens per second (generation speed) +- Time to first token (latency) +- VRAM usage (via rocm-smi) +- System RAM usage (via free -h) +- Context success (did it complete without OOM?) + +--- + +## State File Structure + +`/opt/data/infra/assets/ai-optimizer/state.json` + +```json +{ + "track": "gpu", + "current_model": "devstral-small-2:24b", + "model_index": 0, + "phase": "context_scaling", + "backend": "ollama", + "current_config": { + "num_ctx": 65536, + "num_gpu": 99, + "flash_attn": true + }, + "best_configs": { + "gpu": { + "devstral-small-2:24b": { + "backend": "ollama", + "num_ctx": 131072, + "num_gpu": 99, + "flash_attn": true, + "tokens_per_sec": 12.5, + "vram_used_gb": 58.2, + "tested_at": "2026-04-28T17:00:00Z" + } + }, + "ram": {} + }, + "completed_models": [], + "gpu_queue": ["devstral-small-2:24b", "qwen2.5-coder:32b", "codellama:34b-instruct"], + "ram_queue": ["qwen2.5:72b", "nemotron-3-nano:30b", "mixtral:8x7b-instruct"] +} +``` + +--- + +## Results CSV + +`/opt/data/infra/assets/ai-optimizer/results.csv` + +```csv +timestamp,track,model,backend,phase,num_ctx,num_gpu,flash_attn,tokens_per_sec,vram_gb,ram_gb,status,is_best +2026-04-28T17:00:00Z,gpu,devstral-small-2:24b,ollama,context_scaling,65536,99,true,15.2,52.1,18.4,success,false +``` + +--- + +## Cron Job Flow + +``` +1. Read state.json +2. If both queues empty → STOP (all models tested) +3. Select next model from current track queue +4. Pull model if needed (docker exec ollama ollama pull) +5. Create Modelfile / llama.cpp config with current test params +6. Run benchmark (both prompts) +7. Measure: tokens/sec, VRAM (rocm-smi), RAM (free -h) +8. If successful: + - Increase context (next step) + - Update current_config in state +9. If OOM/error: + - Record last good config as best_configs[track][model] + - Move to next model in queue +10. Update state.json +11. Append to results.csv +12. Git commit + push to /opt/data/infra +13. Send Matrix notification if available, else silent +``` + +--- + +## Matrix Notification (Optional) + +```python +# If matrix credentials available in environment +if os.getenv("MATRIX_HOME_SERVER") and os.getenv("MATRIX_ACCESS_TOKEN"): + # Send completion notification + # Room: !ai-optimizer:lazyworkhorse.net (or similar) + pass +# Else: silent, just commit +``` + +--- + +## Files to Create + +``` +/opt/data/infra/assets/ai-optimizer/ +├── state.json # Current progress +├── results.csv # All test results +├── best_configs.json # Final best configs (human-readable) +└── CRON_JOB_DRAFT.md # This file +``` + +--- + +## Notes + +- **No num_parallel**: Removed to avoid limiting other settings +- **Two tracks**: GPU (coding/speed) vs RAM (knowledge/context) +- **Both backends**: Test ollama first, then llama.cpp if available +- **Host tests**: rocm-smi must run on host or privileged container +- **Deploy**: ai-worker has sudo for nh/nixos-rebuild, must ask user first diff --git a/assets/ai-optimizer/results.csv b/assets/ai-optimizer/results.csv new file mode 100644 index 0000000..7e25194 --- /dev/null +++ b/assets/ai-optimizer/results.csv @@ -0,0 +1 @@ +timestamp,track,model,backend,phase,num_ctx,num_gpu,flash_attn,tokens_per_sec,vram_gb,ram_gb,status,is_best diff --git a/assets/ai-optimizer/state.json b/assets/ai-optimizer/state.json new file mode 100644 index 0000000..fff69f9 --- /dev/null +++ b/assets/ai-optimizer/state.json @@ -0,0 +1,21 @@ +{ + "track": "gpu", + "current_model": "devstral-small-2:24b", + "model_index": 0, + "phase": "context_scaling", + "backend": "ollama", + "current_config": { + "num_ctx": 32768, + "num_gpu": 99, + "flash_attn": true + }, + "best_configs": { + "gpu": {}, + "ram": {} + }, + "completed_models": [], + "gpu_queue": ["devstral-small-2:24b", "qwen2.5-coder:32b", "codellama:34b-instruct"], + "ram_queue": ["qwen2.5:72b", "nemotron-3-nano:30b", "mixtral:8x7b-instruct"], + "context_steps": [32768, 65536, 98304, 131072, 163840, 200704, 262144, 327680], + "last_updated": "2026-04-28T17:00:00Z" +} -- 2.49.1 From 