# Skill Creator Pro > Create new skills, modify and improve existing skills, and measure skill quality. ALWAYS use this skill when users want to create a skill from scratch, edit or optimize an existing skill, extract a skill from a conversation, test a skill, or optimize a skill's description for better triggering accuracy. Also trigger when user says "turn this into a skill", "make a skill", "skill from this", "impr… - **Type:** Skill - **Install:** `agentstack add skill-renol1-skill-creator-pro-skill-creator-pro` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [Renol1](https://agentstack.voostack.com/s/renol1) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [Renol1](https://github.com/Renol1) - **Source:** https://github.com/Renol1/skill-creator-pro ## Install ```sh agentstack add skill-renol1-skill-creator-pro-skill-creator-pro ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Skill Creator Pro Create, test, and iteratively improve agent skills. Works in claude.ai, Claude Code, and Cowork. Cross-platform: installs on Cursor, Codex, Gemini CLI, Windsurf, and 10+ tools. For a working mini-example, see `references/example-skill.md` (35 lines). For a medium-complexity example with lookup tables and references, see `references/example-medium-skill.md` (120 lines). For design principles, see `references/gold-standard.md`. ## Configuration Adjust these defaults to match your workflow: - **default_platform**: claude.ai | claude-code | cowork (default: claude.ai) - **test_rigor**: vibe | standard | rigorous (default: standard) - **output_language**: en | sv | de | fr (default: en) ## The Core Loop 1. **Understand** — What should the skill do? When should it trigger? 2. **Draft** — Write the SKILL.md file and bundled resources 3. **Test** — Run realistic prompts, compare output to expectations 4. **Evaluate** — Review results with the user, grade quality 5. **Improve** — Revise based on feedback, retest 6. **Optimize** — Tune the description for reliable triggering 7. **Package** — Deliver the `.skill` file 8. **Deploy** — Install cross-platform, track performance Jump in wherever the user is. If the user says "just vibe with me", skip the formal eval process — set `test_rigor` to `vibe`. --- ## Routing | User says | Workflow | |-----------|----------| | "Make a skill for X" | **Create** → Step 1 | | "Turn this into a skill" | **Extract** → Step 1b | | "My skill doesn't trigger" | **Optimize** → Step 6 | | "Improve my skill" / shows SKILL.md | **Improve** → Step 5 | | "Test my skill" | **Test** → Step 3 | ### Platform Detection Detect your environment and adapt: - **Claude Code / Cowork**: Subagents available → spawn parallel test runs, use eval viewer, run description optimizer. - **claude.ai**: No subagents → test conversationally, review inline, skip quantitative benchmarks. --- ## Step 1: Understand the Intent 1. **What should this skill enable Claude to do?** (the capability) 2. **When should it trigger?** (phrases, contexts, file types, keywords) 3. **What's the expected output?** (file format, structure, tone) 4. **What should NOT trigger it?** (adjacent tasks sharing keywords) 5. **Are test cases useful?** Verifiable outputs (file transforms, code gen) → yes. Subjective outputs (writing, art) → usually no. Suggest default, let user decide. Proactively ask about edge cases, dependencies, and what "good" looks like. Check available MCPs for research. ### Edge Case Discovery Probe systematically based on skill type: | Skill type | Edge cases to ask about | |------------|------------------------| | **Data transform** (schema→code, CSV→report) | Missing fields, nulls, unexpected types, empty input, huge input, encoding | | **Document generation** (reports, specs) | No data for a section, conflicting inputs, multilingual, very long/short content | | **Code generation** (clients, tests, configs) | Reserved words, special characters, circular deps, platform differences | | **Workflow automation** (deploy, build) | Partial failure, missing credentials, rate limits, idempotency | | **Content/creative** (writing, design) | Ambiguous tone, conflicting instructions, sensitive topics, brand constraints | Most skills span multiple types — check all matching rows. Ask 2-3 edge case questions before drafting. ### Step 1b: Extract from Conversation When the user says "turn this into a skill": 1. Review the conversation — tools used, sequence, corrections made 2. Identify what Claude didn't know that the conversation taught it (= the skill's value) 3. Note corrections (= "Common Mistakes" section) 4. Note input/output formats 5. Confirm understanding before drafting The extraction question: **"If a fresh Claude session got this same request, what would it get wrong?"