| name | academic-pipeline |
| description | Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow. |
| metadata | {"version":"3.7.0","last_updated":"2026-05-05","depends_on":"deep-research, academic-paper, academic-paper-reviewer","status":"active","data_access_level":"verified_only","task_type":"open-ended","related_skills":["deep-research","academic-paper","academic-paper-reviewer"]} |
Academic Pipeline v3.7.0 — Full Academic Research Workflow Orchestrator
A lightweight orchestrator that manages the complete academic pipeline from research exploration to final manuscript. It does not perform substantive work — it only detects stages, recommends modes, dispatches skills, manages transitions, and tracks state.
v3.6.3 (opt-in): Set ARS_PASSPORT_RESET=1 to promote FULL checkpoints to context-reset boundaries. Use resume_from_passport=<hash> in a fresh session to continue from the recorded stage. See references/passport_as_reset_boundary.md.
v2.0 Core Improvements:
- Mandatory user confirmation checkpoints — Each stage completion requires user confirmation before proceeding to the next step
- Academic integrity verification — After paper completion and before review submission, 100% reference and data verification must pass
- Two-stage review — First full review + post-revision focused verification review
- Final integrity check — After revision completion, re-verify all citations and data are 100% correct
- Reproducible — Standardized workflow producing consistent quality assurance each time
- Process documentation — After pipeline completion, automatically generates a "Paper Creation Process Record" PDF documenting the human-AI collaboration history
Quick Start
Full workflow (from scratch):
I want to write a research paper on the impact of AI on higher education quality assurance
--> academic-pipeline launches, starting from Stage 1 (RESEARCH)
Mid-entry (existing paper):
I already have a paper, help me review it
--> academic-pipeline detects mid-entry, starting from Stage 2.5 (INTEGRITY)
Revision mode (received reviewer feedback):
I received reviewer comments, help me revise
--> academic-pipeline detects, starting from Stage 4 (REVISE)
Resume from passport (cross-session context reset, opt-in):
resume_from_passport=<hash> [stage=<n>] [mode=<m>]
--> Loads the Material Passport (Schema 9), locates the kind: boundary entry matching <hash>, and confirms it has no later kind: resume entry consuming it. If pending_decision is set, the decision prompt fires first to capture the user's branch choice for the audit ledger; the prompt is never skipped, even when the user supplies stage=. After the prompt (or immediately if no pending_decision), the next stage is determined by: (a) stage=<n> CLI override if provided, else (b) the matched option's next_stage, else (c) the next field recorded in the boundary entry. CLI stage=/mode= overrides win over option routing.
- Gate (emit):
ARS_PASSPORT_RESET=1 must be set in the emitting session. Without the flag, no kind: boundary entries are written and there is nothing to resume from.
- Gate (resume): No flag required. Any session can invoke
resume_from_passport=<hash> against a passport that carries a valid boundary entry matching the hash.
- Intent: Invoke in a fresh Claude Code session. Resuming within the same session that emitted the boundary provides no token savings and may drop still-live in-session context.
- Stage: Any. Resumes at whatever stage the routing rules above determine.
- Reference:
references/passport_as_reset_boundary.md — see §"resume_from_passport mode contract".
Execution flow:
- Detect the user's current stage and available materials
- Recommend the optimal mode for each stage
- Dispatch the corresponding skill for each stage
- After each stage completion, proactively prompt and wait for user confirmation
- Track progress throughout; Pipeline Status Dashboard available at any time
Trigger Conditions
Trigger Keywords
English: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow
Non-Trigger Scenarios
| Scenario | Skill to Use |
|---|
| Only need to search materials or do a literature review | deep-research |
| Only need to write a paper (no research phase needed) | academic-paper |
| Only need to review a paper | academic-paper-reviewer |
| Only need to check citation format | academic-paper (citation-check mode) |
| Only need to convert paper format | academic-paper (format-convert mode) |
Trigger Exclusions
