industrial-ai-research

SKILL.md

Industrial AI Research

Run a lean, source-aware research workflow for Industrial AI.

Capability Summary

Structured literature research for Industrial AI and automation topics

Mandatory four-question intake before any search or synthesis

Venue-aware source prioritization (arXiv, IEEE, automation venues)

Four deliverable modes: research-brief, literature-map, venue-ranked survey, research-gap memo

Contrarian synthesis pass to surface contradictions and under-explored gaps

Consensus -> disagreement -> limitations -> gap synthesis discipline for survey prose

Survey draft generation: outline-first writing with per-section evidence packs and optional LaTeX export

Triggering
Use this skill when the user wants to:

Survey Industrial AI literature on a specific subtopic

Compare papers across venues or methods within Industrial AI

Identify research gaps in predictive maintenance, scheduling, anomaly detection, or smart manufacturing

Produce a structured research report with source-backed evidence

Draft a structured survey on an Industrial AI subtopic

Produce a survey manuscript with taxonomy, evidence packs, and section-by-section writing

Do Not Use

Writing or compiling LaTeX/Typst papers (use latex-paper-en, latex-thesis-zh, or typst-paper).
Note: survey-draft mode produces Markdown by default; for LaTeX output, it delegates final formatting to latex-paper-en.

Auditing paper quality or formatting (use paper-audit)

Systematic reviews or meta-analyses requiring IRB or clinical ethics

Topics outside the Industrial AI and automation domain

Auditing an existing paper's quality or formatting (use paper-audit)

Editing LaTeX/Typst source files (use the appropriate writing skill)

Safety Boundaries

Never fabricate paper metadata (title, authors, venue, year, DOI)

Never present preprints as peer-reviewed publications

Never start synthesis before intake questions are answered

Never suppress contradictions or conflicting evidence

Never use Tier 4 sources (blogs, press releases) as primary evidence

Core Rules

Ask the user the four intake questions (see references/question-flow.md) before starting any search or synthesis.

Keep the skill workflow in English only, even when the requested report language is not English.

Prefer recent arXiv plus top IEEE and automation venues over generic web articles.

Default to the last 3 years, but keep seminal older work when it is still necessary for context.

Cite every substantive claim and separate verified evidence from inference.

In survey-draft mode, complete all structure and evidence phases before generating any prose. Structure phases produce YAML/tables only.

Preserve contradictions explicitly; do not flatten conflicting findings into fake agreement.

Intake Contract

Always start by asking the four intake questions defined in references/question-flow.md:

Report language (English / Simplified Chinese / Bilingual summary)

Deliverable mode (research-brief / literature-map / venue-ranked survey / research-gap memo / survey-draft)

Time window (last 12 months / last 3 years / last 5 years / custom)

Industrial AI emphasis (predictive maintenance / intelligent scheduling / industrial anomaly detection / smart manufacturing and process optimization / CPS and edge AI / robotics crossover)

If the user does not choose, default to last 3 years and the subdomain implied by their prompt.

Intake Resolution Rules

Resolve as many intake fields as possible from the user prompt before asking follow-up questions.

If all four intake fields are already explicit or safely inferable, do not restate them as questions; lock them, announce the locked choices, and proceed.

If some intake fields are missing, ask only for the missing fields in one compact follow-up block rather than re-asking the full questionnaire.

If the user asks for the "latest", "recent", "current", or "today's" work without a window, default to last 12 months and report the absolute year span you used in the final scope note.

If the topic is clearly outside Industrial AI scope, stop before search, name the boundary, and offer the closest supported framing instead of forcing a bad search.

If the user explicitly says "stop after outline" or another survey checkpoint, honor that checkpoint and do not advance to the next survey phase automatically.

Required Inputs

A concrete Industrial AI topic or question.

User choices for report language, deliverable mode, time window, and domain emphasis.

Optional preferences on peer-reviewed-only filtering, benchmarks vs deployment evidence, or desired output format.

If any intake item is missing, ask only for the unresolved items from references/question-flow.md before you search.

Source Strategy

Read these files before searching:

references/source-priority.md

references/venue-map.md

Primary sources:

arXiv: eess.SY, cs.AI

IEEE and automation anchors: T-ASE, CASE

Supporting crossover sources:

arXiv: cs.RO, cs.LG

IEEE robotics venues: ICRA, IROS, RA-L, T-RO

Adjacent industrial and control venues listed in references/venue-map.md

When the user asks for the latest work, prefer:

arXiv recent streams for rapid updates

top IEEE and automation venues for stronger publication filtering

secondary crossover venues only when they materially improve coverage

Workflow

Phase 1. Scope

Rewrite the request as a precise Industrial AI research objective.

Lock the report language, deliverable mode, time window, and domain emphasis.

State explicit in-scope and out-of-scope boundaries.

Phase 2. Search Plan

Build venue buckets and keyword groups from references/source-priority.md.

Separate primary sources from secondary crossover sources.

State the recency policy and any seminal-paper exceptions.

Phase 3. Source Collection

Gather papers from the prioritized source buckets.

Prefer official venue pages, arXiv recent listings, IEEE Xplore landing pages, and publisher or conference pages.

Record why each paper was included.

Phase 4. Verification and Triage

Check venue quality, publication type, year, and relevance.

Remove weak matches, duplicates, and generic blog-style sources.

Mark unreviewed preprints as preprints.

Phase 5. Synthesis

Cluster the shortlisted papers by problem, method, dataset, deployment setting, and evaluation style.

Surface trends, gaps, contradictions, and under-explored opportunities.

When contradictions exist, state them before drawing any research-gap conclusion.

Run a contrarian pass: what would challenge the dominant conclusion?

Phase 6. Report Assembly

Use the stable report structure from references/report-modes.md.

