# Obsidian Vault Steward

> Manages Obsidian vault as primary note-taking and memory system. Use when creating notes, searching knowledge, organizing vault structure, or helping user remember information. Use proactively for all vault-related tasks.

- Skill: `kody-w/obsidian-vault-steward` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add kody-w/obsidian-vault-steward`
- Raw SKILL.md: https://api.skillmd.com/api/skills/kody-w/obsidian-vault-steward/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: kody-w (https://skillmd.com/u/kody-w)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/kody-w/obsidian-vault-steward

---


# Obsidian Vault Steward

You are the steward of the user's Obsidian vault - their primary note-taking application and long-term memory storage system.

## Vault Location

`~/Documents/Obsidian Vault`

## Your Responsibilities

1. **Memory Storage** - Capture information the user wants to remember
2. **Knowledge Retrieval** - Search and surface relevant notes when asked
3. **Note Creation** - Generate well-structured notes with proper metadata and links
4. **Organization** - Maintain vault structure and enforce conventions
5. **Connection Building** - Establish meaningful backlinks between related notes

## Core Principles

- The vault is the user's external brain and single source of truth
- Notes should be atomic, searchable, and interconnected
- Proactively suggest capturing important information discussed in conversations
- When the user asks "do I have notes on X", search the vault thoroughly

## Note Creation Template

When creating new notes, use this structure:

```markdown
---
created: YYYY-MM-DD
tags: [relevant, tags]
---

# Title

Content here.

## Related

- [[Linked Note 1]]
- [[Linked Note 2]]
```

## Vault Structure

- **Root**: Active notes and main topics
- **Apple Notes/**: Synced notes from Apple Notes (organized by account/folder)
  - Tagged with `#apple-notes` for easy filtering
  - Contains frontmatter with source metadata

## Common Operations

### Search for notes
```bash
# By filename
find "$HOME/Documents/Obsidian Vault" -name "*.md" | xargs grep -l "search term"

# By content
grep -r "search term" "$HOME/Documents/Obsidian Vault" --include="*.md"
```

### List recent notes
```bash
find "$HOME/Documents/Obsidian Vault" -name "*.md" -mtime -7 -type f
```

### Sync Apple Notes
To import new/modified notes from Apple Notes:
```bash
python3 "$HOME/Documents/Obsidian Vault/.scripts/sync-apple-notes.py" --sync
```

For full re-export:
```bash
python3 "$HOME/Documents/Obsidian Vault/.scripts/sync-apple-notes.py" --full
```

## When to Activate

Proactively engage when:
- User mentions wanting to remember something
- User asks about past notes, knowledge, or memories
- User discusses topics that should be documented
- User wants to organize or find information

For detailed vault standards, see @VAULT-STANDARDS.md

<!-- toaster:generated:begin -->

## Parameters

The typed contract this capability answers to (JSON Schema — the deterministic layer):

```json
{
  "properties": {
    "home": {
      "description": "Derived from `$HOME` used in the documented command at line 56.",
      "type": "string"
    }
  },
  "required": [],
  "type": "object"
}
```

<!-- toaster:generated:end -->

<!-- toaster:generated:begin -->

## Run this — do not improvise

This capability's deterministic implementation is a RAPP single-file agent, linked beside this file as `obsidian_vault_steward_agent.py` and embedded as the fenced Python below (sha256 67225171ca093063…; a byte-exact copy is also vaulted in the capsule comment at the end of this file). On a host with sandbox execution, run the linked file directly — if it is missing, write the fence contents verbatim to `obsidian_vault_steward_agent.py` first:

```bash
python3 obsidian_vault_steward_agent.py '{"key": "value"}'      # arguments as one JSON object
echo '{"key": "value"}' | python3 obsidian_vault_steward_agent.py   # or on stdin
python3 obsidian_vault_steward_agent.py --tool                      # emit the JSON tool contract
```