- If the user only needs a single function (just search materials, just check citations), no pipeline is needed — directly trigger the corresponding skill
- If the user is already using a specific mode of a skill, respect that entry point; the pipeline is opt-in
- The pipeline is optional, not mandatory
Pipeline Stages (10 Stages)
| Stage | Name | Skill / Agent Called | Available Modes | Deliverables |
|---|
| 1 | RESEARCH | deep-research | socratic, full, quick | RQ Brief, Methodology, Bibliography, Synthesis |
| 2 | WRITE | academic-paper | plan, full | Paper Draft |
| 2.5 | INTEGRITY | integrity_verification_agent | pre-review | Integrity verification report + corrected paper |
| 3 | REVIEW | academic-paper-reviewer | full (incl. Devil's Advocate) | 5 review reports + Editorial Decision + Revision Roadmap |
| 4 | REVISE | academic-paper | revision | Revised Draft, Response to Reviewers |
| 3' | RE-REVIEW | academic-paper-reviewer | re-review | Verification review report: revision response checklist + residual issues |
| 4' | RE-REVISE | academic-paper | revision | Second revised draft (if needed) |
| 4.5 | FINAL INTEGRITY | integrity_verification_agent | final-check | Final verification report (must achieve 100% pass to proceed) |
| 5 | FINALIZE | academic-paper | format-convert | Final Paper (default MD; DOCX via Pandoc when available, otherwise conversion instructions; ask about LaTeX; confirm correctness; PDF) |
| 6 | PROCESS SUMMARY | orchestrator | auto | Paper creation process record MD + LaTeX to PDF (bilingual) |
Parallelization opportunity (v3.3): Within Stage 2, the academic-paper skill's Phase 1 (literature_strategist_agent) and the visualization_agent can operate in parallel after Phase 2 (structure_architect_agent) completes the outline. Specifically:
- Once the outline includes a visualization plan,
visualization_agent can begin figure generation
- Simultaneously,
argument_builder_agent can build CER chains
draft_writer_agent waits for both to complete before beginning Phase 4
This mirrors PaperOrchestra's parallel execution of Plot Generation (Step 2) and Literature Review (Step 3) after Outline (Step 1), which reduces overall pipeline latency. The parallelization is optional — sequential execution remains the default for simplicity.
Pipeline State Machine
- Stage 1 RESEARCH -> user confirmation -> Stage 2
- Stage 2 WRITE -> user confirmation -> Stage 2.5
- Stage 2.5 INTEGRITY -> PASS -> Stage 3 (FAIL -> fix and re-verify, max 3 rounds)
- Stage 3 REVIEW -> Accept -> Stage 4.5 / Minor|Major -> Stage 4 / Reject -> Stage 2 or end
- Stage 4 REVISE -> user confirmation -> Stage 3'
- Stage 3' RE-REVIEW -> Accept|Minor -> Stage 4.5 / Major -> Stage 4'
- Stage 4' RE-REVISE -> user confirmation -> Stage 4.5 (no return to review)
- Stage 4.5 FINAL INTEGRITY -> PASS (zero issues) -> Stage 5 (FAIL -> fix and re-verify)
- Stage 5 FINALIZE -> MD -> DOCX via Pandoc when available (otherwise instructions) -> ask about LaTeX -> confirm -> PDF -> Stage 6
- Stage 6 PROCESS SUMMARY -> ask language version -> generate process record MD -> LaTeX -> PDF -> end
See references/pipeline_state_machine.md for complete state transition definitions.
Adaptive Checkpoint System
⚠️ IRON RULE — Core rule: After each stage completion, the system must proactively prompt the user and wait for confirmation. The checkpoint presentation adapts based on context and user engagement.
Checkpoint Types
| Type | When Used | Content |
|---|
| FULL | First checkpoint; after integrity boundaries; before finalization | Full deliverables list + decision dashboard + all options |
| SLIM | After 2+ consecutive "continue" responses on non-critical stages | One-line status + explicit continue/pause prompt |
| MANDATORY | Integrity FAIL; Review decision; Stage 5 | Cannot be skipped; requires explicit user input |
Decision Dashboard (shown at FULL checkpoints)
━━━ Stage [X] [Name] Complete ━━━
Metrics:
- Word count: [N] (target: [T] +/-10%) [OK/OVER/UNDER]
- References: [N] (min: [M]) [OK/LOW]
- Coverage: [N]/[T] sections drafted [COMPLETE/PARTIAL]
- Quality indicators: [score if available]
Deliverables:
- [Material 1]
- [Material 2]
Flagged: [any issues detected, or "None"]
Ready to proceed to Stage [Y]? You can also:
1. View progress (say "status")
2. Adjust settings
3. Pause pipeline
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Adaptive Rules
- First checkpoint: always FULL
- After 2+ consecutive "continue" without review: prompt user awareness ("You've continued [N] times in a row. Want to review progress?")
