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, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
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, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.
Academic Pipeline v3.15.0 — 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.
Routing discipline (v3.9.2): see .claude/CLAUDE.md "Routing Discipline (v3.9.2)" + shared/references/intent_clarification_protocol.md for cross-skill routing rules. This skill assumes routing has already settled — ambiguous cross-phase materials should have been clarified upstream.
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
v3.8 (opt-in): Set ARS_CLAIM_AUDIT=1 to enable the L3 claim-faithfulness audit gate at the Stage 4 → Stage 5 transition. When the flag is set, the orchestrator dispatches claim_ref_alignment_audit_agent after the v3.7.1 Cite-Time Provenance Finalizer and before formatter_agent's hard gate. The audit emits claim_audit_results[] + uncited_assertions[] + claim_drifts[] + constraint_violations[] + audit_sampling_summaries[] aggregates per the 8-row matrix; HIGH-WARN classes gate-refuse output via the formatter REFUSE rules 6-10. Default OFF for v3.8.0 — ramp-on plan deferred to post-calibration evidence (spec §5 mode flag rationale). See agents/claim_ref_alignment_audit_agent.md and the orchestrator §3.6 prose.
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 — Stage 6 generates a "Paper Creation Process Record" PDF documenting the human-AI collaboration history (delivered before the terminal acknowledgement that completes the pipeline)
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):
--> 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.
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
한국어: 학술 파이프라인, 연구부터 논문까지, 논문 전체 워크플로, 연구 주제 설정부터 논문 완성까지, 연구-논문 전 과정
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
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' 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 -> completion checkpoint (FULL) -> Stage 6 (user may decline Stage 6: marked skipped, pipeline goes directly to completed)
Stage 6 PROCESS SUMMARY -> ask language version -> generate process record MD -> LaTeX -> PDF -> terminal acknowledgement (finish / end / done / confirm, or an unambiguous natural-language equivalent) -> pipeline global state completed
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; Stage 5 completion (final-deliverable acceptance)
Full deliverables list + decision dashboard + all options
SLIM
After 2+ consecutive "continue" responses on non-critical stages
Before finalization (Stage 5 entry gate): always MANDATORY — this is the checkpoint between Stage 4.5 PASS and the Stage 5 dispatch, where the user explicitly confirms proceeding and makes the finalization-format decision (citation style); the in-stage LaTeX question and content confirmation stay inside Stage 5 execution. The Stage 5 completion checkpoint (Final Paper delivered, before Stage 6) is FULL — never SLIM. See references/pipeline_state_machine.md § Stage 5 boundary semantics
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 (5 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
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 during Stage 6 record compilation (whole-pipeline pass, before the Process Record is delivered). Based on Wang & Zhang (2026).
agents/collaboration_depth_agent.md
5
claim_ref_alignment_audit_agent
Opt-in claim faithfulness auditor (v3.8 #103). Audits sampled citations for claim ↔ reference alignment + negative-constraint compliance; emits per-claim claim_audit_results[], claim_drift[], uncited_assertions[], constraint_violations[]. Dispatched via orchestrator §3.6 when claim_audit mode is requested.
agents/claim_ref_alignment_audit_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 + Methodology Blueprint + 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, Response to Reviewers, and the Editorial Decision Letter (its Review Panel Provenance block feeds the #539 Judge Record) 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
- Stage 5 --> 6: Pass final deliverables list + pipeline state history to Process Summary (user may decline Stage 6 at the Stage 5 completion checkpoint)
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):
--- STAGE TRANSITION: [Current] → [Next] ---
🔄 Core Principles Reinforcement:
1. [Most relevant IRON RULE for the next stage]
2. [Most relevant Anti-Pattern to avoid in the next stage]
3. Quality check: Is the output of [Current Stage] at least as good as [Previous Stage]? If not, PAUSE.
Checkpoint: [MANDATORY/ADVISORY] — [What user needs to confirm]
---
Stage-specific reinforcement content: See references/reinforcement_content.md for the full transition → reinforcement focus table.
Phase-by-phase Invocation Contract (v3.9.2)
academic-pipeline is the orchestrator skill that coordinates the full ARS pipeline across 10 stages (delegating to deep-research, academic-paper, academic-paper-reviewer). Two invocation modes:
Mode A — orchestrator-driven (default):pipeline_orchestrator_agent runs all stages end-to-end with state tracking via Material Passport. state_tracker_agent, integrity_verification_agent, collaboration_depth_agent, and claim_ref_alignment_audit_agent are dispatched by the orchestrator at the appropriate checkpoints.
Mode B — phase-by-phase (cross-session resume): User invokes one phase agent at a time across sessions, typically via ARS_PASSPORT_RESET=1 + resume_from_passport=<hash> (see references/passport_as_reset_boundary.md).
