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academic-pipeline-v1

Orchestrator for the full academic research pipeline: literature search -> research -> write -> integrity check -> review -> revise -> re-review -> re-revise... Skill: academic-pipeline-v1 Owner: eric-promax Summary: Orchestrator for the full academic research pipeline: literature search -> research -> write -> integrity check -> review -> revise -> re-review -> re-revise... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-05-14T02:46:10.557Z | user Academic Pipeline v1.0.0 — Initial Release - Introduces a full academic research workflow orchestrator covering 12 stages from

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Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed May 14, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s1746e7bcag3t464dy9vye9kr586pe2v:academic-pipeline

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-eric-promax-academic-pipeline/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

109,677 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: academic-pipeline
description: "Orchestrator for the full academic research pipeline: literature search -> research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> humanize -> finalize. Coordinates academic-search, deep-research, academic-paper, academic-paper-reviewer, and humanizer into a seamless 12-stage workflow with mandatory integrity verification, two-stage peer review, de-AI processing, 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: "3.5"
  last_updated: "2026-05-13"
  depends_on: "ima-skills, academic-search, deep-research, academic-paper, academic-paper-reviewer, humanizer, humanizer-zh"
  status: active
  related_skills:
    - ima-skills
    - academic-search
    - deep-research
    - academic-paper
    - academic-paper-reviewer
    - humanizer
    - humanizer-zh
---

# Academic Pipeline v3.5 — 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.

**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 2 (RESEARCH)

**Mid-entry (existing paper):**
```
I already have a paper, help me review it
```
--> academic-pipeline detects mid-entry, starting from Stage 4 (INTEGRITY)

**Revision mode (received reviewer feedback):**
```
I received reviewer comments, help me revise
```
--> academic-pipeline detects, starting from Stage 7 (REVISE)

**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

_meta.json

{
  "ownerId": "kn79h8an2b3e37qcycc96jzpjs86qv7g",
  "slug": "academic-pipeline",
  "version": "1.0.0",
  "publishedAt": 1778726770557
}

references/ai_research_failure_modes.md

# AI Research Failure Mode Checklist

**Status**: v3.2
**Parent skill**: `academic-pipeline`
**Used at**: Stage 2.5 INTEGRITY (blocking), Stage 4.5 FINAL INTEGRITY (blocking), Stage 6 PROCESS SUMMARY (reporting only)
**Source**: Lu et al. (2026). Towards end-to-end automation of AI research. *Nature* 651, 914-919. doi:10.1038/s41586-026-10265-5 — Limitations section, Figure 2 (examples of failures in The AI Scientist's own accepted paper), Supplementary Information A.2.9 (debugging traces).

---

## Why this checklist exists

Lu et al. built the first autonomous AI research system to pass blind peer review (ICLR 2025 workshop). Their Limitations section enumerates the specific failure modes they observed — and most of them apply equally to human-in-the-loop AI research workflows like ARS.

These failures are dangerous because **they look like competent work**. A paper containing a hallucinated experimental result reads the same as a paper containing a real one. A shortcut-relying result reads the same as a genuine generalization. A methodology section describing experiments that were never actually run reads the same as a faithful account. The existing integrity verification catches citation hallucinations but is weak on the other failure modes.

The checklist exists to make these failures legible: **at Stage 2.5 and Stage 4.5, the integrity reviewer must explicitly rule out each of the 7 modes, or flag which are suspected and block the pipeline until the user acknowledges.**

This also extends the existing 5-type citation hallucination taxonomy (in `academic-paper-reviewer` references) into a broader 7-type AI research hallucination taxonomy. Citation hallucinations become mode 2 below.

---

## The 7 failure modes

### Mode 1: Implementation bug passing AI self-review

**What it is**: The analysis or experiment code has a bug (off-by-one, wrong variable, silent division-by-zero, type coercion, wrong flag) that produces numerically plausible but scientifically wrong results. The AI runs the code, looks at the output, sees nothing "obviously" wrong, and incorporates the result into the paper.

**Lu 2026 example**: Supplementary A.2.9 traces show The AI Scientist repeatedly accepting experimental runs that had silent crashes or numerical instabilities because the top-level metric "looked reasonable". Figure 2 shows an ICLR reviewer catching one such issue in the accepted paper — the paper's main analysis depended on a setup that the code did not actually implement.

**Detection questions at Stage 2.5**:
- For every numerical result in the draft: does the user have a saved log, notebook, or script run that produced this number? If yes, was the exit code 0 and were there zero warnings? If no log is saved, flag.
- Are any effect sizes suspiciously round (exactly 0.5, exactly 2x baseline, exactly zero variance across runs)? Suspiciously round numbers are a common signal of a constant leaking through a broken pipeline.
- Do error bars / confidence inte

