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 agentmods add skills/agenticnotetaking/arscontexta/pipelinenpx skills add agenticnotetaking/arscontexta --skill pipelinegit 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/pipeline)<a href="https://agentmods.dev/skills/agenticnotetaking/arscontexta/pipeline"><img src="https://agentmods.dev/badge/skills/agenticnotetaking/arscontexta/pipeline.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00054 | $0.02350 |
| Opus 5 | $0.00027 | $0.01175 |
| Sonnet 5 | $0.00011 | $0.00470 |
| Haiku 4.5 | $0.00005 | $0.00235 |
Grade A, and why
pipeline 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 4d 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 — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EXECUTE NOW
Target: $ARGUMENTS
Parse immediately:
- Source file path: the file to process (required)
--handoff: output RALPH HANDOFF block at end (for chaining)- If target is empty: list files in {DOMAIN:inbox}/ and ask which to process
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. Run the full pipeline.
Pipeline Overview
The pipeline chains four phases. Each phase uses skill invocation or /ralph for subagent-based processing. State lives in the queue file — the pipeline is stateless orchestration on top of stateful queue entries.
Source file
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v
Phase 1: /seed — create extract task, move source to archive
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v
Phase 2: /reduce (via /ralph) — extract claims from source
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v
Phase 3: /ralph (all claims) — create -> reflect -> reweave -> verify
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v
Phase 4: /archive-batch — move task files, generate summary
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v
Complete
The pipeline is the convenience wrapper. /ralph is the engine. /seed is the entry point.
Phase 1: Seed
Invoke /seed on the target file to create the extract task, check for duplicates, and move the source to its archive folder.
How to invoke:
Use the Skill tool if available, otherwise execute the /seed workflow directly:
- Validate source exists
- Check for prior processing (duplicate detection)
- Create archive folder
- Move source from {DOMAIN:inbox} to archive
- Create extract task file
- Add extract task to queue
Capture from seed output:
- Batch ID: the source basename (used for --batch filtering in subsequent steps)
- Archive folder path: where the source was moved
- next_claim_start: the claim numbering start
Report: $ Seeded: {source-name}
If seed reports the file was already processed: Ask the user whether to proceed or skip. Do NOT auto-skip — the user may want to re-process with different scope.
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.
- 4d ago First seen · 315 lines · 54 tokens per session scan A bacb7864cddc
pipeline is a skill published in the GitHub repository agenticnotetaking/arscontexta (3,486 stars, last pushed 6mo ago), licensed MIT. It adds 54 tokens to every session and 2,350 once invoked, about $0.0003 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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