In Mode B, single-phase agents (Bucket A per docs/design/2026-05-18-ars-v3.9.2-agent-phase-classification.md) in the downstream skills (deep-research, academic-paper, academic-paper-reviewer) stay strictly within their assigned phase for writes. The 5 agents in academic-pipeline itself are all cross-phase / meta by design (Bucket C/D) — they have no fence by design:
pipeline_orchestrator_agent (D — orchestrator, full pipeline visibility)
Routing into Mode B requires explicit user signal — /ars-<mode> slash command or [direct-mode] prefix. Ambiguous cross-phase input defaults to clarification per .claude/CLAUDE.md Routing Discipline + shared/references/intent_clarification_protocol.md. Critically: if pipeline_orchestrator_agent is dispatched on ambiguous cross-phase materials, the orchestrator itself currently cannot reconcile (this is the v3.10 conductor #134 work) — v3.9.2 routes such cases to clarification BEFORE the orchestrator runs.
Enforcement (v3.9.2): Phase Boundary blocks on downstream Bucket A agents + advisory verifier (scripts/check_pipeline_integrity.py) + a deterministic PreToolUse write-scope guard in hook-enabled runtimes (#134 rescope, PR #294). Multi-phase envelope + orchestrator structured intake remain forward-scope (#134 Slices 3-5).
⚠️ IRON RULE: Stage 4.5 must PASS with zero issues to proceed to Stage 5. Stage 4.5 verifies from scratch independently.
⚠️ IRON RULE (v3.2): Both Stage 2.5 and Stage 4.5 must also run the AI Research Failure Mode Checklist — a 7-mode taxonomy extending the citation hallucination checks into implementation bugs, hallucinated results, shortcut reliance, bug-as-insight, methodology fabrication, and pipeline-level frame-lock. If any of the 7 modes is SUSPECTED, or if Modes 1/3/5/6 are INSUFFICIENT EVIDENCE, the pipeline blocks and the user must acknowledge (confirm / override with reasoning / revise) before the pipeline proceeds. There is no --no-block escape hatch. Stage 6 PROCESS SUMMARY then reports the full failure-mode audit log as part of the AI Self-Reflection Report.
See references/integrity_review_protocol.md for the 5-phase citation/claim verification procedures.
See references/ai_research_failure_modes.md for the 7-mode AI research failure checklist and block/override logic.
Maximum 1 round of RE-REVISE (Stage 4'): If Stage 3' gives Major, enter Stage 4' for revision then proceed directly to Stage 4.5 (no return to review)
Pipeline overrides academic-paper's max 2 revision rule: In the pipeline, revisions are limited to Stage 4 + Stage 4' (one round each), replacing academic-paper's max 2 rounds rule
Mark unresolved issues as Acknowledged Limitations
Provide cumulative revision history (each round's decision, items addressed, unresolved items)
Early-Stopping Criterion (v3.2)
At the end of each revision round, if delta < 3 points on the 0-100 rubric AND no P0 issues remain, suggest stopping the revision loop ("converged"). User can override. Hard cap: 2 full revision loops (Stage 4 + Stage 4').
At pipeline start, estimate token cost based on paper length, mode, and cross-model toggle. Present estimate and ask for user confirmation before Stage 1 begins.
Alongside the token estimate, present the interaction-count budget: long-horizon document corruption compounds with the number of document round-trips, not with token volume (DELEGATE-52, arXiv:2604.15597). Enumerate the round-trip caps the pipeline already enforces — 2 full revision loops (Early-Stopping above), 8 + 5 Socratic coaching rounds (Stage 3→4 / 3'→4'), and the integrity-gate fix→re-verify loop at Stages 2.5/4.5 — and state the worst-case round-trip total those caps imply for the chosen mode. At each stage checkpoint, report the accumulated round-trip count next to the stage status. Advisory only: the count never blocks; the per-loop caps remain the enforcement layer. A run that exceeds its stated worst case signals a loop the caps do not cover — surface that explicitly rather than silently continuing.
Reproducibility
Every pipeline artifact is versioned, hashed, and auditable.
See references/reproducibility_audit.md for standardized workflow guarantees, audit trail format, and artifact tracking.
Stage 6: Process Summary Protocol
Produces the final process record: paper creation journey, collaboration quality evaluation (6 dimensions, 1-100), and AI self-reflection report.
Terminal semantics (#528): Stage 6 is non-mandatory — the user may decline it at the Stage 5 completion checkpoint (Stage 6 marked skipped; the pipeline still terminates completed). When it runs, after the process record is delivered the orchestrator prompts for a terminal acknowledgement — finish / end / done / confirm, or an unambiguous natural-language equivalent that accepts the deliverables. On acknowledgement, Stage 6 is marked completed and the pipeline global state is set to completed; change requests (the other language version, content corrections) keep Stage 6 in_progress and are not acknowledgements. See references/pipeline_state_machine.md § Stage 6 terminal semantics.