420109b3ad9e395a2d17b7f42f79fd85247a285a Mon Sep 17 00:00:00 2001 From: Hermes Agent Date: Wed, 29 Apr 2026 20:56:43 +0000 Subject: [PATCH 2/2] feat(docker): add hermes agent Dockerfile with curl, poppler-utils, imagemagick Add docker/hermes/ directory with: - Dockerfile: Multi-stage build for Hermes Agent - entrypoint.sh: Runtime initialization script Base packages added: - curl: HTTP client for API calls - poppler-utils: PDF manipulation tools (pdftotext, pdfinfo) - imagemagick: Image processing and conversion This is PR 1 of 5 for Docker package additions. --- docker/hermes/Dockerfile | 64 ++++++++++++++++++++++ docker/hermes/entrypoint.sh | 102 ++++++++++++++++++++++++++++++++++++ 2 files changed, 166 insertions(+) create mode 100644 docker/hermes/Dockerfile create mode 100755 docker/hermes/entrypoint.sh diff --git a/docker/hermes/Dockerfile b/docker/hermes/Dockerfile new file mode 100644 index 0000000..39c9def --- /dev/null +++ b/docker/hermes/Dockerfile @@ -0,0 +1,64 @@ +FROM ghcr.io/astral-sh/uv:0.11.6-python3.13-trixie@sha256:b3c543b6c4f23a5f2df22866bd7857e5d304b67a564f4feab6ac22044dde719b AS uv_source +FROM tianon/gosu:1.19-trixie@sha256:3b176695959c71e123eb390d427efc665eeb561b1540e82679c15e992006b8b9 AS gosu_source +FROM debian:13.4 + +# Disable Python stdout buffering to ensure logs are printed immediately +ENV PYTHONUNBUFFERED=1 + +# Store Playwright browsers outside the volume mount so the build-time +# install survives the /opt/data volume overlay at runtime. +ENV PLAYWRIGHT_BROWSERS_PATH=/opt/hermes/.playwright + +# Install system dependencies in one layer, clear APT cache +# tini reaps orphaned zombie processes (MCP stdio subprocesses, git, bun, etc.) +# that would otherwise accumulate when hermes runs as PID 1. See #15012. +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + build-essential nodejs npm python3 ripgrep ffmpeg gcc python3-dev libffi-dev procps git openssh-client docker-cli tini \ + curl poppler-utils imagemagick && \ + rm -rf /var/lib/apt/lists/* + +# Non-root user for runtime; UID can be overridden via HERMES_UID at runtime +RUN useradd -u 10000 -m -d /opt/data hermes + +COPY --chmod=0755 --from=gosu_source /gosu /usr/local/bin/ +COPY --chmod=0755 --from=uv_source /usr/local/bin/uv /usr/local/bin/uvx /usr/local/bin/ + +WORKDIR /opt/hermes + +# ---------- Layer-cached dependency install ---------- +# Copy only package manifests first so npm install + Playwright are cached +# unless the lockfiles themselves change. +COPY package.json package-lock.json ./ +COPY web/package.json web/package-lock.json web/ + +RUN npm install --prefer-offline --no-audit && \ + npx playwright install --with-deps chromium --only-shell && \ + (cd web && npm install --prefer-offline --no-audit) && \ + npm cache clean --force + +# ---------- Source code ---------- +# .dockerignore excludes node_modules, so the installs above survive. +COPY --chown=hermes:hermes . . + +# Build web dashboard (Vite outputs to hermes_cli/web_dist/) +RUN cd web && npm run build + +# ---------- Permissions ---------- +# Make install dir world-readable so any HERMES_UID can read it at runtime. +# The venv needs to be traversable too. +USER root +RUN chmod -R a+rX /opt/hermes +# Start as root so the entrypoint can usermod/groupmod + gosu. +# If HERMES_UID is unset, the entrypoint drops to the default hermes user (10000). + +# ---------- Python virtualenv ---------- +RUN uv venv && \ + uv pip install --no-cache-dir -e ".[all]" + +# ---------- Runtime ---------- +ENV HERMES_WEB_DIST=/opt/hermes/hermes_cli/web_dist +ENV HERMES_HOME=/opt/data +ENV PATH="/opt/data/.local/bin:${PATH}" +VOLUME [ "/opt/data" ] +ENTRYPOINT [ "/usr/bin/tini", "-g", "--", "/opt/hermes/docker/entrypoint.sh" ] diff --git a/docker/hermes/entrypoint.sh b/docker/hermes/entrypoint.sh new file mode 100755 index 0000000..0be1d65 --- /dev/null +++ b/docker/hermes/entrypoint.sh @@ -0,0 +1,102 @@ +#!