references/changelog.md

# Changelog

| Version | Date | Changes |
|---------|------|---------|
| 2.7 | 2026-03-27 | **Style Profile in Material Passport**: Pipeline orchestrator now carries optional Style Profile (Schema 10 in `shared/handoff_schemas.md`) through all stages. Produced by academic-paper intake Step 10 when user provides past writing samples. Consumed by draft_writer (Stage 2) and report_compiler (Stage 1) as soft writing voice guide. Does not affect integrity verification or review stages. Coordinates with deep-research v2.4 and academic-paper v2.5 |
| 2.6 | 2026-03-08 | **Handoff Data Schema**: Enhanced `shared/handoff_schemas.md` with 9 comprehensive schemas (RQ Brief, Bibliography, Synthesis, Paper Draft, Integrity Report, Review Report, Revision Roadmap, Response to Reviewers, Material Passport) with full field definitions, type constraints, and validation rules; orchestrator validates output against schemas before each transition. **Adaptive Checkpoint System**: Replaced static checkpoint template with 3-tier system (FULL/SLIM/MANDATORY) based on stage criticality and user engagement; FULL checkpoints include decision dashboard with metrics; SLIM auto-continues for experienced users; MANDATORY cannot be bypassed at integrity/review/finalization boundaries; awareness guard after 4+ auto-continues. **Mode Advisor**: New `references/mode_advisor.md` with unified cross-skill decision tree, common misconceptions table, user archetype recommendations, decision flowchart, and anti-patterns guide. **Team Collaboration Protocol**: New `references/team_collaboration_protocol.md` with 5 role definitions, per-transition handoff procedures, git branching/tagging strategy, conflict resolution matrix, and communication templates; state tracker extended with `assigned_to`, `approval_gate`, `team_notes` per stage and `schema_validation_log`. **Phase E Claim Verification**: New `references/claim_verification_protocol.md` with E1 claim extraction, E2 source tracing, E3 cross-referencing; verdict taxonomy (VERIFIED / MINOR_DISTORTION / MAJOR_DISTORTION / UNVERIFIABLE / UNVERIFIABLE_ACCESS); severity mapping (MAJOR_DISTORTION -> SERIOUS, UNVERIFIABLE -> SERIOUS, MINOR_DISTORTION -> MINOR, UNVERIFIABLE_ACCESS -> MEDIUM); integrated into integrity_verification_agent Mode 1 (30% spot-check) and Mode 2 (100%); pass/fail criteria updated to include Phase E verdicts. **Mid-Entry Material Passport Check**: Pipeline orchestrator now validates Material Passport on mid-entry; decision tree checks verification_status, freshness (< 24 hours), and content modification (version_label comparison); offers skip/spot-check/full re-verify options for Stage 2.5 when passport is valid; passport freshness validation rules added to `shared/handoff_schemas.md` |
| 2.5 | 2026-03-08 | External Review Protocol: structured intake of real journal reviewer feedback (text/PDF/DOCX); 4-step workflow (parse -> strategic coaching -> revise + Response to Reviewers -> completeness check); differentiated be

references/claim_verification_protocol.md

# Claim Verification Protocol (Phase E)

## Purpose
Verifies that quantitative and factual claims in the paper are accurately supported by their cited sources. Phase A-D verify that references exist and are original; Phase E verifies that claims derived from those references are truthful.

## Scope
- All numerical claims (percentages, counts, effect sizes, p-values)
- All categorical assertions ("X is the largest...", "Y was the first to...")
- All trend claims ("increasing", "declining", "stable")
- All causal claims ("X causes Y", "X leads to Y")

## E1: Claim Extraction
- Scan the paper for all quantitative/factual claims
- For each claim, record: claim text, cited source(s), paper section, page/line
- Expected output: Claim Registry table

## E2: Source Tracing
- For each claim, locate the specific passage in the cited source that supports it
- Use WebSearch + DOI lookup to find the original source
- If source is behind paywall, note as UNVERIFIABLE_ACCESS

## E3: Cross-Referencing
- Compare claim text vs source text
- Check: exact numbers, date ranges, population descriptions, methodology descriptions
- Flag any discrepancies

## Verdict Taxonomy

| Verdict | Definition | Severity | Example |
|---------|-----------|----------|---------|
| VERIFIED | Claim matches source exactly or within rounding tolerance | None | Paper: "15.2%"; Source: "15.2%" |
| MINOR_DISTORTION | Claim paraphrases source but meaning is preserved | MINOR | Paper: "about 15%"; Source: "15.2%" |
| MAJOR_DISTORTION | Claim oversimplifies, exaggerates, or misrepresents source | SERIOUS | Paper: "declined sharply"; Source: "declined by 2.1%" |
| UNVERIFIABLE | Source doesn't contain the claimed information | SERIOUS | Paper cites Smith (2020) for a claim, but Smith (2020) doesn't discuss this topic |
| UNVERIFIABLE_ACCESS | Source exists but full text not accessible for verification | MEDIUM | Paywalled journal article |

## Sampling Strategy
- Mode 1 (pre-review): 30% random sample of claims (minimum 10 claims)
- Mode 2 (final-check): 100% of claims

## Output Format

### Claim Verification Report
| # | Claim | Source | Section | Verdict | Detail |
|---|-------|-------|---------|---------|--------|
| 1 | [claim text] | [source] | [section] | VERIFIED | Exact match |
| 2 | [claim text] | [source] | [section] | MAJOR_DISTORTION | Paper says X, source says Y |

### Summary
- Total claims checked: [N]
- VERIFIED: [N]
- MINOR_DISTORTION: [N]
- MAJOR_DISTORTION: [N] (must be 0 for PASS)
- UNVERIFIABLE: [N] (must be 0 for PASS)
- UNVERIFIABLE_ACCESS: [N] (noted but does not block PASS)

## Pass/Fail Criteria
- PASS: Zero MAJOR_DISTORTION + Zero UNVERIFIABLE
- FAIL: Any MAJOR_DISTORTION or UNVERIFIABLE
- PASS_WITH_NOTES: Only MINOR_DISTORTION and/or UNVERIFIABLE_ACCESS

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