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Teknium 38d3c49aaf refactor(skills): clean up bundled skill set + add environments: relevance gate (#39028 )

* refactor(skills): clean up bundled skill set + add environments: relevance gate

Bundled skills cleanup pass plus a new offer-time relevance gate.

Removals (redundant / dead):
- spotify (covered by the spotify plugin's 7 native tools)
- linear (covered by `hermes mcp install linear`)
- kanban-codex-lane, debugging-hermes-tui-commands
- empty category markers: diagramming, gifs, inference-sh,
  mlops/training, mlops/vector-databases
- domain (stale orphan dup of optional/research/domain-intel)

Bundled -> optional:
- baoyu-article-illustrator, baoyu-comic, creative-ideation, pixel-art
- dspy, subagent-driven-development
- minecraft-modpack-server, pokemon-player
- hermes-s6-container-supervision (-> optional/devops)

Consolidation:
- webhook-subscriptions + native-mcp folded into the hermes-agent skill
  as references/webhooks.md + references/native-mcp.md with SKILL.md pointers
- writing-plans merged into plan (v2.0.0); related_skills + prose refs updated

New: environments: frontmatter gate (agent/skill_utils.skill_matches_environment)
- Offer-time relevance filter (kanban / docker / s6), parallel to platforms:.
- Wired into the 3 OFFER surfaces only (prompt_builder skills index,
  skills_tool.list_skills, skill_commands slash discovery).
- Explicit loads (skill_view, --skills preload) intentionally BYPASS it, so
  load-bearing force-loads like the kanban dispatcher's `--skills kanban-worker`
  always resolve. Verified via E2E.
- kanban-orchestrator/kanban-worker tagged environments: [kanban];
  hermes-s6-container-supervision tagged environments: [s6] + platforms: [linux].

Validation: 8/8 E2E gating assertions (incl force-load invariant);
442 targeted tests green (agent, skills_tool, skill_commands, kanban worker).

* docs: regenerate skill catalogs + pages for the bundled cleanup

Regenerated per-skill doc pages, catalogs, and sidebar to match the skill
moves/removals in the parent commit. Moved skills' pages relocate
bundled -> optional (history preserved); removed skills' pages deleted;
edited skills' pages refreshed (hermes-agent now embeds the webhook +
native-mcp reference pointers). zh-Hans i18n mirror: stale bundled pages
and catalog rows for moved/removed skills pruned (new optional translations
land via the translation pipeline).

* test: drop regression test for removed kanban-codex-lane skill

The kanban-codex-lane skill was removed in the bundled-skills cleanup;
its dedicated regression test read the now-deleted SKILL.md and failed
with FileNotFoundError on CI shard 6.

2026-06-04 06:11:22 -07:00

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Ohmsha Manga Guide Style

Guidelines for educational manga comics using the ohmsha preset.

Character Setup

Role	Default	Traits
Student (Role A)	大雄	Confused, asks basic but crucial questions, represents reader
Mentor (Role B)	哆啦A梦	Knowledgeable, patient, uses gadgets as technical metaphors
Antagonist (Role C, optional)	胖虎	Represents misunderstanding, or "noise" in the data

Custom characters: ask the user for role → name mappings (e.g., Student:小明, Mentor:教授, Antagonist:Bug怪).

Character Reference Sheet Style

For Ohmsha style, use manga/anime style with:

Exaggerated expressions for educational clarity
Simple, distinctive silhouettes
Bright, saturated color palettes
Chibi/SD (super-deformed) variants for comedic reactions

Outline Spec Block

Every ohmsha outline must start with:

【漫画规格单】
- Language: [Same as input content]
- Style: Ohmsha (Manga Guide), Full Color
- Layout: Vertical Scrolling Comic (竖版条漫)
- Characters: [List character names and roles]
- Character Reference: characters/characters.png
- Page Limit: ≤20 pages

Visual Metaphor Rules (Critical)

NEVER create "talking heads" panels. Every technical concept must become:

A tangible gadget/prop - Something characters can hold, use, demonstrate
An action scene - Characters doing something that illustrates the concept
A visual environment - Stepping into a metaphorical space

Examples

Concept	Bad (Talking Heads)	Good (Visual Metaphor)
Word embeddings	Characters discussing vectors	哆啦A梦拿出"词向量压缩机"，把书本压缩成彩色小球
Gradient descent	Explaining math formula	大雄在山谷地形上滚球，寻找最低点
Neural network	Diagram on whiteboard	角色走进由发光节点组成的网络迷宫

Page Title Convention

Avoid AI-style "Title: Subtitle" format. Use narrative descriptions:

❌ "Page 3: Introduction to Neural Networks"
✓ "Page 3: 大雄被海量单词淹没，哆啦A梦拿出'词向量压缩机'"

Ending Requirements

NO generic endings ("What will you choose?", "Thanks for reading")
End with: Technical summary moment OR character achieving a small goal
Final panel: Sense of accomplishment, not open-ended question

Good Endings

Student successfully applies learned concept
Visual callback to opening problem, now solved
Mentor gives summary while student demonstrates understanding

Bad Endings

"What do you think?" open questions
"Thanks for reading this tutorial"
Cliffhanger without resolution

Layout Preference

Ohmsha style typically uses:

webtoon (vertical scrolling) - Primary choice
dense - For information-heavy sections
mixed - For varied pacing

Avoid cinematic and splash for educational content.

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