From 55ef425d0c3967022cb54093112e638c5c3f9e01 Mon Sep 17 00:00:00 2001 From: teknium1 <127238744+teknium1@users.noreply.github.com> Date: Thu, 23 Jul 2026 21:21:58 -0700 Subject: [PATCH] fix(skills): sync bundled + misc CLI skills to current upstream Nine bundled and optional skills had stale flags, install URLs, packages, and paths. Verified each against upstream and corrected: - vllm: removed bogus --enable-metrics/--metrics-port (metrics at /metrics on API port); --speculative-model -> --speculative-config; canonical HF model IDs - lm-evaluation-harness: --tasks list -> lm-eval ls tasks; --allow_code_execution -> --confirm_run_unsafe_code - weights-and-biases: wandb.keras import removed -> wandb.integration.keras (WandbMetricsLogger); log_uniform -> log_uniform_values for raw values - huggingface-hub: upload-large-folder now deprecated; hf papers list -> ls - openhue: Linux install 404 -> openhue_Linux_x86_64.tar.gz tarball (release repo openhue/openhue-cli, v0.24) - apple-notes: memo notes -a is a bare flag, no positional title - excalidraw: upload.py path skills/diagramming/... -> skills/creative/... - searxng-search: removed Method 3 (searxng-data pip package is a PyPI 404) - sketch: noted get-shit-done upstream is archived/unmaintained --- .../research/searxng-search/SKILL.md | 19 +---------- skills/apple/apple-notes/SKILL.md | 10 ++++-- skills/creative/excalidraw/SKILL.md | 4 +-- skills/creative/sketch/SKILL.md | 8 +++-- .../evaluating-llms-harness/SKILL.md | 16 ++++----- .../evaluation/weights-and-biases/SKILL.md | 16 +++++---- skills/mlops/huggingface-hub/SKILL.md | 8 ++--- .../inference/serving-llms-vllm/SKILL.md | 33 ++++++++++--------- skills/smart-home/openhue/SKILL.md | 9 +++-- 9 files changed, 60 insertions(+), 63 deletions(-) diff --git a/optional-skills/research/searxng-search/SKILL.md b/optional-skills/research/searxng-search/SKILL.md index b14840682fe..acf6973be49 100644 --- a/optional-skills/research/searxng-search/SKILL.md +++ b/optional-skills/research/searxng-search/SKILL.md @@ -1,7 +1,7 @@ --- name: searxng-search description: Free keyless meta-search aggregating 70+ engines. -version: 1.0.0 +version: 1.0.1 author: hermes-agent license: MIT platforms: [linux, macos] @@ -124,23 +124,6 @@ for r in data.get("results", []): print() ``` -## Method 3: searxng-data Python Package - -For more structured access, install the `searxng-data` package: - -```bash -pip install searxng-data -``` - -```python -from searxng_data import engines - -# List available engines -print(engines.list_engines()) -``` - -Note: This package only provides engine metadata, not the search API itself. - ## Self-Hosting SearXNG To run your own SearXNG instance: diff --git a/skills/apple/apple-notes/SKILL.md b/skills/apple/apple-notes/SKILL.md index 020f0d641df..3fcf4c25c0c 100644 --- a/skills/apple/apple-notes/SKILL.md +++ b/skills/apple/apple-notes/SKILL.md @@ -1,7 +1,7 @@ --- name: apple-notes description: "Manage Apple Notes via memo CLI: create, search, edit." -version: 1.0.0 +version: 1.0.1 author: Hermes Agent license: MIT platforms: [macos] @@ -49,10 +49,14 @@ memo notes -s "query" # Search notes (fuzzy) ### Create Notes ```bash -memo notes -a # Interactive editor -memo notes -a "Note Title" # Quick add with title +memo notes -a # Add a note (opens your $EDITOR) +memo notes -a -f "Folder Name" # Add a note into a specific folder ``` +`-a`/`--add` is a bare flag — it opens your `$EDITOR` to compose the note; it does +not take a title argument. Use `-f/--folder` to target a folder. Set `$EDITOR` +first (e.g. `export EDITOR=vim`). + ### Edit Notes ```bash diff --git a/skills/creative/excalidraw/SKILL.md b/skills/creative/excalidraw/SKILL.md index 0474391a400..898aaa979e0 100644 --- a/skills/creative/excalidraw/SKILL.md +++ b/skills/creative/excalidraw/SKILL.md @@ -1,7 +1,7 @@ --- name: excalidraw description: "Hand-drawn Excalidraw JSON diagrams (arch, flow, seq)." -version: 1.0.0 +version: 1.0.1 author: Hermes Agent license: MIT dependencies: [] @@ -51,7 +51,7 @@ Save to any path, e.g. `~/diagrams/my_diagram.excalidraw`. Run the upload script (located in this skill's `scripts/` directory) via terminal: ```bash -python