See references/process_summary_protocol.md for full workflow, required content structure, scoring dimensions, and output specifications.
Collaboration Depth Observer (v3.5.0, advisory only — never blocks)
The collaboration_depth_agent observes the user's collaboration pattern with the pipeline. It is advisory only and never blocks progression at any checkpoint. It is non-blocking by design and carries blocking: false in its frontmatter as a structural guarantee.
When invoked: every FULL checkpoint, every SLIM checkpoint, and during Stage 6 record compilation (the whole-pipeline pass runs before the Process Record is generated and delivered, so its output can be a chapter of the record the user acknowledges). MANDATORY checkpoints (Stages 2.5 / 4.5 integrity gates) do not invoke the observer — those are integrity concerns and must not be diluted.
What it does: reads the dialogue range for the just-completed stage (at checkpoints) or the whole pipeline (during Stage 6 record compilation), scores the pattern against the canonical rubric at shared/collaboration_depth_rubric.md, and emits an advisory block/chapter. Dimensions: Delegation Intensity, Cognitive Vigilance, Cognitive Reallocation, Zone Classification (Zone 1 / Zone 2 / Zone 3). Rubric is based on Wang & Zhang (2026) IJETHE 23:11 (DOI 10.1186/s41239-026-00585-x).
The user's collaboration pattern (delegation intensity, vigilance, reallocation)
No — never blocks. Advisory only.
Non-blocking guarantees:
Observer output never appears on the "Flagged" line of any checkpoint.
The Ready to proceed? prompt is unchanged by observer output.
blocked_by: collaboration_depth_agent is never a legal state in state_tracker.
If observer frontmatter ever asserts blocking: true, the orchestrator must refuse to dispatch it.
Cross-model: when ARS_CROSS_MODEL is set, the observer runs on both models and flags any dimension divergence > 2 points. Scores are never silently averaged across models.
See agents/collaboration_depth_agent.md for full scoring procedure and anti-sycophancy discipline; shared/collaboration_depth_rubric.md for the canonical 4-dimension rubric.
Anti-Patterns
Explicit prohibitions to prevent common failure modes:
#
Anti-Pattern
Why It Fails
Correct Behavior
1
Skipping integrity checks
"The paper looks fine, skip Stage 2.5/4.5"
Integrity checks are MANDATORY; they cannot be auto-skipped regardless of perceived quality
2
Orchestrator doing substantive work
Pipeline orchestrator writes content or reviews the paper
Orchestrator only dispatches and coordinates; substantive work belongs to the sub-skills
3
Auto-advancing past MANDATORY checkpoints
Moving to next stage without user confirmation at FULL checkpoints
MANDATORY checkpoints require explicit user input before proceeding
4
Quality degradation across stages
Stage 4 revision is worse than Stage 2 draft because context window is exhausted
If Stage N output quality < Stage N-1, PAUSE and reload core principles before continuing
5
Silently dropping reviewer concerns
Revision addresses 8 of 10 concerns and hopes nobody notices
The R&R tracking table must account for every concern with explicit status
6
Re-verifying only known issues at Stage 4.5
Final integrity check only re-checks Stage 2.5 findings
Stage 4.5 must verify from scratch independently; revision may introduce new issues
7
Inflating Collaboration Quality scores
Giving 90/100 to avoid awkward self-criticism
Honesty first: no inflation, no pleasantries; cite specific evidence for every score
8
Bypassing the Failure Mode Checklist block (v3.2)
"The 7-mode checklist is new, let's skip it this run"
Stage 2.5/4.5 Failure Mode Checklist is MANDATORY and BLOCKING; no --no-block flag exists; overrides require user reasoning recorded for Stage 6
Quality Standards
Dimension
Requirement
Stage detection
Correctly identify user's current stage and available materials
Mode recommendation
Recommend appropriate mode based on user preferences and material status
Material handoff
Stage-to-stage handoff materials are complete and correctly formatted
State tracking
Pipeline state updated in real time; Progress Dashboard accurate
Mandatory checkpoint
User confirmation required after each stage completion
Mandatory integrity check
Stage 2.5 and 4.5 cannot be skipped, must PASS
Mandatory failure mode checklist (v3.2)
Stage 2.5 and 4.5 must run the 7-mode AI research failure checklist; suspected failures block; overrides require user reasoning
No overstepping
⚠️ IRON RULE: Orchestrator does not perform substantive research/writing/reviewing, only dispatching
No forcing
⚠️ IRON RULE: User can pause or exit pipeline at any time (but cannot skip integrity checks)
Reproducible
Same input follows the same workflow across different sessions
Convergence-aware stopping (v3.2)
If delta < 3 points AND no P0 issues, suggest stopping revision loop; user can override
