mikeparcewski/wicked-garden-wickedizer
skills/wickedizer/SKILL.md
Writing humanizer / AI-tell remover (the team's house writing-cleanup skill). Use when writing, rewriting, or humanizing content: strips AI tells (fluff, hedging, promo language, chatbot artifacts) while preserving meaning, and aligns output to a direct, practical, action-oriented team voice. Use when: humanizing AI-sounding prose, "remove AI tells", "make this sound human", drafting a PR description or commit message, or aligning written output to team voice. Aliases: humanize, humanizer, dejargonize, writing-cleanup.
$ install --global
skillsauthnpx skillsauth add mikeparcewski/wicked-garden wicked-garden-wickedizerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Jul 8, 2026, 7:19 AM131.8s9 files scanned
SKILL.md
- name:
- wicked-garden-wickedizer
- context:
- fork
- description:
- |
- Use when writing, rewriting, or humanizing content:
- strips AI tells (fluff, hedging,
- Use when:
- humanizing AI-sounding prose, "remove AI tells", "make this sound human",
- Aliases:
- humanize, humanizer, dejargonize, writing-cleanup.
- portability:
- portable
- status:
- experimental
- phase_relevance:
- ["*"]
- archetype_relevance:
- ["*"]
Wickedizer: Clear, Credible, Human Writing
You are rewriting for trust and clarity, not for "vibes."
Your job:
- Remove AI-generated tells (fluff, hedging, promo language, chatbot artifacts)
- Preserve meaning, constraints, and domain terms
- Align to team voice: direct, pragmatic, outcome-driven
- NEVER invent facts, citations, names, or numbers
When This Skill Applies
Invoke automatically when:
- Writing or rewriting prose (emails, memos, exec summaries, PRDs, POVs)
- Producing slide text or bullets
- Writing work items (epics/stories/tasks/bugs in Jira, Linear, GitHub Issues, Azure DevOps, etc.)
- Drafting PR descriptions, commit messages, ADRs/RFCs, design docs
- Writing code comments, docstrings, READMEs, runbooks
Non-Negotiables
- Do not change meaning - Preserve commitments, constraints, scope, decisions
- Do not add facts - No invented stats, quotes, names, dates, attributions
- Keep useful structure - Headings, bullets, tables that aid scanning
- Keep terminology stable - Same concept = same name everywhere
- Match medium + audience - Slack ≠ PRD ≠ exec memo
Operating Process
Step 0: Classify the Artifact
Pick ONE: exec-summary | technical-doc | prd-requirements | work-item | email-comms | slide-bullets | code-comments | mixed
→ See refs/artifact-types.md for mode-specific rules.
Step 1: Extract Immutables
Identify and preserve:
- Facts, dates, numbers, names, owners
- Decisions, constraints, scope boundaries
- Domain terms and key nouns
- Acceptance criteria / definitions of done
Step 2: De-AI Pass
Remove:
- Chatbot artifacts ("hope this helps", validation filler)
- Promotional language and significance inflation
- Hedge stacking ("could potentially possibly")
- Vague attributions ("experts say")
- Cadence uniformity (same rhythm every sentence)
→ See refs/de-ai-pass.md for detection patterns.
Step 3: Clarity Pass
- Convert abstract nouns into actions
- Replace vague verbs with specific ones
- Ensure reader can answer: What changed? Why? Who owns it? What's next?
Step 4: Voice Pass
Apply team voice defaults:
- Assertive opening sentence with limited bold
- Scannable structure: short paragraphs, real bullets
- Consistent terms (no synonym cycling)
- Close with next steps / decision points
→ See refs/voice-profile.md for full voice guide.
Step 5: Trust Check
Before output, verify:
- [ ] No added facts, citations, names, or stats
- [ ] Meaning and constraints preserved
- [ ] Structure appropriate for medium
- [ ] Output contains at least one: decision, tradeoff, next step, open question, or measurable outcome
→ See refs/failure-modes.md for common problems.
Quick Reference: What to Remove
| Pattern | Fix | |---------|-----| | "Additionally / Furthermore / Moreover" spam | Delete most transitions | | "Pivotal / transformative / game-changing" | Remove or prove | | "Not just X, but Y" scaffolding | State directly | | Rule-of-three when 2 is correct | Use real count | | "Experts agree" without naming | Cite or delete | | "Hope this helps!" | Delete |
Quick Reference: What to Keep
| Element | When | |---------|------| | Bullets and lists | When they aid scanning | | Technical terms | When precise | | Numbers and dates | Always (don't generalize) | | Named constraints | Always | | Scope boundaries | Always |
Output Format
Return:
- The rewritten content
- Optional Change Log (3-8 bullets) if helpful:
- Removed fluff
- Clarified claims
- Preserved constraints
- Adjusted structure
Flexibility Note
These are defaults, not laws. Override when:
- Source material is intentionally casual or formal
- Audience expects different tone (legal, marketing)
- Context requires adaptation
The goal is credible and specific, not rigid adherence. Situation matters more than rules.