skills/diagramming/excalidraw/scripts/upload.py ~/diagrams/my_diagram.excalidraw +python skills/creative/excalidraw/scripts/upload.py ~/diagrams/my_diagram.excalidraw ``` This uploads to excalidraw.com (no account needed) and prints a shareable URL. Requires the `cryptography` pip package (`pip install cryptography`). diff --git a/skills/creative/sketch/SKILL.md b/skills/creative/sketch/SKILL.md index 6e49585acd4..e7f072866b9 100644 --- a/skills/creative/sketch/SKILL.md +++ b/skills/creative/sketch/SKILL.md @@ -1,7 +1,7 @@ --- name: sketch description: "Throwaway HTML mockups: 2-3 design variants to compare." -version: 1.0.0 +version: 1.0.1 author: Hermes Agent (adapted from gsd-build/get-shit-done) license: MIT platforms: [linux, macos, windows] @@ -26,7 +26,9 @@ Load this when the user says things like "sketch this screen", "show me what X c ## If the user has the full GSD system installed -If `gsd-sketch` shows up as a sibling skill (installed via `npx get-shit-done-cc --hermes`), prefer **`gsd-sketch`** for the full workflow: persistent `.planning/sketches/` with MANIFEST, frontier mode analysis, consistency audits across past sketches, and integration with the rest of GSD. This skill is the lightweight standalone version — one-off sketching without the state machinery. +If `gsd-sketch` shows up as a sibling skill (installed via `npx get-shit-done-cc --hermes`), you can use **`gsd-sketch`** for the fuller workflow: persistent `.planning/sketches/` with MANIFEST, frontier mode analysis, consistency audits across past sketches, and integration with the rest of GSD. This skill is the lightweight standalone version — one-off sketching without the state machinery. + +> **Note:** The upstream GSD project ([gsd-build/get-shit-done](https://github.com/gsd-build/get-shit-done)) is **archived / no longer maintained** on GitHub. The npm package (`get-shit-done-cc`) still installs, but treat it as an archived community project — this standalone `sketch` skill is the maintained path and needs nothing extra. ## Core method @@ -215,4 +217,4 @@ Repeat for each variant, then present the comparison table. ## Attribution -Adapted from the GSD (Get Shit Done) project's `/gsd-sketch` workflow — MIT © 2025 Lex Christopherson ([gsd-build/get-shit-done](https://github.com/gsd-build/get-shit-done)). The full GSD system ships persistent sketch state, theme/variant pattern references, and consistency-audit workflows; install with `npx get-shit-done-cc --hermes --global`. +Adapted from the GSD (Get Shit Done) project's `/gsd-sketch` workflow — MIT © 2025 Lex Christopherson ([gsd-build/get-shit-done](https://github.com/gsd-build/get-shit-done)). The upstream GSD repo is now **archived/unmaintained** on GitHub; the `get-shit-done-cc` npm package still installs (`npx get-shit-done-cc --hermes --global`) and ships persistent sketch state, theme/variant pattern references, and consistency-audit workflows, but treat it as an archived community project. diff --git a/skills/mlops/evaluation/evaluating-llms-harness/SKILL.md b/skills/mlops/evaluation/evaluating-llms-harness/SKILL.md index 79c59f1e340..306b4f6fe94 100644 --- a/skills/mlops/evaluation/evaluating-llms-harness/SKILL.md +++ b/skills/mlops/evaluation/evaluating-llms-harness/SKILL.md @@ -1,7 +1,7 @@ --- name: evaluating-llms-harness description: "lm-eval-harness: benchmark LLMs (MMLU, GSM8K, etc.)." -version: 1.0.0 +version: 1.0.1 author: Orchestra Research license: MIT dependencies: [lm-eval, transformers, vllm] @@ -38,7 +38,7 @@ lm_eval --model hf \ **View available tasks**: ```bash -lm_eval --tasks list +lm-eval ls tasks ``` ## Common workflows @@ -451,19 +451,19 @@ Verify model and tokenizer match: **Issue: HumanEval not executing code** -Install execution dependencies: -```bash -pip install human-eval -``` +Code-executing tasks (HumanEval, MBPP, etc.) are gated behind an explicit +confirmation flag — you must pass `--confirm_run_unsafe_code` to run them: -Enable code execution: ```bash lm_eval --model hf \ --model_args pretrained=model-name \ --tasks humaneval \ - --allow_code_execution # Required for HumanEval + --confirm_run_unsafe_code # Required to run tasks that execute generated