agentmods.dev

reduce

A process for extracting structured knowledge from source material and saving it according to the repository's configured vocabulary and workflow.

In plain words
What is it for?
Use it to extract useful information from files, follow configured selection and processing depth, and connect the results to notes, topic maps, queues, and later review steps.
Why use it?
It makes notes and insights consistent with the project's domain rules instead of relying on generic labels or an arbitrary level of detail.

Skill for Claude CodeCodex

3.5k +3 6mo ago A scan 73 tokens original MIT
View the source file agenticnotetaking/arscontexta
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/reduce
Any agent
npx skills add agenticnotetaking/arscontexta --skill reduce
Clone the repo
git clone --depth 1 https://github.com/agenticnotetaking/arscontexta

Made for: Claude Code, Codex.

Per session 73 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,342 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 $0.00073 $0.10342
Opus 5 $0.00036 $0.05171
Sonnet 5 $0.00015 $0.02068
Haiku 4.5 $0.00007 $0.01034

Measured 2d ago against content hash caa0e753214b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

reduce 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 2d 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/reduce/SKILL.md · 1,120 lines

How it starts

The opening of the file, as written. The whole thing — 1,120 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Runtime Configuration (Step 0 — before any processing)

Read these files to configure domain-specific behavior:

  1. ops/derivation-manifest.md — vocabulary mapping, extraction categories, platform hints

    • Use vocabulary.notes for the notes folder name
    • Use vocabulary.inbox for the inbox folder name
    • Use vocabulary.note for the note type name in output
    • Use vocabulary.note_plural for the plural form
    • Use vocabulary.reduce for the process verb in output
    • Use vocabulary.cmd_reflect for the next-phase command name
    • Use vocabulary.cmd_reweave for the backward-pass command name
    • Use vocabulary.cmd_verify for the verification command name
    • Use vocabulary.extraction_categories for domain-specific extraction table
    • Use vocabulary.topic_map for MOC/topic map references
    • Use vocabulary.topic_maps for plural form

  2. ops/config.yaml — processing depth, pipeline chaining, selectivity

    • processing.depth: deep | standard | quick
    • processing.chaining: manual | suggested | automatic
    • processing.extraction.selectivity: strict | moderate | permissive

  3. ops/queue/queue.json — current task queue (for handoff mode)

If these files don't exist (pre-init invocation or standalone use), use universal defaults:

  • depth: standard
  • chaining: suggested
  • selectivity: moderate
  • notes folder: notes/
  • inbox folder: inbox/

THE MISSION (READ THIS OR YOU WILL FAIL)

You are the extraction engine. Raw source material enters. Structured, atomic {vocabulary.note_plural} exit. Everything between is your judgment — and that judgment must err toward extraction, not rejection.

The Core Distinction

Concept What It Means Example
Having knowledge The vault contains information "We store notes in folders"
Articulated reasoning The vault explains WHY something works as a traversable {vocabulary.note} "folder structure mirrors cognitive chunking because..."

Read the full file on GitHub · 1,120 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. 2d ago First seen · 1,120 lines · 73 tokens per session scan A caa0e753214b

Subscribe to this mod's changes

reduce is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 73 tokens to every session and 10,342 once invoked, about $0.0004 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.

DocumentsResearchclaude-codeclaude-code-pluginknowledge-baseknowledge-managementmarkdownsecond-brain
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