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 ralphgit 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/ralph)<a href="https://agentmods.dev/skills/agenticnotetaking/arscontexta/ralph"><img src="https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/ralph/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/ralph"><img src="https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/ralph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00061 | $0.05168 |
| Opus 5.5 | $0.00024 | $0.02067 |
| Sonnet 5.5 | $0.00012 | $0.01034 |
| Haiku 4.5 | $0.00006 | $0.00517 |
Grade A, and why
ralph 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 — 603 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXECUTE NOW
Target: $ARGUMENTS
Parse arguments:
- N (required unless --dry-run): number of tasks to process
- --parallel: concurrent claim workers (max 5) + cross-connect validation
- --batch [id]: process only tasks from specific batch
- --type [type]: process only tasks at a specific phase (extract, create, reflect, reweave, verify, enrich)
- --dry-run: show what would execute without running
- --handoff: output structured RALPH HANDOFF block at end (for pipeline chaining)
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. Process queue tasks.
MANDATORY CONSTRAINT: SUBAGENT SPAWNING IS NOT OPTIONAL
You MUST use the Task tool to spawn a subagent for EVERY task. No exceptions.
This is not a suggestion. This is not an optimization you can skip for "simple" tasks. The entire architecture depends on fresh context isolation per phase. Executing tasks inline in the lead session:
- Contaminates context (later tasks run on degraded attention)
- Skips the handoff protocol (learnings are not captured)
- Violates the ralph pattern (one phase per context window)
If you catch yourself about to execute a task directly instead of spawning a subagent, STOP. Call the Task tool. Every time. For every task. Including create tasks. Including "simple" tasks.
The lead session's ONLY job is: read queue, spawn subagent, evaluate return, update queue, repeat.
Phase Configuration
Each phase maps to specific Task tool parameters. Use these EXACTLY when spawning subagents.
| Phase | Skill Invoked | Purpose |
|---|---|---|
| extract | /reduce | Extract claims from source material |
| create | (inline note creation) | Write the {DOMAIN:note} file |
| enrich | /enrich | Add content to existing {DOMAIN:note} |
| reflect | /reflect | Find connections, update {DOMAIN:topic map}s |
| reweave | /reweave | Update older {DOMAIN:note_plural} with new connections |
| verify | /verify | Description quality + schema + health checks |
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 · 603 lines · 61 tokens per session scan A 7b4ee932f92d
ralph is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,509 stars, last pushed 7mo ago), licensed MIT. It adds 61 tokens to every session and 5,168 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.
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migrate
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