- Integrity boundaries (Stage 2.5, 4.5): always MANDATORY
- Review decisions (Stage 3, 3'): always MANDATORY
- Before finalization (Stage 5): always MANDATORY
- All other stages: start FULL, downgrade to SLIM if user says "just continue"
Checkpoint Rules
- ⚠️ IRON RULE: Cannot auto-skip MANDATORY checkpoints: Even if the previous stage result is perfect, explicit user input is required at MANDATORY checkpoints
- User can adjust: At FULL and MANDATORY checkpoints, users can modify the mode or settings for the next step
- Pause-friendly: Users can pause at any checkpoint and resume later
- SLIM mode: If the user says "just continue" or "fully automatic," subsequent non-critical checkpoints switch to SLIM format (one-line status + explicit continue/pause prompt)
- Awareness guard: After 4+ consecutive continue responses, the system inserts a FULL checkpoint regardless of stage type to ensure user remains engaged
Self-Check Questions (at every FULL checkpoint)
Before presenting the checkpoint to the user, the orchestrator asks itself:
- Citation integrity: Are there any unverified citations in the latest output?
- Sycophantic concession: Did the latest stage uncritically accept all feedback without pushback?
- Quality trajectory: Is the latest output ≥ the quality of the previous stage? If declining, PAUSE and flag.
- Scope discipline: Did the latest stage add content not requested by the user or the revision roadmap?
- Completeness: Are all required deliverables for this stage present?
If ANY answer raises concern, include it in the checkpoint presentation to the user.
Agent Team (4 Agents)
| # | Agent | Role | File |
|---|
| 1 | pipeline_orchestrator_agent | Main orchestrator: detects stage, recommends mode, triggers skill, manages transitions | agents/pipeline_orchestrator_agent.md |
| 2 | state_tracker_agent | State tracker: records completed stages, produced materials, revision loop count | agents/state_tracker_agent.md |
| 3 | integrity_verification_agent | Integrity verifier: 100% reference/citation/data verification (blocking) | agents/integrity_verification_agent.md |
| 4 | collaboration_depth_agent | Observer (advisory only — never blocks). Reads dialogue log and scores user-AI collaboration pattern against shared/collaboration_depth_rubric.md. Invoked at FULL/SLIM checkpoints and at pipeline completion. Based on Wang & Zhang (2026). | agents/collaboration_depth_agent.md |
Orchestrator Workflow
Step 1: INTAKE & DETECTION
pipeline_orchestrator_agent analyzes the user's input:
1. What materials does the user have?
- No materials --> Stage 1 (RESEARCH)
- Has research data --> Stage 2 (WRITE)
- Has paper draft --> Stage 2.5 (INTEGRITY)
- Has verified paper --> Stage 3 (REVIEW)
- Has review comments --> Stage 4 (REVISE)
- Has revised draft --> Stage 3' (RE-REVIEW)
- Has final draft for formatting --> Stage 5 (FINALIZE)
2. What is the user's goal?
- Full workflow (research to publication)
- Partial workflow (only certain stages needed)
3. Determine entry point, confirm with user
Step 2: MODE RECOMMENDATION
Based on entry point and user preferences, recommend modes for each stage:
User type determination:
- Novice / wants guidance --> socratic (Stage 1) + plan (Stage 2) + guided (Stage 3)
- Experienced / wants direct output --> full (Stage 1) + full (Stage 2) + full (Stage 3)
- Time-limited --> quick (Stage 1) + full (Stage 2) + quick (Stage 3)
Explain the differences between modes when recommending, letting the user choose
Step 3: STAGE EXECUTION
Call the corresponding skill (does not do work itself, purely dispatching):
1. Inform the user which Stage is about to begin
2. Load the corresponding skill's SKILL.md
3. Launch the skill with the recommended mode
4. Monitor stage completion status
After completion:
1. Compile deliverables list
2. Update pipeline state (call state_tracker_agent)
3. [MANDATORY] Proactively prompt checkpoint, wait for user confirmation
Step 4: TRANSITION
After user confirmation:
1. Pass the previous stage's deliverables as input to the next stage
2. Trigger handoff protocol (defined in each skill's SKILL.md):
- Stage 1 --> 2: deep-research handoff (RQ Brief + Bibliography + Synthesis)
- Stage 2 --> 2.5: Pass complete paper to integrity_verification_agent
- Stage 2.5 --> 3: Pass verified paper to reviewer
- Stage 3 --> 4: Pass Revision Roadmap to academic-paper revision mode
- Stage 4 --> 3': Pass revised draft and Response to Reviewers to reviewer
- Stage 3' --> 4': Pass new Revision Roadmap + R&R Traceability Matrix (Schema 11) to academic-paper revision mode
- Stage 4/4' --> 4.5: Pass revision-completed paper to integrity_verification_agent (final verification)
- Stage 4.5 --> 5: Pass verified final draft to format-convert mode
3. Begin next stage
Mid-Conversation Reinforcement Protocol
At every stage transition, the orchestrator MUST inject a brief core principles reminder. This prevents context rot in long conversations.
Template (adapt to the upcoming stage):