Budget transparency (v3.2; #388)
Token cost estimate + interaction-count budget (round-trip caps + accumulated count at checkpoints, advisory) + user confirmation at pipeline start
Error Recovery
Stage
Error
Handling
Intake
Cannot determine entry point
Ask user what materials they have and their goal
Stage 1
deep-research not converging
Suggest mode switch (socratic -> full) or narrow scope
Stage 2
Missing research foundation
Suggest returning to Stage 1 to supplement research
Stage 2.5
Still FAIL after 3 correction rounds
List unverifiable items; user decides whether to continue
Stage 3
Review result is Reject
Provide options: major restructuring (Stage 2) or abandon
Stage 4
Revision incomplete on all items
List unaddressed items; ask whether to continue
Stage 3'
Verification still has major issues
Enter Stage 4' for final revision
Stage 4'
Issues remain after revision
Mark as Acknowledged Limitations; proceed to Stage 4.5
Stage 4.5
Final verification FAIL
Fix and re-verify (max 3 rounds)
Any
User leaves midway
Save pipeline state; can resume from breakpoint next time
Any
Skill execution failure
Report error; suggest retry, pause, or mode switch. Do not skip mandatory integrity or failure-mode gates
Agent File References
Agent
Definition File
pipeline_orchestrator_agent
agents/pipeline_orchestrator_agent.md
state_tracker_agent
agents/state_tracker_agent.md
integrity_verification_agent
agents/integrity_verification_agent.md
collaboration_depth_agent
agents/collaboration_depth_agent.md
claim_ref_alignment_audit_agent
agents/claim_ref_alignment_audit_agent.md
Reference Files
Reference
Purpose
references/pipeline_state_machine.md
Complete state machine definition: all legal transitions, preconditions, actions
references/plagiarism_detection_protocol.md
Phase D originality verification protocol + self-plagiarism + AI text characteristics
references/mode_advisor.md
Unified cross-skill decision tree: maps user intent to optimal skill + mode
Mid-entry example starting from Stage 2.5 (existing paper -> integrity check -> review -> revision -> finalization)
Output Language
Follows user language. Academic terminology retained in English.
Integration with Other Skills
academic-pipeline dispatches the following skills (does not do work itself):
Stage 1: deep-research
- socratic mode: Guided research exploration
- full mode: Complete research report
- quick mode: Quick research summary
Stage 2: academic-paper
- plan mode: Socratic chapter-by-chapter guidance
- full mode: Complete paper writing
Stage 2.5: integrity_verification_agent (Mode 1: pre-review)
Stage 4.5: integrity_verification_agent (Mode 2: final-check)
Stage 3: academic-paper-reviewer
- full mode: Complete 5-person review (EIC + R1/R2/R3 + Devil's Advocate)
Stage 3': academic-paper-reviewer
- re-review mode: Verification review (focused on revision responses)
Stage 4/4': academic-paper (revision mode)
Stage 5: academic-paper (format-convert mode)
- Step 1: Consume the citation-style decision recorded at the Stage 5 entry gate; ask which academic formatting style (APA 7.0 / Chicago / IEEE, etc.) only when no gate decision exists (direct format-convert / mid-entry invocation)
- Step 2: Produce MD, then generate DOCX via Pandoc when available (otherwise provide conversion instructions)
- Step 3: Produce LaTeX (using corresponding document class, e.g., apa7 class for APA 7.0)
- Step 4: After user confirms content is correct, tectonic compiles PDF (final version)
- Fonts: Times New Roman (English) + Source Han Serif TC VF (Chinese) + Courier New (monospace)
- ⚠️ IRON RULE: PDF must be compiled from LaTeX (HTML-to-PDF is prohibited)
Dispatched (Stage 3 first review, Stage 3' verification review)
Model Tiering (#517, optional)
When ARS_MODEL_TIERING is set, the dispatching session routes this skill's agents per shared/model_tiering.md (canonical: the full 39-agent judgment/execution table + rules). Compact rule:
Unset (default): every agent inherits the session model — byte-equivalent pre-#517 behavior.
economy (frontier-tier session): execution-type agents dispatch ONE tier below the session model — floor Opus-class, never lower; judgment-type agents stay on the session model. No-op at or below the floor (announce once).
quality-boost (below-frontier session): judgment-type agents at the checkpoint surfaces (Stage 2.5/4.5 gates; the opt-in Stage 4→5 claim–ref audit; final review) jump UP to the frontier tier (however many tiers away — not a single increment); nothing is ever downgraded. No-op at the frontier (announce once).
Unknown values → warn once, behave as unset. Tiers are relative positions, never hard-pinned model ids. When a direction is active, route repeated same-stage calls to the SAME worker so its prompt cache accumulates; unset means dispatch shapes stay byte-equivalent too.