References
Core Process:
- Voice Profile - Tone, sentence structure, ending strong
- De-AI Pass - Detecting and removing AI tells
- Artifact Types - Mode-specific guidance
Principles:
- Structure - Document architecture and organization
- Complexity - KISS, YAGNI, DRY for writing
- Patterns - Reusable templates and structures
Quality:
- Review - Validation checklists by type
- Failure Modes - Common problems and fixes
Related Skills
mikeparcewski/wicked-garden-engineering-conformance-reviewer
development
VerifiedTrustedCommunity
Pattern-conformance agent-half: evaluates a produced artifact or diff against a set of architectural/design pattern rules from the conformance-rule store (wicked_governance schema). Returns structured findings with rule ID, severity, and rationale — the deterministic half (mechanical rule recall) is done by the guard pipeline; this is the semantic evaluation step. Triggered by: the guard_pipeline `outgov_pattern` check (session-close), or explicitly by an engineering review when WICKED_OUTGOV_RULES_DIR is populated. NOT a replacement for the full `engineering` review skill — focuses only on conformance to stored Pattern rules; architecture and code-quality checks live in the `engineering` skill. Semantic evaluation reuses `wicked-garden-qe-semantic-reviewer` as the designated agent-half evaluator (per garden#983 spec). This skill is the orchestrating wrapper that loads applicable Pattern rules and delegates the per-rule semantic judgment to qe-semantic-reviewer.
8SKILL.mdUpdated Jul 22, 2026
mikeparcewski/wicked-garden-domain
tools
VerifiedTrustedCommunity
The FOUNDATIONAL domain-model capability: extract a codebase's domain — testable business rules (with confidence + provenance), entities, requirements — as a schema-conformant model on the estate graph. The workers annotate the store; wicked-core reads it and builds the requirements graph, coverage-gating fail-closed. Steers three fork workers. A shared substrate, not a modernization tool. The `modernize` archetype DERIVES from it; build / migrate / review / specify / explore consume the SAME domain model — none OWN it. Understanding a codebase's domain is upstream of almost everything else garden does. Use when: "extract the business rules / domain model from this codebase", "build a requirements graph from the code", "what does this system actually require", "reverse-engineer the domain before we build/port/migrate". Works on ANY codebase (modern or legacy) — the value is the domain model, not the porting. NOT the code transform itself (that is the archetype consuming this model). This skill produces the DOMAIN MODEL, not new code.
8SKILL.mdUpdated Jul 15, 2026
mikeparcewski/wicked-garden-domain-modeler
development
VerifiedTrustedCommunity
Domain-graph fork worker for the modernize archetype. Groups the estate's Louvain communities into business domains, attaches each requirement to its cluster (advisory cluster_id provenance), and invokes wicked-core's domain-graph build (which reads the annotated estate store, recomputes coverage fail-closed, and builds the requirements graph) — then validates core's output against the vendored schema. Use when: dispatched by wicked-garden-domain after rule extraction to turn a flat rule set into cluster-keyed domains; "group these into domains", "build the requirements graph", "translate clusters into a domain model". NOT for mining the rules themselves (that is domain-extractor) or threat-modeling (that is domain-coverage).
8SKILL.mdUpdated Jul 15, 2026
mikeparcewski/wicked-garden-domain-extractor
tools
VerifiedTrustedCommunity
Rule-extraction fork worker for the FOUNDATIONAL domain-model capability. Mines testable business rules from a codebase — each with a numeric confidence and a provenance{source, ref, source_kinds} — and annotates them into the estate store so wicked-core can build the domain-model requirements graph (coverage-gated). This is a substrate, not a modernization tool: the `modernize` archetype DERIVES from it, and build / migrate / review / specify / explore can consume the same domain model — none OWN it. Use when: dispatched by wicked-garden-domain to mine the business_rules of a codebase (or a module); "extract the domain rules", "what does this system require", building the requirements half of a domain model. NOT for grouping into domains (that is domain-modeler) or judging coverage (that is domain-coverage — a seat-distinct evaluator).
8SKILL.mdUpdated Jul 15, 2026