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Pipeline

OrganizationPopular
agenticnotetaking
pipeline

End-to-end source processing -- seed, reduce, process all claims through reflect/reweave/verify, archive. The full pipeline in one command. Triggers on "/pipeline", "/pipeline [file]", "process this end to end", "full pipeline".

Overview

Publisheragenticnotetaking
Repositoryarscontexta
Skill namepipeline
Stars
3.5K
Forks
226
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by agenticnotetaking on GitHub. Read the source before you install it.

Installation

Install the Pipeline AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agenticnotetaking/arscontexta.git /tmp/arscontexta
mkdir -p .claude/skills
cp -r /tmp/arscontexta/skill-sources/pipeline .claude/skills/pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pipeline in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pipeline on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pipeline is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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
    |
    v
Phase 1: /seed — create extract task, move source to archive
    |
    v
Phase 2: /reduce (via /ralph) — extract claims from source
    |
    v
Phase 3: /ralph (all claims) — create -> reflect -> reweave -> verify
    |
    v
Phase 4: /archive-batch — move task files, generate summary
    |
    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.


Phase 2: Extract (Reduce)

Process the extract task via /ralph. This spawns a subagent that runs /reduce, extracting claims from the source and creating task entries in the queue.

How to invoke:

/ralph 1 --batch {batch_id} --type extract

Or via Task tool:

Task(
  prompt = "Run /ralph 1 --batch {batch_id} --type extract",
  description = "extract: {batch_id}"
)

After completion, read the queue to count extracted claims and enrichments:

Check how many pending tasks exist for this batch. The reduce phase creates 1 queue entry per claim and 1 per enrichment.

Report:

$ Extracted: {N} {DOMAIN:note_plural}, {M} enrichments
  Processing {total_tasks} tasks through the pipeline...

If zero claims extracted: Report the issue. For TFT sources, zero extraction is a bug — the source almost certainly contains extractable content. Ask the user whether to retry with different scope or skip.


Phase 3: Process All Claims

Count total pending tasks for this batch from the queue. Then process all of them through the full phase sequence.

How to invoke:

/ralph {remaining_count} --batch {batch_id}

Or via Task tool:

Task(
  prompt = "Run /ralph {remaining_count} --batch {batch_id}",
  description = "process: {batch_id} ({remaining_count} tasks)"
)

This processes every claim through: create -> reflect -> reweave -> verify. And every enrichment through: enrich -> reflect -> reweave -> verify.

Each phase runs in an isolated subagent with fresh context. /ralph handles all the orchestration: subagent spawning, handoff parsing, queue advancement, learnings capture.

Progress reporting:

The /ralph invocation reports progress per task. The pipeline relays this:

$ Processing {DOMAIN:note} 1/{total}: {title}
  $ create... done
  $ reflect... done (3 connections found)
  $ reweave... done (2 {DOMAIN:note_plural} updated)
  $ verify... done (PASS)

For large batches (20+ claims): /ralph handles context isolation automatically via subagents. The pipeline does NOT need to chunk — /ralph processes N tasks sequentially with fresh context per phase.


Phase 4: Verify Completion

After /ralph finishes, verify all tasks for this batch are done.

Check the queue: count tasks for this batch that are NOT done.

If tasks remain pending:

  • Report which tasks are incomplete and at which phase
  • Show the specific task IDs and their current_phase
  • Suggest: "Run /ralph --batch {batch_id} to continue from where it stopped"
  • Do NOT proceed to archive

If all tasks are done: Proceed to Phase 5.


Phase 5: Archive Batch

When all tasks for the batch are complete, archive the batch.

