remember

A method for turning recurring work friction into written methodology notes. It supports notes from an explicit description, recent corrections in a conversation, or patterns found in saved session transcripts.

In plain words
What is it for?
Use it with a quoted problem, with no argument to review recent corrections, or with the session-mining option to find patterns across past work. It can also alert you when observation or tension counts reach configured thresholds.
Why use it?
It gives repeated mistakes and useful lessons a place to be recorded, while checking existing notes to reduce duplicates. Project configuration files can define the terminology and thresholds it uses.

Skill for Claude CodeCodex

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

Made for: Claude Code, Codex.

Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,365 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 $0.00040 $0.04365
Opus 5 $0.00020 $0.02183
Sonnet 5 $0.00008 $0.00873
Haiku 4.5 $0.00004 $0.00436

Measured 2d ago against content hash 3cea125e9fed, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

remember 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/remember/SKILL.md · 535 lines

How it starts

The opening of the file, as written. The whole thing — 535 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, domain context

    • Use vocabulary.notes for the notes folder name
    • Use vocabulary.note for the note type name in output
    • Use vocabulary.rethink for rethink command name in threshold alerts
    • Use vocabulary.topic_map for MOC references
  2. ops/config.yaml — thresholds

    • self_evolution.observation_threshold (default: 10) — for threshold alerts
    • self_evolution.tension_threshold (default: 5) — for threshold alerts
  3. ops/methodology/ — read existing methodology notes before creating new ones (prevents duplicates)

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

EXECUTE NOW

Target: $ARGUMENTS

Parse immediately:

  • If target contains a quoted description or unquoted text: explicit mode — user describes friction directly
  • If target is empty: contextual mode — review recent conversation for corrections
  • If target contains --mine-sessions or --mine: session mining mode — scan ops/sessions/ for patterns

START NOW. Reference below defines the three modes.


Explicit Mode

User provides a description: /remember "don't process personal notes like research" or /remember always check for duplicates before creating

Step 1: Parse the Friction

Analyze the user's description to extract:

  • What the agent did wrong (or what the user wants to prevent)
  • What the user wants instead (the correct behavior)
  • The scope — when does this apply? Always? Only for specific content types? Only in certain phases?
  • The category — which area of agent behavior does this affect?
Category Applies When
processing How to extract, reduce, or handle content
capture How to record, file, or organize incoming material
connection How to find, evaluate, or add links between notes
maintenance How to handle health checks, reweaving, cleanup
voice How to write, what tone or style to use
behavior General agent conduct, interaction patterns
quality Standards for notes, descriptions, titles

Read the full file on GitHub · 535 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 · 535 lines · 40 tokens per session scan A 3cea125e9fed

Subscribe to this mod's changes

remember is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 40 tokens to every session and 4,365 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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