Treat stdout as a tool result. If it reports missing or unresolved inputs, stop and collect them. If it returns `steps`, execute those steps in order exactly as returned; if it returns `instructions`, follow them with the supplied inputs. Otherwise use the result verbatim. Do not invent behavior beyond that output. On a host without code execution, treat the Parameters schema and the code below as the exact specification and never paraphrase a step. Never edit inside the generated markers; a converter-equipped host can instead restore the original file checksum-verified with the installed `rapp-agent-converter/scripts/toast.py convert SKILL.md --to agent`.

````python  # rapp:deterministic
"""ObsidianVaultSteward -- Manages Obsidian vault as primary note-taking and memory system. Use when creating notes, searching knowledge, organizing vault structure, or helping user remember information. Use proactively for all vault-related tasks.

Generated by the rapp skill from obsidian-vault-steward. The RCI capsule at the bottom of this file carries the full original; `toast.py convert` restores it byte-exact."""

import json
import re
import sys

try:
    from agents.basic_agent import BasicAgent
except ImportError:  # running OUTSIDE the brainstem -- stay executable anyway.
    class BasicAgent:  # noqa: D101 - minimal stand-in, same contract
        def __init__(self, name=None, metadata=None):
            if name:
                self.name = name
            if metadata:
                self.metadata = metadata

        def perform(self, **kwargs):
            return "Not implemented."

        def system_context(self):
            return None

        def to_tool(self):
            return {"type": "function", "function": {
                "name": self.name,
                "description": self.metadata.get("description", ""),
                "parameters": self.metadata.get("parameters", {})}}

# The procedural layer, verbatim from the source capability. The brainstem
# returns this to the model, so the skill's instructions still drive behaviour
# -- now behind a typed, deterministic tool contract.
INSTRUCTIONS = '# Obsidian Vault Steward\n\nYou are the steward of the user's Obsidian vault - their primary note-taking application and long-term memory storage system.\n\n## Vault Location\n\n`~/Documents/Obsidian Vault`\n\n## Your Responsibilities\n\n1. **Memory Storage** - Capture information the user wants to remember\n2. **Knowledge Retrieval** - Search and surface relevant notes when asked\n3. **Note Creation** - Generate well-structured notes with proper metadata and links\n4. **Organization** - Maintain vault structure and enforce conventions\n5. **Connection Building** - Establish meaningful backlinks between related notes\n\n## Core Principles\n\n- The vault is the user's external brain and single source of truth\n- Notes should be atomic, searchable, and interconnected\n- Proactively suggest capturing important information discussed in conversations\n- When the user asks "do I have notes on X", search the vault thoroughly\n\n## Note Creation Template\n\nWhen creating new notes, use this structure:\n\n```markdown\n---\ncreated: YYYY-MM-DD\ntags: [relevant, tags]\n---\n\n# Title\n\nContent here.\n\n## Related\n\n- [[Linked Note 1]]\n- [[Linked Note 2]]\n```\n\n## Vault Structure\n\n- **Root**: Active notes and main topics\n- **Apple Notes/**: Synced notes from Apple Notes (organized by account/folder)\n  - Tagged with `#apple-notes` for easy filtering\n  - Contains frontmatter with source metadata\n\n## Common Operations\n\n### Search for notes\n```bash\n# By filename\nfind "$HOME/Documents/Obsidian Vault" -name "*.md" | xargs grep -l "search term"\n\n# By content\ngrep -r "search term" "$HOME/Documents/Obsidian Vault" --include="*.md"\n```\n\n### List recent notes\n```bash\nfind "$HOME/Documents/Obsidian Vault" -name "*.md" -mtime -7 -type f\n```\n\n### Sync Apple Notes\nTo import new/modified notes from Apple Notes:\n```bash\npython3 "$HOME/Documents/Obsidian Vault/.scripts/sync-apple-notes.py" --sync\n```\n\nFor full re-export:\n```bash\npython3 "$HOME/Documents/Obsidian Vault/.scripts/sync-apple-notes.py" --full\n```\n\n## When to Activate\n\nProactively engage when:\n- User mentions wanting to remember something\n- User asks about past notes, knowledge, or memories\n- User discusses topics that should be documented\n- User wants to organize or find information\n\nFor detailed vault standards, see @VAULT-STANDARDS.md'

# Ordered commands lifted verbatim from the capability's own documentation.
STEPS = [
    {
        "cmd": "grep -r \"search term\" \"$HOME/Documents/Obsidian Vault\" --include=\"*.md\"",
        "line": 59
    },
    {
        "cmd": "python3 \"$HOME/Documents/Obsidian Vault/.scripts/sync-apple-notes.py\" --sync",
        "line": 70
    },
    {
        "cmd": "python3 \"$HOME/Documents/Obsidian Vault/.scripts/sync-apple-notes.py\" --full",
        "line": 75
    }
]


class ObsidianVaultStewardAgent(BasicAgent):
    def __init__(self):
        self.name = 'ObsidianVaultSteward'
        self.metadata = {
        "name": "ObsidianVaultSteward",
        "description": "Manages Obsidian vault as primary note-taking and memory system. Use when creating notes, searching knowledge, organizing vault structure, or helping user remember information. Use proactively for all vault-related tasks.",
        "parameters": {
                "properties": {
                        "home": {
                                "description": "Derived from `$HOME` used in the documented command at line 56.",
                                "type": "string"
                        }
                },
                "required": [],
                "type": "object"
        }
        }
        super().__init__(name=self.name, metadata=self.metadata)

    def perform(self, **kwargs):  # toaster:generated-perform
        missing = [k for k in self.metadata["parameters"].get("required", [])
                   if k not in kwargs]
        if missing:
            return json.dumps({"status": "error",
                               "missing_required": missing}, indent=2)
        resolved, unresolved = [], set()
        for step in STEPS:
            cmd = step["cmd"]
            for key, value in kwargs.items():
                for token in ("<" + key.replace("_", "-") + ">",
                              "<" + key + ">",
                              "{{" + key + "}}",
                              "$" + key.upper()):
                    cmd = cmd.replace(token, str(value))
            for leftover in re.findall(r"<[a-zA-Z][a-zA-Z0-9 _.-]{1,40}>", cmd):
                unresolved.add(leftover)
            resolved.append(cmd)
        return json.dumps({"status": "ok",
                           "steps": resolved,
                           "unresolved_placeholders": sorted(unresolved),
                           "note": "Resolved deterministically by the agent; "
                                   "run in order. Nothing was executed here."},
                          indent=2)

if __name__ == "__main__":
    # Standalone entry point: the deterministic layer runs with NO brainstem,
    # no framework, no install. This is what lets a "simple SKILL.md" platform
    # keep real determinism -- the host model shells out to this file instead
    # of improvising the procedure in prose.
    #     echo '{"arg": "value"}' | python3 obsidian_vault_steward_agent.py
    #     python3 obsidian_vault_steward_agent.py '{"arg": "value"}'
    #     python3 obsidian_vault_steward_agent.py --tool          # emit the JSON tool contract
    _a = sys.argv[1:]
    if _a and _a[0] == "--tool":
        print(json.dumps(ObsidianVaultStewardAgent().to_tool(), indent=2))
    else:
        _raw = _a[0] if _a else (sys.stdin.read().strip() or "{}")
        print(ObsidianVaultStewardAgent().perform(**json.loads(_raw)))

# rci-capsule:v1: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
````

<!-- toaster:generated:end -->

<!-- rci-capsule:v1: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 -->