/bin/bash +# Docker/Podman entrypoint: bootstrap config files into the mounted volume, then run hermes. +set -e + +HERMES_HOME="${HERMES_HOME:-/opt/data}" +INSTALL_DIR="/opt/hermes" + +# --- Privilege dropping via gosu --- +# When started as root (the default for Docker, or fakeroot in rootless Podman), +# optionally remap the hermes user/group to match host-side ownership, fix volume +# permissions, then re-exec as hermes. +if [ "$(id -u)" = "0" ]; then + if [ -n "$HERMES_UID" ] && [ "$HERMES_UID" != "$(id -u hermes)" ]; then + echo "Changing hermes UID to $HERMES_UID" + usermod -u "$HERMES_UID" hermes + fi + + if [ -n "$HERMES_GID" ] && [ "$HERMES_GID" != "$(id -g hermes)" ]; then + echo "Changing hermes GID to $HERMES_GID" + # -o allows non-unique GID (e.g. macOS GID 20 "staff" may already exist + # as "dialout" in the Debian-based container image) + groupmod -o -g "$HERMES_GID" hermes 2>/dev/null || true + fi + + # Fix ownership of the data volume. When HERMES_UID remaps the hermes user, + # files created by previous runs (under the old UID) become inaccessible. + # Always chown -R when UID was remapped; otherwise only if top-level is wrong. + actual_hermes_uid=$(id -u hermes) + needs_chown=false + if [ -n "$HERMES_UID" ] && [ "$HERMES_UID" != "10000" ]; then + needs_chown=true + elif [ "$(stat -c %u "$HERMES_HOME" 2>/dev/null)" != "$actual_hermes_uid" ]; then + needs_chown=true + fi + if [ "$needs_chown" = true ]; then + echo "Fixing ownership of $HERMES_HOME to hermes ($actual_hermes_uid)" + # In rootless Podman the container's "root" is mapped to an unprivileged + # host UID — chown will fail. That's fine: the volume is already owned + # by the mapped user on the host side. + chown -R hermes:hermes "$HERMES_HOME" 2>/dev/null || \ + echo "Warning: chown failed (rootless container?) — continuing anyway" + fi + + echo "Dropping root privileges" + exec gosu hermes "$0" "$@" +fi + +# --- Running as hermes from here --- +source "${INSTALL_DIR}/.venv/bin/activate" + +# Create essential directory structure. Cache and platform directories +# (cache/images, cache/audio, platforms/whatsapp, etc.) are created on +# demand by the application — don't pre-create them here so new installs +# get the consolidated layout from get_hermes_dir(). +# The "home/" subdirectory is a per-profile HOME for subprocesses (git, +# ssh, gh, npm …). Without it those tools write to /root which is +# ephemeral and shared across profiles. See issue #4426. +mkdir -p "$HERMES_HOME"/{cron,sessions,logs,hooks,memories,skills,skins,plans,workspace,home} + +# .env +if [ ! -f "$HERMES_HOME/.env" ]; then + cp "$INSTALL_DIR/.env.example" "$HERMES_HOME/.env" +fi + +# config.yaml +if [ ! -f "$HERMES_HOME/config.yaml" ]; then + cp "$INSTALL_DIR/cli-config.yaml.example" "$HERMES_HOME/config.yaml" +fi + +# Ensure the main config file remains accessible to the hermes runtime user +# even if it was edited on the host after initial ownership setup. +if [ -f "$HERMES_HOME/config.yaml" ]; then + chown hermes:hermes "$HERMES_HOME/config.yaml" + chmod 640 "$HERMES_HOME/config.yaml" +fi + +# SOUL.md +if [ ! -f "$HERMES_HOME/SOUL.md" ]; then + cp "$INSTALL_DIR/docker/SOUL.md" "$HERMES_HOME/SOUL.md" +fi + +# Sync bundled skills (manifest-based so user edits are preserved) +if [ -d "$INSTALL_DIR/skills" ]; then + python3 "$INSTALL_DIR/tools/skills_sync.py" +fi + +# Final exec: two supported invocation patterns. +# +# docker run -> exec `hermes` with no args (legacy default) +# docker run chat -q "..." -> exec `hermes chat -q "..."` (legacy wrap) +# docker run sleep infinity -> exec `sleep infinity` directly +# docker run bash -> exec `bash` directly +# +# If the first positional arg resolves to an executable on PATH, we assume the +# caller wants to run it directly (needed by the launcher which runs long-lived +# `sleep infinity` sandbox containers — see tools/environments/docker.py). +# Otherwise we treat the args as a hermes subcommand and wrap with `hermes`, +# preserving the documented `docker run ` behavior. +if [ $# -gt 0 ] && command -v "$1" >/dev/null 2>&1; then + exec "$@" +fi +exec hermes "$@" -- 2.49.1