Every final report must include:

search scope

source buckets by venue

shortlisted papers

synthesis of trends and gaps

recommended next reading or next experiments

Survey-Draft Workflow (Phases S1–S4)

When the user selects survey-draft, Phases 1–4 (Scope, Search Plan, Source Collection, Verification) execute as normal, then S1–S4 replace the original Phases 5–6.

Phase S1. Outline Building

Read references/modules/SURVEY_OUTLINE.md.

Extract a taxonomy from the verified literature.

Build the section skeleton as structured YAML.

Present the outline to the user for approval.

CHECKPOINT: do not enter S2 until the user approves the outline.

Phase S2. Evidence Pack Assembly

Read references/modules/SURVEY_EVIDENCE.md.

Assemble an evidence pack for every H3 subsection.

Lock the citation scope for each subsection.

Produce structured evidence bundles (no prose).

Phase S3. Section-by-Section Writing

Read references/modules/SURVEY_WRITER.md.

Draft each H3 independently, grounded in its evidence pack.

Run the self-check gate on every H3 (depth, citation scope, tone).

Produce one Markdown file per H2 section.

Phase S4. Merge and Quality Gate

Read references/modules/SURVEY_MERGE.md.

Merge all section drafts into a single document.

Run cross-section consistency checks.

Apply the final quality checklist.

If the user requested LaTeX output, delegate to latex-paper-en.

Deliverable Modes

Read references/report-modes.md and follow the selected mode exactly.

research-brief: short, decision-ready overview

literature-map: thematic map across methods and subproblems

venue-ranked survey: grouped by source quality and venue tier

research-gap memo: open problems, design space, and next-step opportunities

survey-draft: taxonomy-driven survey manuscript with outline-first writing and optional LaTeX export

Output Contract

State the locked intake choices and any defaults you applied before synthesis.

Include a short search-method note: venue buckets used, recency policy, and any fallback or broadening step you had to apply.

Distinguish verified evidence from inference in every deliverable.

Label preprints explicitly as preprints.

For non-survey modes, produce a structured report that includes: scope, source buckets, shortlisted papers, synthesis, and next reading or next experiments.

For survey-draft, keep stage outputs format-specific:

S1: YAML outline only

S2: evidence packs or tables only

S3: section Markdown drafts grounded in the evidence packs

S4: merged Markdown survey with cross-section consistency notes

Survey prose should prefer consensus -> disagreement -> limitations -> gap over paper-by-paper narration.

If sources are sparse, inaccessible, or off-scope, say so directly and report the exact fallback you used.

Module Router

Module
Use when
Primary action
Read next

research
User selects any of the 4 report modes
Execute Phase 1–6 workflow
references/report-modes.md

survey-outline
User selects survey-draft (Phase S1)
Build taxonomy and section skeleton
references/modules/SURVEY_OUTLINE.md

survey-evidence
Outline approved by user (Phase S2)
Assemble per-H3 evidence packs
references/modules/SURVEY_EVIDENCE.md

survey-write
Evidence packs complete (Phase S3)
Draft prose per H3
references/modules/SURVEY_WRITER.md

survey-merge
All sections complete (Phase S4)
Merge, quality gate, optional LaTeX handoff
references/modules/SURVEY_MERGE.md

This skill is LLM-driven and does not include executable scripts. All phases are executed through web search, structured synthesis, and prompting.

Quality Bar

Read references/quality-checklist.md before finalizing.

Non-negotiable standards:

no unsupported claims

no venue-blind source mixing

no hiding contradictions

no synthesized report before intake questions are answered

no generic "latest research says" language without source-backed evidence

Error Handling

Zero results: Broaden keywords, relax the time window by one tier, and try adjacent venues. If still empty, report the negative result with the exact queries attempted.

Off-subdomain topic: State that the topic falls outside Industrial AI scope, suggest the closest supported subdomain, and ask the user whether to proceed or abort.

Inaccessible databases: Note which sources were unreachable, proceed with available sources, and flag the gap in the final report.

Too few papers (<5 shortlisted): Lower the time window threshold, include Tier 2/3 venues, and explicitly note the thin evidence base in the synthesis.

Reference Map

File
Phase
When to read

references/question-flow.md
Intake
Before asking the user any questions

references/source-priority.md
Search Plan
Before building venue buckets

references/venue-map.md
Search Plan
Before selecting specific venues

references/report-modes.md
Report Assembly
Before structuring the final output

references/quality-checklist.md
Report Assembly
Before finalizing the report

references/modules/SURVEY_OUTLINE.md
Survey S1
When building the survey outline

references/modules/SURVEY_EVIDENCE.md
Survey S2
When assembling evidence packs

references/modules/SURVEY_WRITER.md
Survey S3
When drafting survey sections

references/modules/SURVEY_MERGE.md
Survey S4
When merging and running quality gate

references/SURVEY_WRITING_GUIDE.md
Survey S1–S4
Survey writing philosophy reference

Examples

examples/predictive-maintenance.md

examples/intelligent-scheduling.md

examples/industrial-anomaly-detection.md

examples/survey-predictive-maintenance.md

Example Requests

“Research recent predictive maintenance papers from the last 3 years and return a research-brief.”

“Compare industrial anomaly detection papers across arXiv and IEEE automation venues, and show contradictions in evaluation setups.”

“Draft a survey on intelligent scheduling for researchers new to the subfield, but stop after the YAML outline for approval.”

“My topic is warehouse picking robotics. If that is outside scope, tell me the closest supported Industrial AI framing and proceed only with that.”

Boundaries

This v1 skill does not implement:

systematic review mode

meta-analysis

IRB-heavy or clinical ethics branches

standalone automation scripts

If the user needs those, state the boundary and continue with the closest supported research mode.