* chore(skills): remove red-team skills (godmode, obliteratus) from bundled catalog
Anthropic's output classifier on claude-fable-5 (and likely other Claude
models served through it) intermittently returns empty content for sessions
whose system prompt advertises these skills. The bundled skills-catalog block
is injected into every session's system prompt, so the descriptions
- red-teaming/godmode 'Jailbreak LLMs: Parseltongue, GODMODE, ULTRAPLINIAN'
- mlops/inference/obliteratus 'OBLITERATUS: abliterate LLM refusals (diff-in-means)'
trip the classifier on EVERY session regardless of which skill is actually
loaded, killing unrelated legitimate work (PR review, codebase audits, etc.).
Measured impact (controlled, interleaved A/B, claude-fable-5 via OpenRouter,
prompts differing only by the ~204 chars of these catalog lines, N=20 each):
catalog lines present -> 19/20 (95%) blocked
catalog lines absent -> 5/20 (25%) blocked
Removing them ~quartered the block rate. Rewording the descriptions was not
enough; the skills must leave the bundled catalog.
- Delete skills/red-teaming/godmode and skills/mlops/inference/obliteratus
- Drop their generated doc pages + catalog/sidebar entries (EN + zh-Hans)
- Drop the godmode hand-written-page exception in generate-skill-docs.py
* chore(skills): relocate godmode + obliteratus to optional-skills
Rather than deleting outright, move both into optional-skills/ so they remain
installable via `hermes skills install` while leaving the always-injected
bundled catalog (which is what tripped Anthropic's classifier).
- optional-skills/security/godmode (was skills/red-teaming/godmode)
- optional-skills/mlops/obliteratus (was skills/mlops/inference/obliteratus)
- regenerate optional-skills catalog + sidebar entries
These skills require heavy GPU/CUDA stacks or are niche enough that they shouldn't
be active by default. Moved to optional-skills/ where users opt-in via
`hermes skills install official/...`.
Moved:
- mlops/training/axolotl
- mlops/training/trl-fine-tuning
- mlops/training/unsloth
- mlops/inference/outlines
Counts: 91 -> 87 built-in, 72 -> 76 optional.
Auto-regenerated docs (per-skill pages + catalogs) reflect the move.
Completes the Windows-gating coverage for the built-in skills/ tree. Every
bundled SKILL.md now carries an explicit platforms: declaration so the
loader (agent.skill_utils.skill_matches_platform) can skip-load skills
that don't fit the current OS.
74 skills declared cross-platform (platforms: [linux, macos, windows]):
Creative (16): ascii-art, ascii-video, architecture-diagram, baoyu-comic,
baoyu-infographic, claude-design, creative-ideation, design-md,
excalidraw, humanizer, manim-video, p5js, pixel-art,
popular-web-designs, pretext, sketch, songwriting-and-ai-music,
touchdesigner-mcp
Autonomous agents: claude-code, codex, hermes-agent, opencode
Data/devops: jupyter-live-kernel, kanban-orchestrator, kanban-worker,
webhook-subscriptions, dogfood, codebase-inspection
GitHub: github-auth, github-code-review, github-issues,
github-pr-workflow, github-repo-management
Media: gif-search, heartmula, songsee, spotify, youtube-content
MCP / email / gaming / notes / smart-home: native-mcp, himalaya,
pokemon-player, obsidian, openhue
mlops (non-broken): weights-and-biases, huggingface-hub, llama-cpp,
outlines, segment-anything-model, dspy, trl-fine-tuning
Productivity: airtable, google-workspace, linear, maps, nano-pdf,
notion, ocr-and-documents, powerpoint
Red-teaming / research: godmode, arxiv, blogwatcher, llm-wiki,
polymarket
Software-dev: debugging-hermes-tui-commands, hermes-agent-skill-authoring,
node-inspect-debugger, plan, requesting-code-review, spike,
subagent-driven-development, systematic-debugging,
test-driven-development, writing-plans
Misc: yuanbao
5 skills gated from Windows (platforms: [linux, macos]):
mlops/inference/vllm (serving-llms-vllm)
vLLM is officially Linux-only; Windows requires WSL.
mlops/training/axolotl
Axolotl's flash-attn + deepspeed + bitsandbytes stack is Linux-first.
mlops/training/unsloth
Requires Triton + xformers + flash-attn — Linux only in practice.
mlops/models/audiocraft (audiocraft-audio-generation)
torchaudio ffmpeg backend + encodec dependencies are Linux-first.
mlops/inference/obliteratus
Research abliteration workflow; relies on Linux-focused pytorch
kernels and MLX — no first-class Windows path.
Same strict-over-lenient policy as the optional-skills sweep: when the
underlying tool's Windows support is rough, missing, or WSL-only, gate the
skill. Easier to un-gate after verified Windows support lands than to leak
partial support that manifests as mid-task failures.