How to invoke:

/archive-batch {batch_id}

Or execute directly:

  1. Move all task files from ops/queue/ to ops/queue/archive/{date}-{batch_id}/
  2. Generate a batch summary file: {batch_id}-summary.md
  3. Remove completed entries from the queue (or mark as archived)

The summary should include:

  • Source file name and original location
  • Number of claims extracted
  • Number of enrichments
  • List of created {DOMAIN:note_plural} with titles
  • Any notable learnings from the batch

Phase 6: Final Report

--=={ pipeline }==--

Source: {source_file}
Batch: {batch_id}

Extraction:
  {DOMAIN:note_plural} extracted: {N}
  Enrichments identified: {M}

Processing:
  {DOMAIN:note_plural} created: {N}
  Existing {DOMAIN:note_plural} enriched: {M}
  Connections added: {C}
  {DOMAIN:topic map}s updated: {T}
  Older {DOMAIN:note_plural} updated via reweave: {R}

Quality:
  All verify checks: {PASS/FAIL count}

Archive: ops/queue/archive/{date}-{batch_id}/
Summary: {batch_id}-summary.md

{DOMAIN:note_plural} created:
- [[claim title 1]]
- [[claim title 2]]
- ...

If --handoff flag was set, also output:

=== RALPH HANDOFF: pipeline ===
Target: {source_file}

Work Done:
- Seeded source: {batch_id}
- Extracted {N} {DOMAIN:note_plural} and {M} enrichments
- Processed all claims through 4-phase pipeline
- Archived batch to {archive_path}

Files Modified:
- {DOMAIN:notes}/ ({N} new {DOMAIN:note_plural})
- ops/queue/archive/{date}-{batch_id}/ (archived)

Learnings:
- [Friction]: {description} | NONE
- [Surprise]: {description} | NONE
- [Methodology]: {description} | NONE
- [Process gap]: {description} | NONE

Queue Updates:
- All tasks for batch {batch_id} marked done and archived
=== END HANDOFF ===

Error Handling

Phase failure at any stage:

  1. Report the failure with context (which phase, which task, what error)
  2. Show the current queue state for this batch
  3. Suggest remediation: "Run /ralph --batch {batch_id} to continue from where it stopped"
  4. Do NOT attempt to continue automatically past failures

The pipeline is resumable. Queue state persists across sessions:

  • /seed detects prior processing and asks whether to proceed
  • /ralph picks up from the last completed phase (queue is the source of truth)
  • /archive-batch verifies completeness before archiving

Seed failure: If /seed fails (file not found, duplicate detected and user declines), stop the pipeline entirely.

Extract failure: If /reduce extracts zero claims, report and stop. Do not proceed to an empty processing phase.

Processing failure: If /ralph fails mid-batch, the queue preserves state. Individual claims resume from their failed phase on next /ralph invocation.

Archive failure: If archiving fails, the claims are still created and connected. Only the organizational cleanup is missing — re-run /archive-batch manually.


Resumability

The pipeline is designed to be interrupted and resumed at any point:

Interrupted AtHow to Resume
Before seedRun /pipeline again (starts fresh)
After seed, before reduce/ralph 1 --batch {id} --type extract
After reduce, during claims/ralph --batch {id} (picks up from failed phase)
After all claims, before archive/archive-batch {id}

State lives in the queue file. The pipeline reads queue state, not session state. This means you can interrupt, close the session, and resume later.


Edge Cases

No target file: List {DOMAIN:inbox}/ candidates, suggest the best one based on age and relevance.

Source already seeded: /seed detects this and asks the user. If they decline, the pipeline stops cleanly.

Large source (2500+ lines): /reduce handles chunking automatically. The pipeline does not need special handling.

No ops/derivation-manifest.md: Use universal vocabulary for all output.


Critical Constraints

never:

  • Skip the seed phase (duplicate detection is important)
  • Continue past a failed phase automatically
  • Process claims inline instead of via /ralph subagents
  • Archive a batch with incomplete tasks

always:

  • Report progress at each phase boundary
  • Verify all tasks are done before archiving
  • Show the user what was created (list of {DOMAIN:note_plural})
  • Suggest next steps if interrupted
  • Use domain-native vocabulary from derivation manifest

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pipeline AI skill do?

End-to-end source processing -- seed, reduce, process all claims through reflect/reweave/verify, archive. The full pipeline in one command. Triggers on "/pipeline", "/pipeline [file]", "process this end to end", "full pipeline".

Why use Pipeline on TypingMind?

Because you install it once and use it with any model. Pipeline is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agenticnotetaking/arscontexta/tree/main/skill-sources/pipeline. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pipeline?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pipeline?

As many as you like. As long as a model supports skills, you can use Pipeline with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pipeline AI skill free?

Yes. It is published on GitHub by agenticnotetaking under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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