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Seed

OrganizationPopular
agenticnotetaking
seed

Add a source file to the processing queue. Checks for duplicates, creates archive folder, moves source from inbox, creates extract task, and updates queue. Triggers on "/seed", "/seed [file]", "queue this for processing".

Overview

Publisheragenticnotetaking
Repositoryarscontexta
Skill nameseed
Stars
3.5K
Forks
226
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by agenticnotetaking on GitHub. Read the source before you install it.

Installation

Install the Seed 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.
  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/agenticnotetaking/arscontexta.git /tmp/arscontexta
mkdir -p .claude/skills
cp -r /tmp/arscontexta/skill-sources/seed .claude/skills/seed
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Seed 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 Seed 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 Seed 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.

EXECUTE NOW

Target: $ARGUMENTS

The target MUST be a file path. If no target provided, list {DOMAIN:inbox}/ contents and ask which to seed.

Step 0: Read Vocabulary

Read ops/derivation-manifest.md (or fall back to ops/derivation.md) for domain vocabulary mapping. All output must use domain-native terms. If neither file exists, use universal terms.

START NOW. Seed the source file into the processing queue.


Step 1: Validate Source

Confirm the target file exists. If it does not, check common locations:

  • {DOMAIN:inbox}/{filename}
  • Subdirectories of {DOMAIN:inbox}/

If the file cannot be found, report error and stop:

ERROR: Source file not found: {path}
Checked: {locations checked}

Read the file to understand:

  • Content type: what kind of material is this? (research article, documentation, transcript, etc.)
  • Size: line count (affects chunking decisions in /reduce)
  • Format: markdown, plain text, structured data

Step 2: Duplicate Detection

Check if this source has already been processed. Two levels of detection:

2a. Filename Match

Search the queue file and archive folders for matching source names:

bash
SOURCE_NAME=$(basename "$FILE" .md | tr ' ' '-' | tr '[:upper:]' '[:lower:]')

# Check queue for existing entry
# Search in ops/queue.yaml, ops/queue/queue.yaml, or ops/queue/queue.json
grep -l "$SOURCE_NAME" ops/queue*.yaml ops/queue/*.yaml ops/queue/*.json 2>/dev/null

# Check archive folders
ls -d ops/queue/archive/*-${SOURCE_NAME}* 2>/dev/null

2b. Content Similarity (if semantic search available)

If semantic search is available (qmd MCP tools or CLI), check for content overlap:

mcp__qmd__search query="claims from {source filename}" limit=5

Or via keyword search in the {DOMAIN:notes}/ directory:

bash
grep -rl "{key terms from source title}" {DOMAIN:notes}/ 2>/dev/null | head -5

2c. Report Duplicates

If either check finds a match:

  • Show what was found (filename match or content overlap)
  • Ask: "This source may have been processed before. Proceed anyway? (y/n)"
  • If the user declines, stop cleanly
  • If the user confirms (or no duplicate found), continue

Step 3: Create Archive Structure

Create the archive folder. The date-prefixed folder name ensures uniqueness.

bash
DATE=$(date -u +"%Y-%m-%d")
SOURCE_BASENAME=$(basename "$FILE" .md | tr ' ' '-' | tr '[:upper:]' '[:lower:]')
ARCHIVE_DIR="ops/queue/archive/${DATE}-${SOURCE_BASENAME}"
mkdir -p "$ARCHIVE_DIR"

The archive folder serves two purposes:

  1. Permanent home for the source file (moved from {DOMAIN:inbox})
  2. Destination for task files after batch completion (/archive-batch moves them here)

Step 4: Move Source to Archive

Move the source file from its current location to the archive folder. This is the claiming step — once moved, the source is owned by this processing batch.

{DOMAIN:inbox} sources get moved:

bash
if [[ "$FILE" == *"{DOMAIN:inbox}"* ]] || [[ "$FILE" == *"inbox"* ]]; then
  mv "$FILE" "$ARCHIVE_DIR/"
  FINAL_SOURCE="$ARCHIVE_DIR/$(basename "$FILE")"
fi

Sources outside {DOMAIN:inbox} stay in place:

bash
# Living docs (like configuration files) stay where they are
# Archive folder is still created for task files
FINAL_SOURCE="$FILE"

Use $FINAL_SOURCE in the task file — this is the path all downstream phases reference.

Why move immediately: All references (task files, {DOMAIN:note_plural}' Source footers) use the final archived path from the start. No path updates needed later. If it is in {DOMAIN:inbox}, it is unclaimed. Claimed sources live in archive.

Step 5: Determine Claim Numbering

Find the highest existing claim number across the queue and archive to ensure globally unique claim IDs.

bash
# Check queue for highest claim number in file references
QUEUE_MAX=$(grep -oE '[0-9]{3}\.md' ops/queue*.yaml ops/queue/*.yaml 2>/dev/null | \
  grep -oE '[0-9]{3}' | sort -n | tail -1)
QUEUE_MAX=${QUEUE_MAX:-0}

# Check archive for highest claim number
ARCHIVE_MAX=$(find ops/queue/archive -name "*-[0-9][0-9][0-9].md" 2>/dev/null | \
  grep -v summary | sed 's/.*-\([0-9][0-9][0-9]\)\.md/\1/' | sort -n | tail -1)
ARCHIVE_MAX=${ARCHIVE_MAX:-0}

# Next claim starts after the highest
NEXT_CLAIM_START=$((QUEUE_MAX > ARCHIVE_MAX ? QUEUE_MAX + 1 : ARCHIVE_MAX + 1))

Claim numbers are globally unique and never reused across batches. This ensures every claim file name ({source}-{NNN}.md) is unique vault-wide.

