skills/skill-creator/SKILL.md
Transforms documents containing theoretical knowledge or frameworks (PDFs, markdown, book notes, research papers, methodology guides) into actionable, reusable Claude Code skills using systematic reading methodology. Use when user mentions "create a skill from this document", "turn this into a skill", "extract a skill from this file", or when analyzing documents with methodologies, frameworks, or processes that could be made actionable.
npx skillsauth add lyndonkl/claude skill-creatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill applies Mortimer Adler's systematic reading methodology ("How to Read a Book") through a six-step progressive approach: inspectional reading, structural analysis, component extraction, synthesis, skill construction, and validation. The process is collaborative -- at decision points, options and trade-offs are presented for the user to choose.
Methodology composition. All five extractable steps are now backed by domain-neutral standalone skills, reusable beyond skill creation. The orchestration is owned by the skill-creator agent; this skill-file remains as documentation of the methodology and as a way to apply the methodology without the agent's session-workspace overhead.
| Step | Standalone skill | purpose_context to pass | Local resource still useful for |
| ---- | ---------------- | ------------------------- | ------------------------------- |
| 1 | inspectional-reading | skill_extraction_from_methodology | session init mechanics ($SESSION_DIR, global-context.md) |
| 2 | structural-analysis | skill_extraction_from_methodology | unity-formula examples specific to skill creation |
| 3 | component-extraction | skill_extraction_from_methodology | section-based reading patterns specific to long-form skill sources |
| 4 | synthesis-application | skill_construction | completeness inventory specific to skills (terms / propositions / arguments / solutions / decision-criteria / triggers) |
| 5 | skill-construction | skill_construction | rubric template + complexity-level decision aid |
| 6 | evaluation-rubrics | (existing skill, no purpose_context needed) | n/a |
COPY THIS CHECKLIST and work through each step:
Skill Creation Workflow
- [ ] Step 0: Initialize session workspace
- [ ] Step 1: Inspectional Reading
- [ ] Step 2: Structural Analysis
- [ ] Step 3: Component Extraction
- [ ] Step 4: Synthesis and Application
- [ ] Step 5: Skill Construction
- [ ] Step 6: Validation and Refinement
Step 0: Initialize Session Workspace
Create working directory and global context file. See resources/inspectional-reading.md#session-initialization for setup commands.
Step 1: Inspectional Reading
Skim document systematically, classify type, assess skill-worthiness. Writes to step-1-output.md. See resources/inspectional-reading.md#why-systematic-skimming for skim approach, resources/inspectional-reading.md#why-document-type-matters for classification, resources/inspectional-reading.md#why-skill-worthiness-check for assessment criteria.
Step 2: Structural Analysis
Reads global-context.md + step-1-output.md. Classify content, state unity, enumerate parts, define problems. Writes to step-2-output.md. See resources/structural-analysis.md#why-classify-content, resources/structural-analysis.md#why-state-unity, resources/structural-analysis.md#why-enumerate-parts, resources/structural-analysis.md#why-define-problems.
Step 3: Component Extraction
Reads global-context.md + step-2-output.md. Choose reading strategy, extract terms/propositions/arguments/solutions section-by-section. Writes to step-3-output.md. See resources/component-extraction.md#why-reading-strategy for strategy selection, resources/component-extraction.md#section-based-extraction for programmatic approach, resources/component-extraction.md#why-extract-terms through resources/component-extraction.md#why-extract-solutions for what to extract.
Step 4: Synthesis and Application
Reads global-context.md + step-3-output.md. Evaluate completeness, identify applications, transform to actionable steps, define triggers. Writes to step-4-output.md. See resources/synthesis-application.md#why-evaluate-completeness, resources/synthesis-application.md#why-identify-applications, resources/synthesis-application.md#why-transform-to-actions, resources/synthesis-application.md#why-define-triggers.
Step 5: Skill Construction
Reads global-context.md + step-4-output.md. Determine complexity, plan resources, create SKILL.md and resource files, create rubric. Writes to step-5-output.md. See resources/skill-construction.md#why-complexity-level, resources/skill-construction.md#why-plan-resources, resources/skill-construction.md#why-skill-md-structure, resources/skill-construction.md#why-resource-structure, resources/skill-construction.md#why-evaluation-rubric.
Step 6: Validation and Refinement
Reads global-context.md + step-5-output.md + actual skill files. Score using rubric, present analysis, refine based on user decision. Writes to step-6-output.md. See resources/evaluation-rubric.json for criteria.
global-context.md for continuitytesting
Cluster a conference's event records into a small set of coarse themes with finer sub-clusters, an explicit outlier bucket, and soft (multi-membership) affinities — using the hybrid embed-then-label pipeline (embed abstracts, reduce, density-cluster, then LLM-label the clusters) when embedding libraries are available, and an LLM-reasoned hierarchical fallback when they are not. Embeddings do the grouping; the LLM only names the groups. Conference-agnostic. Use when turning structured event records into a navigable theme map for preference elicitation and scheduling, when you need 6-8 reasonable themes rather than 20 muddy ones, or when overlapping talks must belong to more than one theme. Trigger keywords - theme clustering, cluster talks, embed then label, soft membership, outlier talks, conference themes, topic map.
development
Build a personal conference schedule as a constraint-optimization problem — hard constraints (no time overlap, room-to-room travel time, capacity/registration, the attendee's own must-attends and blackouts) plus a user-owned weighted objective trading interest against breadth, pacing (maximize contiguous free time), and serendipity. Surfaces unbreakable conflicts (two high-value overlapping talks the model cannot rank) as decisions for the human rather than silently picking, and reports what each choice traded away. Conference-agnostic. Use to turn a preference profile plus a theme map into a day-by-day plan, to resolve overlapping sessions, or to balance a packed vs paced schedule. Trigger keywords - schedule optimization, conference schedule, constraint optimization, overlapping talks, contiguous free time, conflict surfacing, packed vs paced.
development
Parse a heterogeneous conference program (markdown, HTML, PDF-derived text, or JSON) into normalized event records with per-field confidence scores and independent classification axes (topic, depth, format, prerequisites, recorded, capacity). Detects the program's format before extracting, treats every inferred field as uncertain (present vs inferred vs missing), and flags thin or missing abstracts so downstream enrichment can target them. Conference-agnostic. Use when ingesting a conference or event schedule into a structured store, normalizing a talk/session list, or extracting per-session metadata with calibrated confidence. Trigger keywords - program ingestion, parse schedule, session extraction, event records, conference program, talk metadata, per-field confidence.
development
Build a personalized preference profile from a small number of well-chosen, cluster-grounded questions instead of a long survey. Represents the person's interests as an uncertainty region over the theme map, picks the single highest-information-gain choice-based question (contrasting real talks from different clusters), balances exploiting known interests against exploring uncertain ones, deliberately injects outlier probes to fight selection bias, and stops as soon as the schedule would be stable. Also elicits the user-owned objective weights and hard constraints. Interactive — runs where it can actually ask the person. Conference-agnostic. Use to turn a theme map into a preference profile, to decide what to ask a conference attendee, or to elicit scheduling priorities. Trigger keywords - preference elicitation, ask few questions, information gain, choice-based questions, selection bias probe, objective weights, attendee preferences.