llm-boost
Optimize LLM-facing content: documentation c7score, llms.txt generation, Claude Code skill optimization, XML tag structuring for prompts, CLAUDE.md auditing, and LLM parameter tuning. Use when optimizing docs for AI assistants, creating llms.txt, improving c7score, optimizing skills for token efficiency, applying 500-line rule, implementing progressive disclosure, designing XML tag structures for prompts, auditing CLAUDE.md files, tuning LLM temperature and token settings, or compressing markdown for context efficiency.
What this skill does
# LLM Boost Skill
Optimize all LLM-facing content: documentation, skills, prompts, and parameters.
## Quick Reference
| Area | Key Metric | Target |
| ---------- | ------------------------- | ---------------- |
| c7score | Question-Snippet Match | 80% weight |
| Skills | SKILL.md size | <=500 lines |
| LLM Tuning | Task-appropriate settings | See tuning table |
---
## Documentation Optimization (c7score)
<workflow>
1. **Analyze**: Read README.md, docs/\*.md
2. **Generate questions**: Create 15-20 "How do I..." questions covering setup, auth, basic usage, errors, advanced features, integrations
3. **Map questions to snippets**: Mark complete, partial, or missing (prioritize missing)
4. **Optimize** by priority:
| Priority | Weight | Action |
| --------------------- | ------ | -------------------------------------------- |
| P1: Question coverage | 80% | Add complete code for unanswered questions |
| P2: Remove duplicates | 5% | Consolidate similar snippets |
| P3: Fix formatting | 5% | Proper language tags, TITLE/DESCRIPTION/CODE |
| P4: Remove metadata | 5% | Strip licensing, directory trees, citations |
| P5: Enhance init | 5% | Combine import-only with usage examples |
5. **Validate** each snippet: runs standalone, answers specific question, proper format, includes imports
6. **Score** before vs after across all 5 metrics
</workflow>
### Snippet Transformation Patterns
- **API ref to usage example**: Replace method signatures with complete working code including imports, setup, and expected output
- **Import-only to complete setup**: Combine `from lib import X` with actual usage showing real output
- **Multiple fragments to one comprehensive**: Merge related 1-2 line snippets into one complete workflow
- **Remove metadata**: Strip directory trees, license text, BibTeX citations entirely
For detailed patterns: [references/optimization_patterns.md](references/optimization_patterns.md)
---
## llms.txt Generation
<format_rules>
- H1 title required, H2 sections only (no H3+)
- Full URLs with protocol, prefer .md files
- `- [Title](url): description` link format
- "Optional" section = skippable for shorter context
- No code blocks, images, or complex formatting
- Place at repo root as `/llms.txt`
</format_rules>
| Project Type | Must Have | Should Have |
| ------------ | ---------------------------------------------- | ---------------------------- |
| Library | Documentation, API Reference, Examples | Getting Started, Development |
| CLI Tool | Getting Started, Commands, Examples | Configuration, Development |
| Framework | Documentation, Guides, API Reference, Examples | Integrations |
For templates: [examples/sample_llmstxt.md](examples/sample_llmstxt.md)
---
## Skill Optimization
### 500-Line Rule
**Keep in SKILL.md**: purpose, quick start, critical practices, brief examples (5-10 lines), cross-references.
**Move to reference files**: API docs, extensive examples (>20 lines), troubleshooting, pattern libraries, schemas.
### Optimization Modes
| Mode | Size | Action |
| ---------- | ---------- | ------------------------------------------- |
| Light | <3K tokens | Tighten wording, add YAML if missing |
| Standard | 3K-6K | Consolidate, tables over prose, one example |
| Aggressive | 6K-10K | Table everything, strip filler |
| Split | >=10K | Propose 3-4 files + index |
### YAML Frontmatter
Description field (max 1024 chars) must include: what the skill does, when to use it, key technologies, action verbs. Write in third person.
### Progressive Disclosure Pattern
```markdown
## Topic Overview
Brief explanation (2-3 sentences).
**Quick Example:**
(5-10 line code block)
**For detailed docs**: [REFERENCE.md](REFERENCE.md#topic)
```
---
## XML Tag Structuring
<design_principles>
| Principle | Guideline |
| ------------------ | -------------------------------------------------------------------- |
| Semantic naming | Tag names describe content: `<contract>`, `<rubric>` |
| Consistency | Same tag names throughout; reference by name in instructions |
| Nesting | `<outer><inner></inner></outer>` for hierarchy |
| No canonical tags | No "best" tags - name for your use case |
| Combine techniques | Pair with CoT (`<thinking>`/`<answer>`) and multishot (`<examples>`) |
</design_principles>
### Core Patterns
**Multi-document**: `<documents><document index="1"><source>...</source><content>...</content></document></documents>`
**Structured evaluation**: `<rubric>` + `<submission>` -> `<evaluation><score>` + `<feedback>`
**CoT separation**: `<thinking>` for reasoning, `<answer>` for final output
**Multishot examples**: `<examples><example><input>...</input><output>...</output></example></examples>`
**Guard rails**: `<instructions><task>...</task><formatting>...</formatting><constraints>...</constraints></instructions>`
### Output Extraction
```python
import re
def extract_tag(text, tag):
match = re.search(f'<{tag}>(.*?)</{tag}>', text, re.DOTALL)
return match.group(1).strip() if match else None
```
For comprehensive tag catalog: [references/xml_tags.md](references/xml_tags.md)
---
## LLM Parameter Tuning
| Task | max_tokens | temperature | top_p | Rationale |
| -------------------- | ---------- | ----------- | ----- | --------------------------------------------- |
| Theorem proving | 4096 | 0.6 | 0.95 | CoT needs space; higher temp explores tactics |
| Code generation | 2048 | 0.2-0.4 | - | Deterministic preferred |
| Creative/exploration | 4096 | 0.8-1.0 | - | Maximum diversity |
| Classification | 256 | 0.0-0.1 | - | Consistency over creativity |
| Summarization | 1024 | 0.3 | - | Faithful to source |
---
## CLAUDE.md Audit Checklist
| Check | How |
| ---------------------- | -------------------------------------- |
| Tech stack claims | `Read("package.json\|Cargo.toml")` |
| File path references | `Glob("claimed/path")` |
| Command references | `Grep("script", glob="package.json")` |
| Testing framework | `Glob("**/*.test.*")` |
| Linting config | `Glob("**/biome.json\|**/.eslintrc*")` |
| Line count | `wc -l CLAUDE.md` - target <300 |
| No code duplication | Uses file:line pointers |
| WHAT/WHY/HOW structure | Manual review |
---
## Reference Materials
- [c7score Metrics](references/c7score_metrics.md) - scoring rubrics and weights
- [Optimization Patterns](references/optimization_patterns.md) - snippet transformation patterns
- [llms.txt Format](references/llmstxt_format.md) - complete format specification
- [XML Tag Patterns](references/xml_tags.md) - comprehensive tag catalog
- [Skill Optimization](references/skill_optimization.md) - 3-level loading, migration workflow
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.