Step 6: Create Extract Task File

Write the task file to ops/queue/${SOURCE_BASENAME}.md:

markdown
---
id: {SOURCE_BASENAME}
type: extract
source: {FINAL_SOURCE}
original_path: {original file path before move}
archive_folder: {ARCHIVE_DIR}
created: {UTC timestamp}
next_claim_start: {NEXT_CLAIM_START}
---

# Extract {DOMAIN:note_plural} from {source filename}

## Source
Original: {original file path}
Archived: {FINAL_SOURCE}
Size: {line count} lines
Content type: {detected type}

## Scope
{scope guidance if provided via --scope, otherwise: "Full document"}

## Acceptance Criteria
- Extract claims, implementation ideas, tensions, and testable hypotheses
- Duplicate check against {DOMAIN:notes}/ during extraction
- Near-duplicates create enrichment tasks (do not skip)
- Each output type gets appropriate handling

## Execution Notes
(filled by /reduce)

## Outputs
(filled by /reduce)

Step 7: Update Queue

Add the extract task entry to the queue file.

For YAML queues (ops/queue.yaml):

yaml
- id: {SOURCE_BASENAME}
  type: extract
  status: pending
  source: "{FINAL_SOURCE}"
  file: "{SOURCE_BASENAME}.md"
  created: "{UTC timestamp}"
  next_claim_start: {NEXT_CLAIM_START}

For JSON queues (ops/queue/queue.json):

json
{
  "id": "{SOURCE_BASENAME}",
  "type": "extract",
  "status": "pending",
  "source": "{FINAL_SOURCE}",
  "file": "{SOURCE_BASENAME}.md",
  "created": "{UTC timestamp}",
  "next_claim_start": {NEXT_CLAIM_START}
}

If no queue file exists: Create one with the appropriate schema header (phase_order definitions) and this first task entry.

Step 8: Report

--=={ seed }==--

Seeded: {SOURCE_BASENAME}
Source: {original path} -> {FINAL_SOURCE}
Archive folder: {ARCHIVE_DIR}
Size: {line count} lines
Content type: {detected type}

Task file: ops/queue/{SOURCE_BASENAME}.md
Claims will start at: {NEXT_CLAIM_START}
Claim files will be: {SOURCE_BASENAME}-{NNN}.md (unique across vault)
Queue: updated with extract task

Next steps:
  /ralph 1 --batch {SOURCE_BASENAME}     (extract claims)
  /pipeline will handle this automatically

Why This Skill Exists

Manual queue management is error-prone. This skill:

  • Ensures consistent task file format across batches
  • Handles claim numbering automatically (globally unique)
  • Checks for duplicates before creating unnecessary work
  • Moves sources to their permanent archive location immediately
  • Provides clear next steps for the user

Naming Convention

Task files use the source basename for human readability:

  • Task file: {source-basename}.md
  • Claim files: {source-basename}-{NNN}.md
  • Summary: {source-basename}-summary.md
  • Archive folder: {date}-{source-basename}/

Claim numbers (NNN) are globally unique across all batches, ensuring every filename is unique vault-wide. This is required because wiki links resolve by filename, not path.

Source Handling Patterns

{DOMAIN:inbox} source (most common):

{DOMAIN:inbox}/research/article.md
    | /seed
    v
ops/queue/archive/2026-01-30-article/article.md  <- source moved here
ops/queue/article.md                               <- task file created

Living doc (outside {DOMAIN:inbox}):

CLAUDE.md -> stays as CLAUDE.md (no move)
ops/queue/archive/2026-01-30-claude-md/           <- folder still created
ops/queue/claude-md.md                             <- task file created

When /archive-batch runs later, it moves task files into the existing archive folder and generates a summary.


Edge Cases

Source outside {DOMAIN:inbox}: Works — source stays in place, archive folder is created for task files only.

No queue file: Create ops/queue/queue.yaml (or .json) with schema header and this first entry.

Large source (2500+ lines): Note in output: "Large source ({N} lines) -- /reduce will chunk automatically."

Source is a URL or non-file: Report error: "/seed requires a file path."

No ops/derivation-manifest.md: Use universal vocabulary for all output.


Critical Constraints

never:

  • Skip duplicate detection (prevents wasted processing)
  • Move a source that is not in {DOMAIN:inbox} (living docs stay in place)
  • Reuse claim numbers from previous batches (globally unique is required)
  • Create a task file without updating the queue (both must happen together)

always:

  • Ask before proceeding when duplicates are detected
  • Create the archive folder even for living docs (task files need it)
  • Use the archived path (not original) in the task file for {DOMAIN:inbox} sources
  • Report next steps clearly so the user knows what to do next
  • Compute next_claim_start from both queue AND archive (not just one)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Seed AI skill do?

Add a source file to the processing queue. Checks for duplicates, creates archive folder, moves source from inbox, creates extract task, and updates queue. Triggers on "/seed", "/seed [file]", "queue this for processing".

Why use Seed on TypingMind?

Because you install it once and use it with any model. Seed 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 Seed in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agenticnotetaking/arscontexta/tree/main/skill-sources/seed. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Seed?

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 Seed?

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

Is the Seed AI skill free?

Yes. It is published on GitHub by agenticnotetaking 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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