seed

A workflow for adding a source file to a processing queue in a knowledge vault. It checks whether the source was already handled, archives it, creates a processing task, and updates the queue.

In plain words
What is it for?
Use it to queue research articles, documentation, transcripts, or other files from the inbox for extraction and later processing.
Why use it?
It prevents duplicate processing and keeps incoming files organized. It also confirms that the requested source exists and records enough information to process it later.

Skill for Claude CodeCodex

Part of the arscontexta plugin — 26 skills, 1 agent, 2 hooks shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/agenticnotetaking/arscontexta/seed
Any agent
npx skills add agenticnotetaking/arscontexta --skill seed
Clone the repo
git clone --depth 1 https://github.com/agenticnotetaking/arscontexta

Made for: Claude Code, Codex.

Or install arscontexta, the plugin that ships this one along with the rest of its 26 skills, 1 agent, 2 hooks.

Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,490 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00049 $0.02490
Opus 5 $0.00024 $0.01245
Sonnet 5 $0.00010 $0.00498
Haiku 4.5 $0.00005 $0.00249

Measured 3d ago against content hash a04342aa3055, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

seed scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skill-sources/seed/SKILL.md · 304 lines

How it starts

The opening of the file, as written. The whole thing — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

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:

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

Read the full file on GitHub · 304 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 304 lines · 49 tokens per session scan A a04342aa3055

Subscribe to this mod's changes

seed is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 49 tokens to every session and 2,490 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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