** That's what the skill needs to teach. --- ## Step 2: Write the SKILL.md File ### Skill Structure ``` skill-name/ ├── SKILL.md (required — main instructions) ├── references/ (optional — templates, docs, loaded on demand) ├── scripts/ (optional — executable code for deterministic tasks) └── assets/ (optional — fonts, icons, files used in output) ``` ### Progressive Disclosure | Level | What | When loaded | Size target | |-------|------|-------------|-------------| | **Metadata** | name + description | Always in context | ~100 words | | **SKILL.md file body** | Main instructions | When skill triggers | --skill-path --model --max-iterations 5` **claude.ai:** Test queries mentally, add missing keywords and exclusions. --- ## Step 7: Package and Deliver **With terminal:** `python -m scripts.package_skill ` or `zip -r skill.skill skill-name/` **Without terminal:** Present files individually for download. **Updating:** Preserve original name. Copy to writable location first. Package as `skill.skill`, not `skill-v2.skill`. --- ## Step 8: After Deployment ### Cross-Platform Install ```bash ./scripts/install.sh path/to/my-skill # Auto-detects Claude Code, Cursor, Codex, Gemini ``` Read `references/cross-platform.md` for manual install, legacy format conversion, and platform limitations. ### Track Performance 1. Watch for repeated corrections → gap in the skill 2. Note triggering issues → adjust description 3. Log changes in the Changelog --- ## Available Scripts and Agents ### Scripts (`scripts/`) | Script | Purpose | |--------|---------| | `install.sh` | Auto-detect platforms and install skill to all | | `package_skill.py` | Validate and package as `.skill` zip | | `quick_validate.py` | Check structure and frontmatter | | `run_loop.py` | Automated description optimization loop | | `run_eval.py` | Run single eval iteration | | `improve_description.py` | Generate improved description from failures | | `aggregate_benchmark.py` | Aggregate eval results into benchmark.json | | `generate_report.py` | Generate human-readable eval report | ### Agents (`agents/`) — Claude Code / Cowork only | Agent | Purpose | |-------|---------| | `grader.md` | Grade assertions against skill output — pass/fail per criterion | | `comparator.md` | Blind A/B comparison between two skill versions | | `analyzer.md` | Root-cause analysis — why one version beat another | ### References (`references/`) | File | When to read | |------|-------------| | `gold-standard.md` | Before writing — design principles, quality checklist, 5 anti-patterns | | `patterns.md` | When stuck — 21 patterns including adaptive output, conversation loops, review output, dimensional framework | | `example-skill.md` | Starting simple — complete 35-line meeting-notes skill | | `example-medium-skill.md` | Starting medium — 120-line skill with lookup table and references | | `schemas.md` | Writing evals — JSON schemas for evals, grading, benchmarks | | `cross-platform.md` | Distributing — install per platform, format conversion, limitations | --- ## Edge Cases - **Unknown domain**: If the user wants a skill for a topic you don't know well, research first (web search, MCPs). Don't guess domain conventions. - **Non-English skills**: The SKILL.md body can be any language. Frontmatter fields (name, description) should stay English for cross-platform compatibility. - **Skills needing MCP tools**: Use fully qualified names (`ServerName:tool_name`). Note MCP dependency in description. - **Very large skills**: If approaching 500 lines even after extracting to references, consider splitting into multiple focused skills (see Chunked Skills pattern). ## Common Mistakes 1. **Not eating your own dogfood** — If the skill teaches a practice (changelog, frontmatter, review step), the skill itself should follow it. Users notice hypocrisy immediately. 2. **Structure-first opening** — "Three modes: analyze, explain, optimize" describes how the skill is organized. Nobody cares. Lead with what the user gets: "Paste your post → get a rewritten version." This mistake cascades — if the overview is structural, the whole skill reads like documentation instead of a tool. 3. **Description too narrow** — "Creates meeting notes" misses "clean up my notes", "what were the action items", "summarize this call". Add casual phrasings. 4. **Testing only happy path** — Rich input works, minimal input ("write a postmortem") halluccinates. Always test with both. 5. **Frontmatter without version** — Published skills without `version` can't be tracked by registries or updated safely. 6. **Explaining what Claude already knows** — Don't teach Python syntax in a Python skill. Add YOUR conventions, not general knowledge. 7. **Output template without filled example** — A template with `[placeholders]` shows structure. A filled example shows what the user actually gets. Without one, users don't know if the skill produces three sentences or three paragraphs. 