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Code Issues Projects Releases Packages Wiki Activity Actions 9

docs: deep audit — fix stale config keys, missing commands, and registry drift (#22784)

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* docs: deep audit — fix stale config keys, missing commands, and registry drift

Cross-checked ~80 high-impact docs pages (getting-started, reference, top-level
user-guide, user-guide/features) against the live registries:

  hermes_cli/commands.py    COMMAND_REGISTRY (slash commands)
  hermes_cli/auth.py        PROVIDER_REGISTRY (providers)
  hermes_cli/config.py      DEFAULT_CONFIG (config keys)
  toolsets.py               TOOLSETS (toolsets)
  tools/registry.py         get_all_tool_names() (tools)
  python -m hermes_cli.main <subcmd> --help (CLI args)

reference/
- cli-commands.md: drop duplicate hermes fallback row + duplicate section,
  add stepfun/lmstudio to --provider enum, expand auth/mcp/curator subcommand
  lists to match --help output (status/logout/spotify, login, archive/prune/
  list-archived).
- slash-commands.md: add missing /sessions and /reload-skills entries +
  correct the cross-platform Notes line.
- tools-reference.md: drop bogus '68 tools' headline, drop fictional
  'browser-cdp toolset' (these tools live in 'browser' and are runtime-gated),
  add missing 'kanban' and 'video' toolset sections, fix MCP example to use
  the real mcp_<server>_<tool> prefix.
- toolsets-reference.md: list browser_cdp/browser_dialog inside the 'browser'
  row, add missing 'kanban' and 'video' toolset rows, drop the stale
  '38 tools' count for hermes-cli.
- profile-commands.md: add missing install/update/info subcommands, document
  fish completion.
- environment-variables.md: dedupe GMI_API_KEY/GMI_BASE_URL rows (kept the
  one with the correct gmi-serving.com default).
- faq.md: Anthropic/Google/OpenAI examples — direct providers exist (not just
  via OpenRouter), refresh the OpenAI model list.

getting-started/
- installation.md: PortableGit (not MinGit) is what the Windows installer
  fetches; document the 32-bit MinGit fallback.
- installation.md / termux.md: installer prefers .[termux-all] then falls
  back to .[termux].
- nix-setup.md: Python 3.12 (not 3.11), Node.js 22 (not 20); fix invalid
  'nix flake update --flake' invocation.
- updating.md: 'hermes backup restore --state pre-update' doesn't exist —
  point at the snapshot/quick-snapshot flow; correct config key
  'updates.pre_update_backup' (was 'update.backup').

user-guide/
- configuration.md: api_max_retries default 3 (not 2); display.runtime_footer
  is the real key (not display.runtime_metadata_footer); checkpoints defaults
  enabled=false / max_snapshots=20 (not true / 50).
- configuring-models.md: 'hermes model list' / 'hermes model set ...' don't
  exist — hermes model is interactive only.
- tui.md: busy_indicator -> tui_status_indicator with values
  kaomoji|emoji|unicode|ascii (not kawaii|minimal|dots|wings|none).
- security.md: SSH backend keys (TERMINAL_SSH_HOST/USER/KEY) live in .env,
  not config.yaml.
- windows-wsl-quickstart.md: there is no 'hermes api' subcommand — the
  OpenAI-compatible API server runs inside hermes gateway.

user-guide/features/
- computer-use.md: approvals.mode (not security.approval_level); fix broken
  ./browser-use.md link to ./browser.md.
- fallback-providers.md: top-level fallback_providers (not
  model.fallback_providers); the picker is subcommand-based, not modal.
- api-server.md: API_SERVER_* are env vars — write to per-profile .env,
  not 'hermes config set' which targets YAML.
- web-search.md: drop web_crawl as a registered tool (it isn't); deep-crawl
  modes are exposed through web_extract.
- kanban.md: failure_limit default is 2, not '~5'.
- plugins.md: drop hard-coded '33 providers' count.
- honcho.md: fix unclosed quote in echo HONCHO_API_KEY snippet; document
  that 'hermes honcho' subcommand is gated on memory.provider=honcho;
  reconcile subcommand list with actual --help output.
- memory-providers.md: legacy 'hermes honcho setup' redirect documented.

Verified via 'npm run build' — site builds cleanly; broken-link count went
from 149 to 146 (no regressions, fixed a few in passing).

