Produces an enriched SKILL.md with all six Dojo SkillRegistry.IsValid() fields populated — name, description, tier, agents, tool_dependencies, and trigger phrases — inferred from the existing body without rewriting it. Use when: "normalize this skill", "make this skill dojo-compatible", "import a community skill", "enrich skill frontmatter", "fix skill registry validation", "prepare skill for dojo", "community skill is missing fields".
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SKILL.md
Source instructions · Read-only preview
name
normalize-community-skill
model
sonnet
description
Produces an enriched SKILL.md with all six Dojo SkillRegistry.IsValid() fields populated — name, description, tier, agents, tool_dependencies, and trigger phrases — inferred from the existing body without rewriting it. Use when: "normalize this skill", "make this skill dojo-compatible", "import a community skill", "enrich skill frontmatter", "fix skill registry validation", "prepare skill for dojo", "community skill is missing fields".
license
Complete terms in LICENSE.txt
category
skill-forge
triggers
["normalize this skill","make this skill dojo-compatible","import a community skill","enrich skill frontmatter","fix skill registry validation"]
tier
1
agents
["primary"]
tool_dependencies
["file_system"]
inputs
[{"name":"skill_path","type":"string","description":"Path to the community SKILL.md file to normalize","required":true}]
outputs
[{"name":"normalized_skill","type":"ref","format":"cas-ref","description":"Enriched SKILL.md with all six Dojo SkillRegistry.IsValid() fields populated"}]
Normalize Community Skill
I. Workflow
Step 1: Read and Parse the Input File
Read the SKILL.md file at the provided path. Extract:
The raw YAML frontmatter block (between the --- delimiters)
The markdown body (everything after the second ---)
Handle two common frontmatter layouts found in community skills:
For the nested layout, hoist name and description to the top level. Discard vendor-specific fields (version, author, created_at) — they are not part of the Dojo schema.
Step 2: Extract or Generate Trigger Phrases
Check the description field for an explicit trigger block using the pattern:
Trigger phrases?: (.+)
If found, parse the comma-separated or semicolons-separated list as the trigger array.
If not found, generate triggers from the description's first sentence:
Take the first sentence of the description (up to the first period or newline)
Extract the core verb-object pair: e.g., "Guides creation of release specifications" → "create a release specification"
Generate 2–3 variations using common user phrasings:
Direct imperative: "create a release specification"
Question form: "how do I create a release specification"
Contextual: "writing a release spec for this feature"
Quality gate: Triggers must be natural language phrases a human would type, not YAML keys or function names.
Step 3: Infer Tier
Scan the markdown body for evidence of tool usage and skill invocations. Apply this decision table:
Evidence found in body
Tier
No tool or dependency mentions
1
References file_system, bash, web_tools, or script_execution by name or by action (read/write files, run commands, fetch URLs)
2
Invokes another skill by name (e.g., "use the skill-creation skill") or delegates to a sub-agent
3
Uses 3+ tools, orchestrates multiple agents, or explicitly calls itself a meta-skill
4
When evidence is ambiguous, default to the lower tier. Tier inflation makes the skill harder to route correctly.
Step 4: Extract tool_dependencies
Scan the body for references to any of the following allowlist items:
script_execution — running Python, Node, or other language scripts (distinct from bash)
meta_skill — invokes other skills, orchestration
Map natural language evidence to allowlist items:
Natural language signal
Maps to
"read the file", "write output to", "list directory"
file_system
"run the command", "execute in terminal", "shell script"
bash
"fetch the URL", "HTTP GET", "open the browser"
web_tools
"run the Python script", "execute the Node script"
script_execution
"invoke the X skill", "call the Y workflow"
meta_skill
If no matches are found, set tool_dependencies: []. Do not invent dependencies.
Step 5: Default Agents
If the original frontmatter does not specify an agents field, set:
agents: ["primary"]
This is the Dojo Gateway's default routing target. Only override if the body explicitly names a specific agent (e.g., "This skill is used by the forger agent").
