--- name: pipeline-5-e5a5218725d5 description: End-to-end source processing -- seed, reduce, process all claims through reflect/revisit/review, archive. The full pipeline in one command. Triggers on "/pipeline", "/pipeline [file]", "process this end to end", "full pipeline". version: "1.0" generated_from: "arscontexta-v1.6" user-invocable: true context: fork model: opus allowed-tools: Read, Write, Edit, Grep, Glob, Bash, Task argument-hint: "[file] — path to source file to process end-to-end" --- ## EXECUTE NOW **Target: $ARGUMENTS** Parse immediately: - Source file path: the file to process (required) - `--handoff`: output RALPH HANDOFF block at end (for chaining) - If target is empty: list files in arscontexta/inbox/ and ask which to process ### Step 0: Read Vocabulary Read `arscontexta/ops/derivation-manifest.md` (or fall back to `arscontexta/ops/derivation.md`) for domain vocabulary mapping. All output must use domain-native terms. If neither file exists, use universal terms. **START NOW.** Run the full pipeline. --- ## Pipeline Overview The pipeline chains four phases. Each phase uses skill invocation or /ralph for subagent-based processing. State lives in the queue file — the pipeline is stateless orchestration on top of stateful queue entries. ``` Source file | v Phase 1: /seed — create extract task, move source to archive | v Phase 2: /extract (via /ralph) — extract claims from source | v Phase 3: /ralph (all claims) — create -> reflect -> reweave -> verify | v Phase 4: /archive-batch — move task files, generate summary | v Complete ``` The pipeline is the convenience wrapper. /ralph is the engine. /seed is the entry point. --- ## Phase 1: Seed Invoke /seed on the target file to create the extract task, check for duplicates, and move the source to its archive folder. **How to invoke:** Use the Skill tool if available, otherwise execute the /seed workflow directly: - Validate source exists - Check for prior processing (duplicate detection) - Create archive folder - Move source from inbox to archive - Create extract task file - Add extract task to queue **Capture from seed output:** - **Batch ID**: the source basename (used for --batch filtering in subsequent steps) - **Archive folder path**: where the source was moved - **next_claim_start**: the claim numbering start Report: `$ 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 /extract, 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} notes, {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 note 1/{total}: {title} $ create... done $ reflect... done (3 connections found) $ reweave... done (2 notes 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 `arscontexta/ops/queue/` to `arscontexta/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 notes with titles - Any notable learnings from the batch --- ## Phase 6: Final Report ``` --=={ pipeline }==-- Source: {source_file} Batch: {batch_id} Extraction: notes extracted: {N} Enrichments identified: {M} Processing: notes created: {N} Existing notes enriched: {M} Connections added: {C} indexs updated: {T} Older notes updated via reweave: {R} Quality: All verify checks: {PASS/FAIL count} Archive: arscontexta/ops/queue/archive/{date}-{batch_id}/ Summary: {batch_id}-summary.md notes 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} notes and {M} enrichments - Processed all claims through 4-phase pipeline - Archived batch to {archive_path} Files Modified: - docs/notes/ ({N} new notes) - arscontexta/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 /extract 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 At | How to Resume | |----------------|---------------| | Before seed | Run /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 arscontexta/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):** /extract handles chunking automatically. The pipeline does not need special handling. **No arscontexta/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 notes) - Suggest next steps if interrupted - Use domain-native vocabulary from derivation manifest --- Source attribution: FDU-INS Original project: https://github.com/FDU-INS/Insurance-Skills/blob/b22eca2e974f918ce591c5b2bd829fb7510e0191/Skills/pipeline-5/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: FDU-INS Original project: https://github.com/FDU-INS/Insurance-Skills/blob/b22eca2e974f918ce591c5b2bd829fb7510e0191/Skills/pipeline-5/SKILL.md Attribution basis: Source repository record Fields of work: Development & Engineering, Operations, Project & Program Management Roles: Business Process Analyst, Data Engineer, Project Manager Specialties: API Tools: Arscontexta Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: ddd7b8639dd3f5326225f7ce91c70c66d92a201cec1d242289018b4fc475f6ec