* docs: round 2 audit fixes + regenerate skill catalogs

Follow-up to the previous commit on this branch:

Round 2 manual fixes:
- quickstart.md: KIMI_CODING_API_KEY mentioned alongside KIMI_API_KEY;
  voice-mode and ACP install commands rewritten — bare 'pip install ...'
  doesn't work for curl-installed setups (no pip on PATH, not in repo
  dir); replaced with 'cd ~/.hermes/hermes-agent && uv pip install -e
  ".[voice]"'. ACP already ships in [all] so the curl install includes it.
- cli.md / configuration.md: 'auxiliary.compression.model' shown as
  'google/gemini-3-flash-preview' (the doc's own claimed default);
  actual default is empty (= use main model). Reworded as 'leave empty
  (default) or pin a cheap model'.
- built-in-plugins.md: added the bundled 'kanban/dashboard' plugin row
  that was missing from the table.

Regenerated skill catalogs:
- ran website/scripts/generate-skill-docs.py to refresh all 163 per-skill
  pages and both reference catalogs (skills-catalog.md,
  optional-skills-catalog.md). This adds the entries that were genuinely
  missing — productivity/teams-meeting-pipeline (bundled),
  optional/finance/* (entire category — 7 skills:
  3-statement-model, comps-analysis, dcf-model, excel-author, lbo-model,
  merger-model, pptx-author), creative/hyperframes,
  creative/kanban-video-orchestrator, devops/watchers,
  productivity/shop-app, research/searxng-search,
  apple/macos-computer-use — and rewrites every other per-skill page from
  the current SKILL.md. Most diffs are tiny (one line of refreshed
  metadata).

Validation:
- 'npm run build' succeeded.
- Broken-link count moved 146 -> 155 — the +9 are zh-Hans translation
  shells that lag every newly-added skill page (pre-existing pattern).
  No regressions on any en/ page.
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website/docs/user-guide/skills/optional/mlops/mlops-accelerate.md
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@ -20,6 +20,7 @@ Simplest distributed training API. 4 lines to add distributed support to any PyT
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `accelerate`, `torch`, `transformers` |
| Platforms | linux, macos, windows |
| Tags | `Distributed Training`, `HuggingFace`, `Accelerate`, `DeepSpeed`, `FSDP`, `Mixed Precision`, `PyTorch`, `DDP`, `Unified API`, `Simple` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-chroma.md
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@ -20,6 +20,7 @@ Open-source embedding database for AI applications. Store embeddings and metadat
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `chromadb`, `sentence-transformers` |
| Platforms | linux, macos, windows |
| Tags | `RAG`, `Chroma`, `Vector Database`, `Embeddings`, `Semantic Search`, `Open Source`, `Self-Hosted`, `Document Retrieval`, `Metadata Filtering` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-clip.md
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@ -20,6 +20,7 @@ OpenAI's model connecting vision and language. Enables zero-shot image classific
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `transformers`, `torch`, `pillow` |
| Platforms | linux, macos, windows |
| Tags | `Multimodal`, `CLIP`, `Vision-Language`, `Zero-Shot`, `Image Classification`, `OpenAI`, `Image Search`, `Cross-Modal Retrieval`, `Content Moderation` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-faiss.md
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@ -20,6 +20,7 @@ Facebook's library for efficient similarity search and clustering of dense vecto
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `faiss-cpu`, `faiss-gpu`, `numpy` |
| Platforms | linux, macos |
| Tags | `RAG`, `FAISS`, `Similarity Search`, `Vector Search`, `Facebook AI`, `GPU Acceleration`, `Billion-Scale`, `K-NN`, `HNSW`, `High Performance`, `Large Scale` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-flash-attention.md
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@ -20,6 +20,7 @@ Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `flash-attn`, `torch`, `transformers` |
| Platforms | linux, macos |
| Tags | `Optimization`, `Flash Attention`, `Attention Optimization`, `Memory Efficiency`, `Speed Optimization`, `Long Context`, `PyTorch`, `SDPA`, `H100`, `FP8`, `Transformers` |
## Reference: full SKILL.md
@ -362,10 +363,6 @@ Flash Attention uses float16/bfloat16 for speed. Float32 not supported.
**Performance benchmarks**: See [references/benchmarks.md](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops/flash-attention/references/benchmarks.md) for detailed speed and memory comparisons across GPUs and sequence lengths.
**Algorithm details**: See [references/algorithm.md](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops/flash-attention/references/algorithm.md) for tiling strategy, recomputation, and IO complexity analysis.
**Advanced features**: See [references/advanced-features.md](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops/flash-attention/references/advanced-features.md) for rotary embeddings, ALiBi, paged KV cache, and custom attention masks.
## Hardware requirements
- **GPU**: NVIDIA Ampere+ (A100, A10, A30) or AMD MI200+

