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Frontmatter Parsing

OrganizationPopular
a5c-ai
frontmatter-parsing

YAML frontmatter parsing and manipulation for .planning/ documents. Provides read, write, update, query, and validation operations on frontmatter blocks in GSD markdown artifacts.

Overview

Publishera5c-ai
Repositorybabysitter
Skill namefrontmatter-parsing
Stars
1.8K
Forks
112
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by a5c-ai on GitHub. Read the source before you install it.

Installation

Install the Frontmatter Parsing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/a5c-ai/babysitter/tree/main/library/methodologies/gsd/skills/frontmatter-parsing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/a5c-ai/babysitter.git /tmp/babysitter
mkdir -p .claude/skills
cp -r /tmp/babysitter/library/methodologies/gsd/skills/frontmatter-parsing .claude/skills/frontmatter-parsing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Frontmatter Parsing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Frontmatter Parsing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Frontmatter Parsing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

status: in-progress phase: 72 wave: 1 depends_on: [] files_modified: ["src/auth/oauth.ts", "src/auth/tokens.ts"] created: 2026-03-02 updated: 2026-03-02

Plan content below...


## Capabilities

### 1. Parse Frontmatter

Extract frontmatter from a markdown file into structured data:

```yaml
# Input file: .planning/phase-72/PLAN-1.md
---
status: planned
phase: 72
plan_number: 1
wave: 1
depends_on: []
files_modified:
  - src/auth/oauth.ts
  - src/auth/tokens.ts
  - src/middleware/auth.ts
task_count: 4
created: 2026-03-02
gap_closure: false
---

Parsed result:

json
{
  "status": "planned",
  "phase": 72,
  "plan_number": 1,
  "wave": 1,
  "depends_on": [],
  "files_modified": ["src/auth/oauth.ts", "src/auth/tokens.ts", "src/middleware/auth.ts"],
  "task_count": 4,
  "created": "2026-03-02",
  "gap_closure": false
}

2. Extract Specific Fields

Read individual fields without parsing the entire frontmatter:

get_field(.planning/phase-72/PLAN-1.md, "wave") -> 1
get_field(.planning/phase-72/PLAN-1.md, "status") -> "planned"
get_field(.planning/phase-72/PLAN-1.md, "files_modified") -> ["src/auth/oauth.ts", ...]

3. Update Fields

Update individual frontmatter fields without modifying body content:

update_field(.planning/phase-72/PLAN-1.md, "status", "executed")
update_field(.planning/phase-72/PLAN-1.md, "wave", 2)
update_field(.planning/phase-72/PLAN-1.md, "updated", "2026-03-02")

Uses Edit tool to surgically replace only the target field line.

4. Add New Fields

Add fields to existing frontmatter:

add_field(.planning/phase-72/PLAN-1.md, "executed_at", "2026-03-02T14:30:00Z")
add_field(.planning/phase-72/PLAN-1.md, "executor_agent", "gsd-executor")

Inserts new field before the closing --- delimiter.

5. Remove Fields

Remove fields from frontmatter:

remove_field(.planning/phase-72/PLAN-1.md, "gap_closure")

6. Query Across Files

Find documents matching frontmatter criteria:

query(directory: ".planning/phase-72/", field: "wave", value: 1)
-> [".planning/phase-72/PLAN-1.md", ".planning/phase-72/PLAN-2.md"]

query(directory: ".planning/", field: "status", value: "planned", recursive: true)
-> [".planning/phase-72/PLAN-1.md", ".planning/phase-73/PLAN-1.md"]

Uses Grep to search for field patterns, then validates matches.

7. Validate Frontmatter

Validate frontmatter against document type schema:

yaml
# PLAN.md required fields
required:
  - status       # planned|executing|executed|verified
  - phase        # integer or decimal
  - plan_number  # integer
  - wave         # integer (execution order group)
  - task_count   # integer

# PLAN.md optional fields
optional:
  - depends_on      # array of plan references
  - files_modified  # array of file paths
  - gap_closure     # boolean
  - created         # date
  - updated         # date
  - executed_at     # datetime

Document type schemas:

  • PLAN.md: status, phase, plan_number, wave, task_count
  • SUMMARY.md: phase, status, tasks_completed, commits
  • RESEARCH.md: phase, status, approach_count, recommended
  • STATE.md: last_updated, session_count, current_milestone
