academic-pipeline

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":"3.11.1","last_updated":"2026-06-06","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 v3.11.1 — 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:

1. Mandatory user confirmation checkpoints — Each stage completion requires user confirmation before proceeding to the next step
2. Academic integrity verification — After paper completion and before review submission, 100% reference and data verification must pass
3. Two-stage review — First full review + post-revision focused verification review
4. Final integrity check — After revision completion, re-verify all citations and data are 100% correct
5. Reproducible — Standardized workflow producing consistent quality assurance each time
6. 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:

1. Detect the user's current stage and available materials
2. Recommend the optimal mode for each stage
3. Dispatch the corresponding skill for each stage
4. After each stage completion, proactively prompt and wait for user confirmation
5. 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

1. Stage 1 RESEARCH -> user confirmation -> Stage 2
2. Stage 2 WRITE -> user confirmation -> Stage 2.5
3. Stage 2.5 INTEGRITY -> PASS -> Stage 3 (FAIL -> fix and re-verify, max 3 rounds)
4. Stage 3 REVIEW -> Accept -> Stage 4.5 / Minor|Major -> Stage 4 / Reject -> Stage 2 or end
5. Stage 4 REVISE -> user confirmation -> Stage 3'
6. Stage 3' RE-REVIEW -> Accept|Minor -> Stage 4.5 / Major -> Stage 4'
7. Stage 4' RE-REVISE -> user confirmation -> Stage 4.5 (no return to review)
8. Stage 4.5 FINAL INTEGRITY -> PASS (zero issues) -> Stage 5 (FAIL -> fix and re-verify)
9. Stage 5 FINALIZE -> MD -> DOCX via Pandoc when available (otherwise instructions) -> ask about LaTeX -> confirm -> PDF -> Stage 6
10. 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

1. First checkpoint: always FULL
2. After 2+ consecutive "continue" without review: prompt user awareness ("You've continued [N] times in a row. Want to review progress?")
3. Integrity boundaries (Stage 2.5, 4.5): always MANDATORY
4. Review decisions (Stage 3, 3'): always MANDATORY
5. Before finalization (Stage 5): always MANDATORY
6. All other stages: start FULL, downgrade to SLIM if user says "just continue"

Checkpoint Rules

1. ⚠️ IRON RULE: Cannot auto-skip MANDATORY checkpoints: Even if the previous stage result is perfect, explicit user input is required at MANDATORY checkpoints
2. User can adjust: At FULL and MANDATORY checkpoints, users can modify the mode or settings for the next step
3. Pause-friendly: Users can pause at any checkpoint and resume later
4. 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)
5. 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:

1. Citation integrity: Are there any unverified citations in the latest output?
2. Sycophantic concession: Did the latest stage uncritically accept all feedback without pushback?
3. Quality trajectory: Is the latest output ≥ the quality of the previous stage? If declining, PAUSE and flag.
4. Scope discipline: Did the latest stage add content not requested by the user or the revision roadmap?
5. 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	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
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

Resolve the paper output location once, here at intake (see skills/resource-resolver/SKILL.md), so it's ready by the time Stage 5 needs it — don't re-ask at every stage:

# macOS / Linux / Git Bash:
RESOLVE="$(find ~/.claude -path "*/resource-resolver/scripts/resolve.py" | head -1)"
ROLE_JSON=$(python "$RESOLVE" --role paper --json)
PAPER_DIR=$(echo "$ROLE_JSON" | python3 -c "
import json, sys
d = json.load(sys.stdin)
if d.get('status') == 'resolved':
    print(d['primary'])
")

# Windows (PowerShell):
$RESOLVE = (Get-ChildItem $env:USERPROFILE\.claude -Recurse -Filter resolve.py |
    Where-Object FullName -like "*resource-resolver*" | Select-Object -First 1).FullName
$RoleJson = python $RESOLVE --role paper --json | ConvertFrom-Json
$PAPER_DIR = if ($RoleJson.status -eq "resolved") { $RoleJson.primary } else { "" }

Only a "resolved" status yields a usable path — a stale_mapping response also carries a non-empty primary, so never read primary without checking status. If $PAPER_DIR comes back empty, inspect $ROLE_JSON and follow the branching rules in the "Calling convention for other skills" section of skills/resource-resolver/SKILL.md: an error key means surface message and stop; status: "stale_mapping" means the configured paper directory is gone and the user must re-confirm it (do not silently recreate it); only "unresolved" / "no_candidates" leads to the first-use confirmation flow. Resolve this before continuing intake.

Shell state does not persist across separate tool-call invocations, so do not rely on $PAPER_DIR still being set by the time Stage 5 runs. Carry the resolved path through pipeline state (state_tracker_agent) and substitute it as a literal value when Stage 5 (format-convert -> final output) writes there, or re-run the resolve snippet in that same call.

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, plus the `$PAPER_DIR` resolved at intake, 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):

--- 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)
• state_tracker_agent (D — meta state, all phases)
• integrity_verification_agent (C — Stage 2.5 / 4.5 cross-skill gate)
• collaboration_depth_agent (C — FULL/SLIM checkpoints + pipeline completion, advisory-only)
• claim_ref_alignment_audit_agent (C — opt-in claim audit, phase-orthogonal)

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): prompt-level via Phase Boundary blocks on downstream Bucket A agents + advisory verifier (scripts/check_pipeline_integrity.py). Deterministic PreToolUse hook + multi-phase envelope + orchestrator structured intake deferred to v3.10 active conductor (#134).