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website/docs/user-guide/skills/optional/mlops/mlops-guidance.md
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@ -20,6 +20,7 @@ Control LLM output with regex and grammars, guarantee valid JSON/XML/code genera
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `guidance`, `transformers` |
| Platforms | linux, macos, windows |
| Tags | `Prompt Engineering`, `Guidance`, `Constrained Generation`, `Structured Output`, `JSON Validation`, `Grammar`, `Microsoft Research`, `Format Enforcement`, `Multi-Step Workflows` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-hermes-atropos-environments.md
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@ -19,6 +19,7 @@ Build, test, and debug Hermes Agent RL environments for Atropos training. Covers
| Version | `1.1.0` |
| Author | Hermes Agent |
| License | MIT |
| Platforms | linux, macos, windows |
| Tags | `atropos`, `rl`, `environments`, `training`, `reinforcement-learning`, `reward-functions` |
| Related skills | [`axolotl`](/docs/user-guide/skills/bundled/mlops/mlops-training-axolotl), [`fine-tuning-with-trl`](/docs/user-guide/skills/bundled/mlops/mlops-training-trl-fine-tuning), `lm-evaluation-harness` |

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website/docs/user-guide/skills/optional/mlops/mlops-huggingface-tokenizers.md
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@ -20,6 +20,7 @@ Fast tokenizers optimized for research and production. Rust-based implementation
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `tokenizers`, `transformers`, `datasets` |
| Platforms | linux, macos, windows |
| Tags | `Tokenization`, `HuggingFace`, `BPE`, `WordPiece`, `Unigram`, `Fast Tokenization`, `Rust`, `Custom Tokenizer`, `Alignment Tracking`, `Production` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-instructor.md
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@ -20,6 +20,7 @@ Extract structured data from LLM responses with Pydantic validation, retry faile
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `instructor`, `pydantic`, `openai`, `anthropic` |
| Platforms | linux, macos, windows |
| Tags | `Prompt Engineering`, `Instructor`, `Structured Output`, `Pydantic`, `Data Extraction`, `JSON Parsing`, `Type Safety`, `Validation`, `Streaming`, `OpenAI`, `Anthropic` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-lambda-labs.md
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@ -20,6 +20,7 @@ Reserved and on-demand GPU cloud instances for ML training and inference. Use wh
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `lambda-cloud-client>=1.0.0` |
| Platforms | linux, macos, windows |
| Tags | `Infrastructure`, `GPU Cloud`, `Training`, `Inference`, `Lambda Labs` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-llava.md
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@ -20,6 +20,7 @@ Large Language and Vision Assistant. Enables visual instruction tuning and image
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `transformers`, `torch`, `pillow` |
| Platforms | linux, macos, windows |
| Tags | `LLaVA`, `Vision-Language`, `Multimodal`, `Visual Question Answering`, `Image Chat`, `CLIP`, `Vicuna`, `Conversational AI`, `Instruction Tuning`, `VQA` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-modal.md
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@ -20,6 +20,7 @@ Serverless GPU cloud platform for running ML workloads. Use when you need on-dem
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `modal>=0.64.0` |
| Platforms | linux, macos, windows |
| Tags | `Infrastructure`, `Serverless`, `GPU`, `Cloud`, `Deployment`, `Modal` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-nemo-curator.md
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@ -20,6 +20,7 @@ GPU-accelerated data curation for LLM training. Supports text/image/video/audio.
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `nemo-curator`, `cudf`, `dask`, `rapids` |
| Platforms | linux, macos |
| Tags | `Data Processing`, `NeMo Curator`, `Data Curation`, `GPU Acceleration`, `Deduplication`, `Quality Filtering`, `NVIDIA`, `RAPIDS`, `PII Redaction`, `Multimodal`, `LLM Training Data` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-peft.md
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@ -20,6 +20,7 @@ Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `peft>=0.13.0`, `transformers>=4.45.0`, `torch>=2.0.0`, `bitsandbytes>=0.43.0` |
| Platforms | linux, macos, windows |
| Tags | `Fine-Tuning`, `PEFT`, `LoRA`, `QLoRA`, `Parameter-Efficient`, `Adapters`, `Low-Rank`, `Memory Optimization`, `Multi-Adapter` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-pinecone.md
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@ -20,6 +20,7 @@ Managed vector database for production AI applications. Fully managed, auto-scal
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `pinecone-client` |
| Platforms | linux, macos, windows |