Step 6: Validate Against IsValid()
Before writing, verify the enriched frontmatter satisfies all IsValid() conditions:
name is non-empty string
description is non-empty string
At least one trigger phrase exists (inline in description or as a generated list)
tier is an integer 1–4
agents is a non-empty array
tool_dependencies contains only allowlist values (or is empty array)
If any check fails, diagnose which field is missing and apply the relevant step above again.
Encode trigger phrases inline in the description field using the pattern Trigger phrases: phrase one, phrase two, phrase three. — this is how the Dojo SkillRegistry parses them from the description at load time.
Write the result back to the same file path, or to a new path if the user specified an output location.
If IsValid() would still fail after normalization, report the specific failure and request user input.
II. Best Practices
Preserve the body exactly. The markdown body is the intellectual content of the skill. Only the frontmatter changes. Do not reformat, reorder, or summarize the body.
Prefer inference over defaults. A defaulted tool_dependencies: [] is valid but weak. Spend one pass scanning the body before accepting an empty list.
One trigger phrase is the minimum, three is the target. Single-phrase triggers create brittle routing. Provide phrasings that cover direct, question, and contextual invocation styles.
Tier 2 is the most common correct answer. Most community skills describe a workflow that touches files or runs commands. Tier 1 (pure reasoning) and Tier 4 (orchestration) are less common. When unsure between 1 and 2, check whether the workflow steps reference any tool actions.
Nested metadata is a data loss risk. When hoisting from a metadata: block, log which fields were discarded so the original author can audit the result.
III. Quality Checklist
Before delivering the normalized skill, confirm:
Frontmatter has exactly: name, description, tier, agents, tool_dependencies, license
No vendor-specific fields remain (version, author, created_at)
description ends with Trigger phrases: ... block
Trigger phrases are natural language, not identifiers
tier matches evidence found in the body (not assumed)
tool_dependencies values are all from the allowlist
Markdown body is byte-for-byte identical to the original
IsValid() simulation passes all six checks
Normalization summary has been reported to the user
IV. Related Skills
scan-community-repos - Identifies which skills in a repo need normalization before you run this skill
batch-normalize-and-package - Calls this skill in a loop across an entire repo's skill set
skill-creation - Use when a community skill is so incomplete it needs a full rewrite rather than normalization
skill-maintenance - Use after normalization to rename or refactor skills that don't follow verb-object naming
Output
Enriched SKILL.md written back to the same path (or a specified output path) with all six required frontmatter fields populated
Normalization summary reported to the user: which fields were added, what evidence supported each inference, and whether IsValid() passes
Markdown body is byte-for-byte identical to the original — only frontmatter changes
Examples
Scenario 1: "Import this skill from alirezarezvani/claude-skills — it only has name and description" → Read the file, extract trigger phrases from the description, scan the body for tool usage to infer tier and tool_dependencies, set agents to ["primary"], reconstruct frontmatter, write back, report normalization summary.
Scenario 2: "A skill is stuck in 'pending' state in the SkillRegistry" → Read the skill, run IsValid() simulation to identify the failing check, apply only the steps needed to fix the missing fields, write and re-validate.
Edge Cases
Skill has a metadata: nested block instead of flat frontmatter — hoist name and description to the top level, discard vendor-specific fields (version, author, created_at), log which fields were dropped so the original author can audit
Skill body is empty or under 100 bytes — this is a stub, not a normalizable skill; report it as incompatible and request the full file from the original author
Skill is missing name or description — this is an "incompatible" classification, not a normalization case; do not attempt to infer these fields, redirect to skill-creation if a rewrite is needed
Anti-Patterns
Inventing fields instead of inferring: Adding tool_dependencies: ["bash"] because it seems likely, without evidence in the body — every added field must be traceable to specific language in the skill
Rewriting the body: Reformatting, summarizing, or reordering the markdown body violates the principle that normalization is structural, not editorial — only frontmatter changes
Defaulting tier to 2 without checking: Tier 2 is the most common correct answer but not the automatic answer; scan the body for tool action evidence before assigning
