ml-llm-wiki
Use when answering questions from this machine-learning knowledge base. Triggers: questions about transformers, attention cost and efficiency, and long-context scaling; 'what do we know about attention', 'check the ML wiki'. Read-only querying of compiled knowledge; to add, update, supersede, lint, audit, or critique, use the llm-wiki skill instead.
What this skill does
# Machine Learning Wiki A self-contained markdown knowledge base on transformer architectures, attention cost and efficiency, and long-context scaling. This skill is for _querying_ it: the knowledge is already compiled into articles under `wiki/`, so read those rather than re-deriving from scratch. Keep this current: as the wiki grows, update the `name` and `description` above so they describe what it actually covers and trigger on the right questions. (Sample note: this example wiki lives in `examples/` within the llm-wiki repo. To load it as a skill, place the directory in your skills path named `ml-llm-wiki`, so the directory matches the `name` above.) Maintenance and deeper analysis - ingesting sources, superseding stale knowledge, linting, auditing, critiquing reasoning - is not done here. Use the **llm-wiki** skill, which owns the write workflow and the file format. The llm-wiki skill is required to keep this wiki current; without it the wiki is still readable, but do not hand-edit articles outside the conventions in `wiki/README.md`. ## What's inside One topic so far, `machine-learning`: how attention works, why its memory cost was once thought to be a hard quadratic limit and why that turned out to be an implementation artefact, and what makes long context practical. ## How to query 1. Read `wiki/index.md` - the catalogue, grouped by topic. Start here to find relevant articles. 2. Read the articles it points to. Follow body links for related material; `grep -rl "<article>.md" wiki/` lists pages that link to a given article (backlinks). 3. If a `local/` directory exists, search it too and fold in any relevant personal notes, labelling each hit as `local/ (uncommitted)` so it is never mistaken for shared, committed knowledge. `local/` is the user's own, gitignored and absent from the index. 4. Answer from the wiki's content in preference to general knowledge. Cite articles with markdown links, e.g. `[Attention Efficiency](wiki/machine-learning/attention-efficiency.md)`. 5. If a cited article has `status: stale`, say so and point to its replacement. Here, `attention-cost.md` is stale and superseded by `attention-efficiency.md`. 6. If the wiki has no answer, check `wiki/gaps.md` - the question may already be a tracked gap. Recording a new gap is a write, so it goes through the llm-wiki skill, not here. ## Conventions `wiki/README.md` explains the format - frontmatter, the raw/wiki split, and supersession-not-deletion - for anyone reading without a skill. Articles carry `status: current | stale`; stale pages are kept on purpose and point at their replacement. ## Updating To add a source, change an article, supersede knowledge, lint, audit, or critique, invoke the **llm-wiki** skill. It is required for all writes and keeps the format consistent. This skill deliberately does not modify the wiki. ## Tips - Use sub-agents with well defined goals, scope and context to parallelise work and reduce context rot in the main conversation.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.