---

Integrity Review Protocol

Stage 2.5 (pre-review) and Stage 4.5 (post-revision) verification. 5-phase protocol: references → citation context → statistical data → originality → claims.

⚠️ 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.

• [v3.4.0] compliance_agent runs mode-aware PRISMA-trAIce + RAISE compliance check; tier-based block semantics. See shared/compliance_checkpoint_protocol.md.

---

Two-Stage Review Protocol

Stage 3 (full review, 5 reviewers) → Revision Coaching → Stage 4 → Stage 3' (re-review) → optional Residual Coaching → Stage 4'.

See references/two_stage_review_protocol.md for detailed stage flows and coaching dialogue limits.

---

Mid-Entry Protocol

Users can enter from any stage. The orchestrator will:

1. Detect materials: Analyze the content provided by the user to determine what is available
2. Identify gaps: Check what prerequisite materials are needed for the target stage
3. Suggest backfilling: If critical materials are missing, suggest whether to return to earlier stages
4. Direct entry: If materials are sufficient, directly start the specified stage

Important: mid-entry cannot skip Stage 2.5

• If the user brings a paper and enters directly, go through Stage 2.5 (INTEGRITY) first before Stage 3 (REVIEW)
• Only exception: User can provide a previous integrity verification report and content has not been modified

---

External Review Protocol

Handles external (human) reviewer feedback integration. 4-step workflow: Intake & Structuring → Strategic Revision Coaching → Revision & Response → Self-Verification.

See references/external_review_protocol.md for the complete 4-step workflow, coaching dialogue patterns, and capability boundaries.

---

Progress Dashboard

ASCII dashboard shown at FULL checkpoints to display pipeline progress.

See references/progress_dashboard_template.md for the dashboard template.

---

Revision Loop Management

• Stage 3 (first review) -> Stage 4 (revision) -> Stage 3' (verification review) -> Stage 4' (re-revision, if needed) -> Stage 4.5 (final verification)
• 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').

Budget Transparency (v3.2)

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.

---

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.

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 after Stage 6 (pipeline completion). 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 (at completion), 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).

Distinction from existing mechanisms:

Mechanism	What it evaluates	Blocking?
integrity_verification_agent (Stages 2.5 / 4.5)	Paper content — references, citations, data	Yes (blocking gate)
Stage 6 Collaboration Quality Evaluation (6 dims, 1–100)	AI's self-reflection on its own behaviour	No, but produced once only
collaboration_depth_agent (this observer)	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)	Token cost estimate + 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

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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

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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
references/claim_verification_protocol.md	Phase E claim verification protocol: claim extraction, source tracing, cross-referencing, verdict taxonomy
references/claim_audit_calibration_protocol.md	v3.8 #103 claim_ref_alignment audit calibration: gold-set shape (T-C3), threshold gates FNR<0.15 / FPR<0.10 (T-C1), per-class FNR/FPR reporting (T-C2). Re-run via PYTHONPATH=. python3 -m unittest scripts.test_claim_audit_calibration -v.
references/ai_research_failure_modes.md	7-mode AI research failure checklist (Lu 2026), run at Stage 2.5 + 4.5 with blocking behaviour, reported at Stage 6
references/team_collaboration_protocol.md	Multi-person team coordination: role definitions, handoff protocol, version control, conflict resolution
references/integrity_review_protocol.md	Stage 2.5 + 4.5 integrity verification: 5-phase protocol details
references/two_stage_review_protocol.md	Two-stage review: Stage 3 full review + Stage 3' verification review
references/external_review_protocol.md	External (human) reviewer feedback: 4-step intake/coaching/revision/verification
references/process_summary_protocol.md	Stage 6: collaboration quality evaluation + AI self-reflection report
references/reproducibility_audit.md	Standardized workflow guarantees + audit trail format
references/progress_dashboard_template.md	ASCII progress dashboard template
references/reinforcement_content.md	Stage-specific reinforcement focus table for transitions

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Templates

Template	Purpose
templates/pipeline_status_template.md	Progress Dashboard output template

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Examples

Example	Demonstrates
examples/full_pipeline_example.md	Complete pipeline conversation log (Stage 1-5, with integrity + 2-stage review)
examples/mid_entry_example.md	Mid-entry example starting from Stage 2.5 (existing paper -> integrity check -> review -> revision -> finalization)

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Output Language

Follows user language. Academic terminology retained in English.

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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: Ask user which academic formatting style (APA 7.0 / Chicago / IEEE, etc.)
  - 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)

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Related Skills

Skill	Relationship
deep-research	Dispatched (Stage 1 research phase)
academic-paper	Dispatched (Stage 2 writing, Stage 4/4' revision, Stage 5 formatting)
academic-paper-reviewer	Dispatched (Stage 3 first review, Stage 3' verification review)

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Version Info

Item	Content
Skill Version	3.11.1
Last Updated	2026-06-06
Maintainer	Cheng-I Wu
Dependent Skills	deep-research v2.0+, academic-paper v2.0+, academic-paper-reviewer v1.1+
Role	Full academic research workflow orchestrator

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Changelog

See references/changelog.md for full version history.