code ``` +Without this flag lm-eval refuses to run the task rather than silently skipping +code execution. + ## Advanced topics **Benchmark descriptions**: See [references/benchmark-guide.md](references/benchmark-guide.md) for detailed description of all 60+ tasks, what they measure, and interpretation. diff --git a/skills/mlops/evaluation/weights-and-biases/SKILL.md b/skills/mlops/evaluation/weights-and-biases/SKILL.md index 6dd17694b12..656cb5de680 100644 --- a/skills/mlops/evaluation/weights-and-biases/SKILL.md +++ b/skills/mlops/evaluation/weights-and-biases/SKILL.md @@ -1,7 +1,7 @@ --- name: weights-and-biases description: "W&B: log ML experiments, sweeps, model registry, dashboards." -version: 1.0.0 +version: 1.0.1 author: Orchestra Research license: MIT dependencies: [wandb] @@ -238,7 +238,7 @@ sweep_config = { }, 'parameters': { 'learning_rate': { - 'distribution': 'log_uniform', + 'distribution': 'log_uniform_values', 'min': 1e-5, 'max': 1e-1 }, @@ -317,7 +317,7 @@ sweep_config = { 'method': 'bayes', 'metric': {'name': 'val/loss', 'goal': 'minimize'}, 'parameters': { - 'lr': {'distribution': 'log_uniform', 'min': 1e-5, 'max': 1e-1} + 'lr': {'distribution': 'log_uniform_values', 'min': 1e-5, 'max': 1e-1} } } ``` @@ -433,17 +433,21 @@ trainer.fit(model, datamodule=dm) ```python import wandb -from wandb.keras import WandbCallback +from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint # Initialize wandb.init(project="keras-demo") -# Add callback +# Add callbacks (the monolithic WandbCallback was removed; +# use the dedicated callbacks from wandb.integration.keras instead) model.fit( x_train, y_train, validation_data=(x_val, y_val), epochs=10, - callbacks=[WandbCallback()] # Auto-logs metrics + callbacks=[ + WandbMetricsLogger(), # Auto-logs metrics + WandbModelCheckpoint("models/model-{epoch}") # Saves checkpoints + ] ) ``` diff --git a/skills/mlops/huggingface-hub/SKILL.md b/skills/mlops/huggingface-hub/SKILL.md index a9ed104b3c0..bb82ac1c040 100644 --- a/skills/mlops/huggingface-hub/SKILL.md +++ b/skills/mlops/huggingface-hub/SKILL.md @@ -1,7 +1,7 @@ --- name: huggingface-hub description: "HuggingFace hf CLI: search/download/upload models, datasets." -version: 1.0.0 +version: 1.0.1 author: Hugging Face license: MIT tags: [huggingface, hf, models, datasets, hub, mlops] @@ -25,8 +25,8 @@ The `hf` command is the modern command-line interface for interacting with the H ### General Operations * `hf download REPO_ID`: Download files from the Hub. -* `hf upload REPO_ID`: Upload files/folders (recommended for single-commit). -* `hf upload-large-folder REPO_ID LOCAL_PATH`: Recommended for resumable uploads of large directories. +* `hf upload REPO_ID`: Upload files/folders (recommended for single-commit; also handles resumable uploads of large directories). +* `hf upload-large-folder REPO_ID LOCAL_PATH`: **[Deprecated]** — use `hf upload` instead. * `hf sync`: Sync files between a local directory and a bucket. * `hf env` / `hf version`: View environment and version details. @@ -50,7 +50,7 @@ The `hf` command is the modern command-line interface for interacting with the H * **Datasets:** `hf datasets list`, `info`, and `parquet` (list parquet URLs). * **SQL Queries:** `hf datasets sql SQL` — Execute raw SQL via DuckDB against dataset parquet URLs. * **Models:** `hf models list` and `info`. -* **Papers:** `hf papers list` — View daily papers. +* **Papers:** `hf papers ls` — View daily papers. ### Discussions & Pull Requests (`hf discussions`) * Manage the lifecycle of Hub contributions: `list`, `create`, `info`, `comment`, `close`, `reopen`, and `rename`. diff --git a/skills/mlops/inference/serving-llms-vllm/SKILL.md b/skills/mlops/inference/serving-llms-vllm/SKILL.md index 2f754e1b0f5..33441394013 100644 --- a/skills/mlops/inference/serving-llms-vllm/SKILL.md +++ b/skills/mlops/inference/serving-llms-vllm/SKILL.md @@ -1,7 +1,7 @@ --- name: serving-llms-vllm description: "vLLM: high-throughput LLM serving, OpenAI API, quantization." -version: 1.0.0 +version: 1.0.1 author: Orchestra Research license: MIT dependencies: [vllm, torch, transformers] @@ -31,7 +31,7 @@ pip install vllm ```python from vllm import