| Tags | `RAG`, `Pinecone`, `Vector Database`, `Managed Service`, `Serverless`, `Hybrid Search`, `Production`, `Auto-Scaling`, `Low Latency`, `Recommendations` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-pytorch-fsdp.md
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@ -20,6 +20,7 @@ Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - par
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `torch>=2.0`, `transformers` |
| Platforms | linux, macos |
| Tags | `Distributed Training`, `PyTorch`, `FSDP`, `Data Parallel`, `Sharding`, `Mixed Precision`, `CPU Offloading`, `FSDP2`, `Large-Scale Training` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-pytorch-lightning.md
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@ -20,6 +20,7 @@ High-level PyTorch framework with Trainer class, automatic distributed training
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `lightning`, `torch`, `transformers` |
| Platforms | linux, macos, windows |
| Tags | `PyTorch Lightning`, `Training Framework`, `Distributed Training`, `DDP`, `FSDP`, `DeepSpeed`, `High-Level API`, `Callbacks`, `Best Practices`, `Scalable` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-qdrant.md
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@ -20,6 +20,7 @@ High-performance vector similarity search engine for RAG and semantic search. Us
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `qdrant-client>=1.12.0` |
| Platforms | linux, macos, windows |
| Tags | `RAG`, `Vector Search`, `Qdrant`, `Semantic Search`, `Embeddings`, `Similarity Search`, `HNSW`, `Production`, `Distributed` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-saelens.md
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@ -20,6 +20,7 @@ Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SA
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `sae-lens>=6.0.0`, `transformer-lens>=2.0.0`, `torch>=2.0.0` |
| Platforms | linux, macos, windows |
| Tags | `Sparse Autoencoders`, `SAE`, `Mechanistic Interpretability`, `Feature Discovery`, `Superposition` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-simpo.md
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@ -20,6 +20,7 @@ Simple Preference Optimization for LLM alignment. Reference-free alternative to
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `torch`, `transformers`, `datasets`, `trl`, `accelerate` |
| Platforms | linux, macos, windows |
| Tags | `Post-Training`, `SimPO`, `Preference Optimization`, `Alignment`, `DPO Alternative`, `Reference-Free`, `LLM Alignment`, `Efficient Training` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-slime.md
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@ -20,6 +20,7 @@ Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang f
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `sglang-router>=0.2.3`, `ray`, `torch>=2.0.0`, `transformers>=4.40.0` |
| Platforms | linux, macos |
| Tags | `Reinforcement Learning`, `Megatron-LM`, `SGLang`, `GRPO`, `Post-Training`, `GLM` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-stable-diffusion.md
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@ -20,6 +20,7 @@ State-of-the-art text-to-image generation with Stable Diffusion models via Huggi
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `diffusers>=0.30.0`, `transformers>=4.41.0`, `accelerate>=0.31.0`, `torch>=2.0.0` |
| Platforms | linux, macos, windows |
| Tags | `Image Generation`, `Stable Diffusion`, `Diffusers`, `Text-to-Image`, `Multimodal`, `Computer Vision` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-tensorrt-llm.md
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@ -20,6 +20,7 @@ Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest l
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `tensorrt-llm`, `torch` |
| Platforms | linux, macos |
| Tags | `Inference Serving`, `TensorRT-LLM`, `NVIDIA`, `Inference Optimization`, `High Throughput`, `Low Latency`, `Production`, `FP8`, `INT4`, `In-Flight Batching`, `Multi-GPU` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-torchtitan.md
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@ -20,6 +20,7 @@ Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D par
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `torch>=2.6.0`, `torchtitan>=0.2.0`, `torchao>=0.5.0` |
| Platforms | linux, macos |
| Tags | `Model Architecture`, `Distributed Training`, `TorchTitan`, `FSDP2`, `Tensor Parallel`, `Pipeline Parallel`, `Context Parallel`, `Float8`, `Llama`, `Pretraining` |
## Reference: full SKILL.md

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website/docs/user-guide/skills/optional/mlops/mlops-whisper.md
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@ -20,6 +20,7 @@ OpenAI's general-purpose speech recognition model. Supports 99 languages, transc
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `openai-whisper`, `transformers`, `torch` |
| Platforms | linux, macos |
| Tags | `Whisper`, `Speech Recognition`, `ASR`, `Multimodal`, `Multilingual`, `OpenAI`, `Speech-To-Text`, `Transcription`, `Translation`, `Audio Processing` |
## Reference: full SKILL.md