Install
$ agentstack add skill-bruno-cunha-souza-valarmindskills-skill-creator ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Skill Creator
Meta-skill that scaffolds new skills for the ValarMindSkills repository following project conventions.
When to Use
- User asks to create, scaffold, or add a new skill to this repository
- User wants to convert an existing prompt, workflow, or playbook into a reusable skill
- User wants to refactor a long
SKILL.mdinto a skill plusreferences/files
Do not use when
- User wants to edit an existing skill — open the file directly
- User wants to install skills — use
scripts/install-plugin-claude.shorscripts/install-antigravity.sh - User wants to rebuild the plugin itself (
.claude-plugin/,hooks/) — that is a separate concern; this skill scaffolds skill content only
Goal
Produce a complete, idiomatic skill under skills// that:
- Follows one of the five project archetypes (Procedural, Lifecycle, Expert Profile, Best Practices, Reference)
- Uses only the project's frontmatter convention (
name,description,source) - Passes every item of [
references/CHECKLIST.md](references/CHECKLIST.md) before the turn ends - Has its prompt content audited by
@prompt-engineeringfor clarity, anti-hallucination, and token economy before being reported to the user
This skill scaffolds the structure; @prompt-engineering hardens the content. The two are paired: scaffold first (Steps 1–8), audit the prompt (Step 9), then validate structure (Step 10) and report (Step 11).
Inputs you must collect before starting
| Input | Required | How to obtain | | :--- | :--- | :--- | | Purpose | Yes | Ask: "What problem will this skill solve?" | | Trigger phrases | Yes | Ask: "What will the user say or type to invoke it?" Collect PT and EN variants when applicable | | Archetype fit | Yes | Derive from purpose using the decision matrix in [references/ARCHETYPES.md](references/ARCHETYPES.md) | | Canonical output | Only if Procedural / Best Practices | Ask for an example of the ideal output; it will seed EXAMPLE.md | | External source | No | URL or credit if the skill is based on public material |
If any required input is missing, stop and ask before scaffolding.
Procedure
Step 1 — Discovery
Run, in order:
ls skills/ # existing slugs — avoid collisions
cat README.md # project format and contribution rules
cat CLAUDE.md 2>/dev/null # agent-facing instructions
Pick one or two existing skills closest in purpose to the new one and read their SKILL.md. Cite their paths back to the user as reference models.
Step 2 — Archetype decision
Apply the decision matrix from [references/ARCHETYPES.md](references/ARCHETYPES.md):
| Purpose | Archetype | Canonical example | | :--- | :--- | :--- | | Deterministic artifact from a trigger | Procedural | skills/github-commit/ | | Multi-phase audit or hardening with branches | Lifecycle | skills/clean-code/ | | Persona with capabilities and traits | Expert Profile | skills/code-review/ | | Principles, heuristics, and worked examples | Best Practices | skills/api-security-best-practices/ | | Catalog, schema, or spec reference | Reference | skills/obsidian-bases/ |
Pick exactly one archetype and state it explicitly before moving on. Full signals and counter-signals live in [references/ARCHETYPES.md](references/ARCHETYPES.md).
Step 3 — Naming and slug
- Use kebab-case, lowercase,
≤ 64characters - Must NOT contain
anthropicorclaude(Anthropic spec) - Prefer a noun or noun-phrase (
code-review, notreview-the-code) - Verify no collision with
ls skills/
If the requested name violates any rule, stop and propose an alternative before scaffolding.
Step 4 — Scaffold files
Create:
skills//
└── SKILL.md
Add the rest conditionally:
references/FILE.md— one or more — when the body would exceed ~400 lines, when there is multi-framework or multi-language branching, or when there is a dense catalog to document. File names areUPPERCASE.md.EXAMPLE.md— when the output format is variable, when a canonical example is pedagogical, or when the archetype is Procedural with nontrivial output.- Never create a
scripts/folder inside the skill — it is not a project convention. See [references/STRUCTURE.md](references/STRUCTURE.md#anti-patterns).
Step 5 — Write the frontmatter
Use the minimal project frontmatter:
---
name:
description: ""
source: ValarMindSkills
---
Rules enforced by [references/FRONTMATTER.md](references/FRONTMATTER.md):
descriptionwritten in the third person ("Use when the user asks...")- Include explicit trigger phrases (PT and EN when the user writes in both)
- Do not invent fields. Anthropic-official fields like
when_to_use,allowed-tools,disable-model-invocationare listed for reference only and are not used in this repository
Step 6 — Write the body
Follow the archetype skeleton from [references/ARCHETYPES.md](references/ARCHETYPES.md). Target sections, in order:
- Procedural: Goal → Inputs → Procedure (numbered steps) → Constraints → Output format → Example request
- Lifecycle: When to Use → Prerequisites → Phase 0…N → Constraints
- Expert Profile: Use when / Do not use → Expert Purpose → Capabilities → Behavioral Traits → Knowledge Base → Response Approach → Example Interactions
- Best Practices: When to Use → Core Concepts → Detailed Topics → Practical Guidance → Examples → Guidelines
- Reference: Purpose → Schema → Themed subsections → Tables → Code/YAML literals → Pointers to
references/
For every axis the caller might want to override via extra prompt text (/my-skill in Spanish, /my-skill verbose), add an explicit row in Inputs with Required: No and a default. Rules that must survive adversarial extras go in Constraints using absolute verbs (Never, Must not). See [references/EXTRA_INSTRUCTIONS.md](references/EXTRA_INSTRUCTIONS.md) for the full pattern catalog.