8. **Unprioritized checklist** — 14 checkboxes with equal weight = user does none. Mark the top 2-3: "If you fix nothing else, fix THESE." --- ## Checklist Before Shipping - [ ] Description: specific, third-person, ALWAYS triggers, casual phrasings, DO NOT triggers - [ ] SKILL.md file body under 500 lines - [ ] Detail in reference files (one level deep) - [ ] Consistent terminology, no time-sensitive info - [ ] Data/facts live in ONE place — SKILL.md references, doesn't duplicate refs - [ ] Concrete examples included (at least one edge case) - [ ] Freedom matches fragility, WHY explained - [ ] Transformative skills (rewrite, convert, summarize): has "did the original survive?" verification step - [ ] At least 2–3 realistic tests run (rich AND minimal input) - [ ] Triggering tested with near-miss queries - [ ] Frontmatter: version, author, license - [ ] Changelog started - [ ] Scripts: error handling, install commands, verbose messages - [ ] Skill follows its own advice (dogfood check) --- ## Changelog ### v10.0.0 (2026-04-09) - Added pattern: "Adaptive Output" for input-dependent templates (CSV analyst) - Added pattern: "Conversation Loop" for multi-turn interactive skills (interview coach) - Added pattern: "Review/Audit Output" for skills that judge instead of produce (document reviewer) - Total patterns: 21. Found by: batch test of three skills targeting untested skill types ### v9.0.0 (2026-04-09) - Added pattern: "Dimensional Framework" for subjective domains (tone, style, design) - Added pattern: "User Intent Mapping" for ambiguous input phrases - Added checklist: input-preservation verification for transformative skills - Found by: tone-adapter skill test — first subjective skill revealed all three gaps ### v8.0.0 (2026-04-09) - Starter template examples now require filled output templates, not just placeholders - Added principle #7: "Prioritize checklists" — top 2-3 items must be marked - Added checklist item: no data duplication between SKILL.md and references - Added Common Mistakes #7 (unfilled output template) and #8 (unprioritized checklist) - Found by: linkedin-algorithm skill analysis revealed all three gaps ### v7.0.0 (2026-04-09) - Fixed starter template: opening must lead with user value, not skill structure - Added Common Mistake #2: "Structure-first opening" - Template now shows BAD/GOOD example for overview sentence ### v6.0.0 (2026-04-09) - Added own frontmatter (version, license, last_reviewed) - Added Common Mistakes, Edge Cases, Configuration, Changelog sections - Added casual triggers to description ("help me build a skill", "teach Claude to") - Added scripts/agents inventory tables - Added medium-complexity example (references/example-medium-skill.md) - Fixed terminology: "skill" = concept, "SKILL.md file" = file - Skill now follows all practices it teaches (dogfood compliance) ### v5.0.0 (2026-04-09) - Added cross-platform support (install.sh, references/cross-platform.md) - Added patterns: self-refinement, tunable params, stale detection, safety rails - Extended frontmatter: version, author, license, allowed-tools, last_reviewed ### v4.0.0 (2026-04-09) - Added Edge Case Discovery table with hybrid-skill note - Added minimal input testing guidance - Added Safety Rails and Review Questions patterns ### v3.0.0 (2026-04-09) - Moved pitfalls/patterns to references/patterns.md - Removed duplication between SKILL.md and gold-standard.md - Added concrete test example (meeting-notes) - Added packaging fallback without terminal - Added example-skill.md (35-line complete skill) - Added Size Guide and Changelog pattern ### v1.0.0 (2026-04-09) - Initial public version. claude.ai-first, no personal references. --- ## Learned - Testing with minimal input is as important as rich input — v3 test revealed this - Edge Case Discovery needs hybrid-skill awareness — v4 test revealed this - Skills that teach practices must follow them — v6 self-audit revealed 7 violations - Opening must lead with user value, not skill structure — linkedin skill revealed this - Output templates without filled examples leave users guessing — linkedin analysis revealed this - Unprioritized checklists get ignored — linkedin analysis revealed this - Data in SKILL.md AND refs = duplication that drifts — linkedin analysis revealed this - Subjective domains need dimensional frameworks, not pass/fail checklists — tone-adapter revealed this - Transformative skills need input-preservation checks — tone-adapter revealed this - Ambiguous user input needs intent mapping tables — tone-adapter revealed this - Fixed output templates don't work for data-dependent skills — csv-analyst revealed this - Linear workflows don't model interactive coaching — interview-coach revealed this - Skills that judge need scoring dimensions + finding format, not output templates — document-reviewer revealed this - Each test finds fewer new gaps (3→3→3) — skill is converging but not done --- ## Principle of Lack of Surprise Skills must not contain malware, exploit code, or anything compromising system security. A skill's contents should not surprise the user in their intent. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [Renol1](https://github.com/Renol1) - **Source:** [Renol1/skill-creator-pro](https://github.com/Renol1/skill-creator-pro) - **License:** Apache-2.0 Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-renol1-skill-creator-pro-skill-creator-pro - Seller: https://agentstack.voostack.com/s/renol1 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.