LLM, SamplingParams -llm = LLM(model="meta-llama/Llama-3-8B-Instruct") +llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct") sampling = SamplingParams(temperature=0.7, max_tokens=256) outputs = llm.generate(["Explain quantum computing"], sampling) @@ -40,14 +40,14 @@ print(outputs[0].outputs[0].text) **OpenAI-compatible server**: ```bash -vllm serve meta-llama/Llama-3-8B-Instruct +vllm serve meta-llama/Meta-Llama-3-8B-Instruct # Query with OpenAI SDK python -c " from openai import OpenAI client = OpenAI(base_url='http://localhost:8000/v1', api_key='EMPTY') print(client.chat.completions.create( - model='meta-llama/Llama-3-8B-Instruct', + model='meta-llama/Meta-Llama-3-8B-Instruct', messages=[{'role': 'user', 'content': 'Hello!'}] ).choices[0].message.content) " @@ -74,24 +74,23 @@ Choose configuration based on your model size: ```bash # For 7B-13B models on single GPU -vllm serve meta-llama/Llama-3-8B-Instruct \ +vllm serve meta-llama/Meta-Llama-3-8B-Instruct \ --gpu-memory-utilization 0.9 \ --max-model-len 8192 \ --port 8000 # For 30B-70B models with tensor parallelism -vllm serve meta-llama/Llama-2-70b-hf \ +vllm serve meta-llama/Meta-Llama-3-70B-Instruct \ --tensor-parallel-size 4 \ --gpu-memory-utilization 0.9 \ --quantization awq \ --port 8000 -# For production with caching and metrics -vllm serve meta-llama/Llama-3-8B-Instruct \ +# For production with caching (Prometheus metrics are exposed +# automatically at /metrics on the API port) +vllm serve meta-llama/Meta-Llama-3-8B-Instruct \ --gpu-memory-utilization 0.9 \ --enable-prefix-caching \ - --enable-metrics \ - --metrics-port 9090 \ --port 8000 \ --host 0.0.0.0 ``` @@ -112,10 +111,10 @@ Verify TTFT (time to first token) < 500ms and throughput > 100 req/sec. **Step 3: Enable monitoring** -vLLM exposes Prometheus metrics on port 9090: +vLLM exposes Prometheus metrics at `/metrics` on the API port (default 8000): ```bash -curl http://localhost:9090/metrics | grep vllm +curl http://localhost:8000/metrics | grep vllm ``` Key metrics to monitor: @@ -131,7 +130,7 @@ Use Docker for consistent deployment: # Run vLLM in Docker docker run --gpus all -p 8000:8000 \ vllm/vllm-openai:latest \ - --model meta-llama/Llama-3-8B-Instruct \ + --model meta-llama/Meta-Llama-3-8B-Instruct \ --gpu-memory-utilization 0.9 \ --enable-prefix-caching ``` @@ -175,7 +174,7 @@ print(f"Loaded {len(prompts)} prompts") from vllm import LLM, SamplingParams llm = LLM( - model="meta-llama/Llama-3-8B-Instruct", + model="meta-llama/Meta-Llama-3-8B-Instruct", tensor_parallel_size=2, # Use 2 GPUs gpu_memory_utilization=0.9, max_model_len=4096 @@ -338,9 +337,11 @@ Verify tensor parallelism uses power of 2 GPUs: vllm serve MODEL --tensor-parallel-size 4 # Not 3 ``` -Enable speculative decoding for faster generation: +Enable speculative decoding for faster generation (pass config as JSON; +`--speculative-model` was removed in favor of `--speculative-config`): ```bash -vllm serve MODEL --speculative-model DRAFT_MODEL +vllm serve MODEL \ + --speculative-config '{"model": "DRAFT_MODEL", "num_speculative_tokens": 5, "method": "draft_model"}' ``` ## Advanced topics diff --git a/skills/smart-home/openhue/SKILL.md b/skills/smart-home/openhue/SKILL.md index 3f60c0556f9..ce3bbceb035 100644 --- a/skills/smart-home/openhue/SKILL.md +++ b/skills/smart-home/openhue/SKILL.md @@ -1,7 +1,7 @@ --- name: openhue description: "Control Philips Hue lights, scenes, rooms via OpenHue CLI." -version: 1.0.0 +version: 1.0.1 author: community license: MIT platforms: [linux, macos, windows] @@ -20,8 +20,11 @@ Control Philips Hue lights and scenes via a Hue Bridge from the terminal. ## Prerequisites ```bash -# Linux (pre-built binary) -curl -sL https://github.com/openhue/openhue-cli/releases/latest/download/openhue-linux-amd64 -o ~/.local/bin/openhue && chmod +x ~/.local/bin/openhue +# Linux (pre-built binary — releases ship tarballs, not bare binaries) +curl -sL "https://github.com/openhue/openhue-cli/releases/latest/download/openhue_Linux_x86_64.tar.gz" \ + | tar -xz -C /tmp openhue \ + && install -m 0755 /tmp/openhue ~/.local/bin/openhue +# (use openhue_Linux_arm64.tar.gz on ARM64) # macOS brew install openhue/cli/openhue-cli