Combined with prior commits in this branch, every bundled SKILL.md
(skills/ + optional-skills/) now has a platforms: declaration.
For 14 of 74 compressed skills, the original description contained
trigger keywords, technique counts, attribution, or use-case phrases
not covered by the existing body content. Prepends a 'When to use' /
'What's inside' block near the top so the agent still has the full
context when the skill is loaded.
Skills salvaged:
- codex, ascii-video, creative-ideation, excalidraw, manim-video, p5js
- gif-search, heartmula, youtube-content
- lm-evaluation-harness, obliteratus, vllm, axolotl
- powerpoint
Remaining 60 skills were verified to already cover the dropped content
in their existing body sections (When to Use, overview, intro prose)
or had short descriptions fully captured by the new compressed form.
Target: every skill's description fits in a one-line gateway menu and
leads with trigger keywords an agent would match on. Drops filler like
'Use this skill to', 'A skill for', 'This skill provides'.
Before: max description length was 791 chars (architecture-diagram),
74 of 81 built-in skills were >60 chars.
After: max 60, mean 54, all 81 built-in skills <=60.
Rewritten with double-quoted YAML scalars to preserve Chinese/arrow
glyphs (baoyu-comic, yuanbao, youtube-content).
Adds a 'Video Guide' section pointing at the walkthrough of a Hermes agent
abliterating Gemma with OBLITERATUS, so the agent can surface it when the
user wants a visual overview before running the workflow.
- Description truncated to 60 chars in system prompt (extract_skill_description),
so the 500-char HF workflow description never reached the agent; shortened to
'llama.cpp local GGUF inference + HF Hub model discovery.' (56 chars).
- Restore llama-cpp-python section (basic, chat+stream, embeddings,
Llama.from_pretrained) and frontmatter dependencies entry.
- Fix broken 'Authorization: Bearer ***' curl line (missing closing quote;
llama-server doesn't require auth by default).
Three tightly-scoped built-in skill consolidations to reduce redundancy in
the available_skills listing injected into every system prompt:
1. gguf-quantization → llama-cpp (merged)
GGUF is llama.cpp's format; two skills covered the same toolchain. The
merged llama-cpp skill keeps the full K-quant table + imatrix workflow
from gguf and the ROCm/benchmarks/supported-models sections from the
original llama-cpp. All 5 reference files preserved.
2. grpo-rl-training → fine-tuning-with-trl (folded in)
GRPO isn't a framework, it's a trainer inside TRL. Moved the 17KB
deep-dive SKILL.md to references/grpo-training.md and the working
template to templates/basic_grpo_training.py. TRL's GRPO workflow
section now points to both. Atropos skill's related_skills updated.
3. guidance → optional-skills/mlops/
Dropped from built-in. Outlines (still built-in) covers the same
structured-generation ground with wider adoption. Listed in the
optional catalog for users who specifically want Guidance.
Net: 3 fewer built-in skill lines in every system prompt, zero content
loss. Contributor authorship preserved via git rename detection.
* feat(gateway): skill-aware slash commands, paginated /commands, Telegram 100-cap
Map active skills to Telegram's slash command menu so users can
discover and invoke skills directly. Three changes:
1. Telegram menu now includes active skill commands alongside built-in
commands, capped at 100 entries (Telegram Bot API limit). Overflow
commands remain callable but hidden from the picker. Logged at
startup when cap is hit.
2. New /commands [page] gateway command for paginated browsing of all
commands + skills. /help now shows first 10 skill commands and
points to /commands for the full list.
3. When a user types a slash command that matches a disabled or
uninstalled skill, they get actionable guidance:
- Disabled: 'Enable it with: hermes skills config'
- Optional (not installed): 'Install with: hermes skills install official/<path>'
Built on ideas from PR #3921 by @kshitijk4poor.
* chore: move 21 niche skills to optional-skills
Move specialized/niche skills from built-in (skills/) to optional
(optional-skills/) to reduce the default skill count. Users can
install them with: hermes skills install official/<category>/<name>
Moved skills (21):
- mlops: accelerate, chroma, faiss, flash-attention,
hermes-atropos-environments, huggingface-tokenizers, instructor,
lambda-labs, llava, nemo-curator, pinecone, pytorch-lightning,
qdrant, saelens, simpo, slime, tensorrt-llm, torchtitan
- research: domain-intel, duckduckgo-search
- devops: inference-sh cli
Built-in skills: 96 → 75
Optional skills: 22 → 43
* fix: only include repo built-in skills in Telegram menu, not user-installed
User-installed skills (from hub or manually added) stay accessible via
/skills and by typing the command directly, but don't get registered
in the Telegram slash command picker. Only skills whose SKILL.md is
under the repo's skills/ directory are included in the menu.
This keeps the Telegram menu focused on the curated built-in set while
user-installed skills remain discoverable through /skills and /commands.
