Ars Contexta is a Claude Code plugin that creates a personalized knowledge system from a conversation about how someone thinks and works, using connected Markdown files, note-processing skills, hooks, navigation maps, and templates. It is for people who want an owned, persistent second brain tailored to their domain. The catalogue entries are its generated skills, hooks, agent, and plugin workflow.
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.
npx skills add agenticnotetaking/arscontexta --skill remembergit clone --depth 1 https://github.com/agenticnotetaking/arscontextaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agenticnotetaking/arscontexta/remember)<a href="https://agentmods.dev/skills/agenticnotetaking/arscontexta/remember"><img src="https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/remember/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agenticnotetaking/arscontexta/remember"><img src="https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/remember.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 8 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
- medium Excessive Agency · line 216 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.04365 |
| Opus 5.5 | $0.00016 | $0.01746 |
| Sonnet 5.5 | $0.00008 | $0.00873 |
| Haiku 4.5 | $0.00004 | $0.00436 |
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 1mo 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.
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:
-
ops/derivation-manifest.md— vocabulary mapping, domain context- Use
vocabulary.notesfor the notes folder name - Use
vocabulary.notefor the note type name in output - Use
vocabulary.rethinkfor rethink command name in threshold alerts - Use
vocabulary.topic_mapfor MOC references
- Use
-
ops/config.yaml— thresholdsself_evolution.observation_threshold(default: 10) — for threshold alertsself_evolution.tension_threshold(default: 5) — for threshold alerts
-
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-sessionsor--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 |
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.
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.
- 1mo ago First seen · 535 lines · 40 tokens per session scan A 3cea125e9fed
remember is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,509 stars, last pushed 7mo ago), licensed MIT. It adds 40 tokens to every session and 4,365 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
llm-wiki
Build and maintain an LLM-curated knowledge base from papers, articles, transcripts, notes and project findings. Ingest sources into linked Markdown, answer questions with citations, lint or upgrade a wiki, and preserve useful synthesis across sessions and agents. Also capture verified task experience, consolidate…
migrate
Scan the project and migrate existing documentation into OKF format. Use when the user wants to populate the knowledge base from existing docs, README, schemas, API specs, runbooks, or any structured project knowledge. Trigger on "migrate to okf", "populate the knowledge base", "scan my docs", "convert docs to okf"…
claude-mega-brain
OKF knowledge navigation. ALWAYS use this skill the moment a block appears in session context — it means the project has documented concepts and you must consult them before answering questions about data, schemas, metrics, APIs, or systems. Also trigger when the user asks about a table, column, metric definition…
ingest
Create or update a single OKF concept file from raw input. Use when the user wants to document a specific table, metric, API, service, or concept in the knowledge base — even if they say "add this to the okf", "documenta essa tabela", "cria um conceito pra isso", "adiciona no knowledge base", "register this API", or…
init
Initialize OKF knowledge base files in the current project. Use when the user wants to start documenting their project for claude-mega-brain, or when the user says "init mega brain", "setup the knowledge base", "cria o okf", "inicializa o mega brain", "quero usar o mega brain", "criar a estrutura do knowledge base"…
query
Progressive retrieval from the Trove index. Use when the user asks what Trove knows about a subsystem, where code lives, or wants context assembled before editing. Runs architecture -> subsystem -> module -> symbol retrieval with a context budget.