  • CONTEXT.md: phase, decisions_count, preferences

8. Batch Operations

Update frontmatter across multiple files:

batch_update(
  files: [".planning/phase-72/PLAN-1.md", ".planning/phase-72/PLAN-2.md"],
  field: "status",
  value: "executed"
)

Tool Use Instructions

Parsing Frontmatter

  1. Use Read to load the target file
  2. Extract content between first --- and second ---
  3. Parse YAML content into key-value pairs
  4. Return structured object

Updating a Field

  1. Use Read to load the file
  2. Locate the target field line within frontmatter
  3. Use Edit with the exact field line as old_string
  4. Replace with new field value line
  5. If field is multi-line (arrays), handle properly

Querying Across Files

  1. Use Glob to find candidate files in the target directory
  2. Use Grep to search for the field pattern (e.g., ^wave: 1$)
  3. For each match, verify it's within frontmatter (not body content)
  4. Return list of matching file paths

Validating Frontmatter

  1. Use Read to load the file
  2. Parse frontmatter
  3. Check required fields exist for the document type
  4. Validate field types (string, integer, array, boolean, date)
  5. Return validation result with any errors

Process Integration

  • plan-phase.js - Read/write plan frontmatter (wave, depends_on, files_modified)
  • execute-phase.js - Update plan status as execution progresses, read wave assignments for parallel execution
  • audit-milestone.js - Query all plans by status for completion checks
  • research-phase.js - Write research frontmatter (approach_count, recommended)
  • discuss-phase.js - Read phase metadata from roadmap sections

Output Format

json
{
  "operation": "parse|get|update|add|remove|query|validate|batch",
  "status": "success|error",
  "file": ".planning/phase-72/PLAN-1.md",
  "field": "status",
  "previousValue": "planned",
  "newValue": "executed",
  "frontmatter": {},
  "queryResults": [],
  "validation": {
    "valid": true,
    "errors": [],
    "warnings": []
  }
}

Configuration

SettingDefaultDescription
frontmatterDelimiter---YAML frontmatter delimiter
strictValidationfalseFail on unknown fields
autoUpdateTimestamptrueAuto-update updated field on writes

Error Handling

ErrorCauseResolution
No frontmatter foundFile missing --- delimitersAdd frontmatter block or skip
YAML parse errorMalformed YAML in frontmatterFix YAML syntax (check indentation, colons, quotes)
Field not foundRequested field not in frontmatterReturn null, optionally add field
Type mismatchField value does not match expected typeCoerce if possible, error if not
Edit collisionField line not unique in fileInclude surrounding context for uniqueness

Constraints

  • Frontmatter must be valid YAML between --- delimiters at the top of the file
  • Field updates must not modify body content below the frontmatter
  • Array fields must use YAML list syntax (either inline [a, b] or block - a\n- b)
  • Date fields must use ISO 8601 format
  • Boolean fields must use true/false (not yes/no)
  • Query operations are read-only; they never modify files
  • Batch operations are atomic per file (each file update succeeds or fails independently)

Frequently asked questions

What does the Frontmatter Parsing AI skill do?

YAML frontmatter parsing and manipulation for .planning/ documents. Provides read, write, update, query, and validation operations on frontmatter blocks in GSD markdown artifacts.

Why use Frontmatter Parsing on TypingMind?

Because you install it once and use it with any model. Frontmatter Parsing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Frontmatter Parsing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/a5c-ai/babysitter/tree/main/library/methodologies/gsd/skills/frontmatter-parsing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Frontmatter Parsing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Frontmatter Parsing?

As many as you like. As long as a model supports skills, you can use Frontmatter Parsing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Frontmatter Parsing AI skill free?

Yes. It is published on GitHub by a5c-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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