Hard budget: keep the SKILL.md body under 500 lines. If you exceed it, move detail into references/.
Step 7 — Write references/ (if applicable)
One topic per file. Open each reference with a one-line companion banner:
> Reference companion for the [](../SKILL.md) skill.
Link from SKILL.md using relative paths: [label](references/FILE.md). Do not link across skills with relative paths — use @ text references instead.
Step 8 — Write EXAMPLE.md (if applicable)
Keep it minimal and self-contained: one canonical input and one canonical output, no more. See skills/github-commit/EXAMPLE.md and skills/github-pr-review/EXAMPLE.md for the two dominant shapes (code blocks vs. worked document).
Step 9 — Audit prompt content with @prompt-engineering
Mandatory step before validation. The previous steps produce a structurally correct skill — @prompt-engineering hardens the content of the prompt against the failure modes that scaffolding alone does not catch (vague success criteria, hallucination floors missing, silent omissions, redundant pleasantries, safety rules weakened during edits).
Run the audit in this order:
- Treat the just-written
SKILL.mdas input. - Classify the prompt:
role: skill,use case: skill. The@prompt-engineeringskill loads its [USECASES.md §1](../prompt-engineering/references/USECASES.md) skeleton and findings catalog automatically when this classification is set. - Walk Phase 0–6 of
@prompt-engineeringagainst the draft. Every Critical and Major finding is a blocker; Minor findings are reported but do not block. - Apply the proposed rewrite (Block 3 of the audit output) only with user approval.
@prompt-engineeringis read-only; this skill mediates the apply step. - Record the post-audit metrics in the Step 11 report: clarity score, anti-hallucination coverage, token delta, overall risk tag.
If @prompt-engineering is unavailable (skill not installed in the current session), skip this step and surface the gap explicitly in Step 11 (prompt audit: skipped — @prompt-engineering not available). Do not silently omit the audit.
Step 10 — Validation
Walk through [references/CHECKLIST.md](references/CHECKLIST.md) item by item. Do not skip. If any item fails, fix it before reporting to the user. The "Prompt audit" section of the checklist verifies Step 9 ran and its findings were addressed.
Quick YAML sanity check:
python3 -c "import yaml; d=open('skills//SKILL.md').read().split('---'); yaml.safe_load(d[1])"
Step 11 — Report to user
Deliver the report in the format below, then suggest updating README.md to add the new skill to the "Available skills" table.
Constraints
- Markdown and YAML only — no executable scripts inside
skills// - All prose in English (per README §Contributing). Only trigger phrases in the
descriptionmay be bilingual description≤ 1024 characters,name≤ 64 characters, body Critical/Major findings unresolved`.- Never commit without the user's explicit approval
Output format
After creation, report exactly:
Created skills//
SKILL.md ( lines, archetype: )
references/.md ( lines) [if any]
EXAMPLE.md ( lines) [if any]
Frontmatter:
name:
source: ValarMindSkills
description:
Prompt audit (@prompt-engineering):
clarity: /8 axes pass
anti-hallucination: /12 strategies covered
token delta:
risk tag (overall): SAFE | REVIEW | BREAKING
blocking findings: (resolved before report)
Install:
bash scripts/install-plugin-claude.sh # Claude Code CLI (plugin valarmindskills@valarmindskills)
bash scripts/install-all.sh # Claude Code + Antigravity
Invoke:
/valarmindskills:
Then offer to update README.md and, if the user agrees, add the new row to the "Available skills" table.
Example request
See [EXAMPLE.md](EXAMPLE.md) for a worked end-to-end creation of hello-skill.
Typical activating phrases:
- "Crie uma skill nova para auditar docker-compose"
- "Scaffold a skill that generates OpenAPI specs from Go handlers"
- "Adicionar uma skill para revisar migrations"
- "Help me design a SKILL.md for a changelog summarizer"
Related Skills
@prompt-engineering— paired sibling. Audits the prompt content of the just-scaffoldedSKILL.md(Step 9) before validation. Scaffold first, audit second.
References
- [ARCHETYPES](references/ARCHETYPES.md) — the five archetypes, decision matrix, and canonical examples
- [FRONTMATTER](references/FRONTMATTER.md) — YAML field reference (project + official Anthropic comparison)
- [STRUCTURE](references/STRUCTURE.md) — directory layout, naming rules, progressive disclosure, anti-patterns
- [EXTRAINSTRUCTIONS](references/EXTRAINSTRUCTIONS.md) — how skills consume free-form overrides after invocation, precedence rules, Inputs/Constraints patterns
- [CHECKLIST](references/CHECKLIST.md) — final validation before handing the skill back to the user (includes the prompt-audit checklist section)
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: Bruno-Cunha-Souza
- Source: Bruno-Cunha-Souza/ValarMindSkills
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.