STUNSPOT Stunspot's Guide to Automotive Systems

How to Use This Canon

Stunspot’s Guide to Automotive Systems is built for model-facing use first and human reading second. Treat it as a structured knowledge substrate for automotive reasoning: definitions, causal models, system boundaries, field diagnostics, lifecycle economics, and execution artifacts.

Use the canon to help an AI system reason more like a careful automotive systems analyst: mechanism-first, evidence-aware, repair-aware, cost-aware, safety-aware, and explicit about uncertainty.

Directory Policy

This repository separates navigation from corpus material.

Layer Path Role
GitHub Pages navigation docs/ Landing page, canon map, usage guide, knowledge-pack guide, layout, CSS, and future brand assets.
Source reports knowledge-packs/by-report/ Canonical individual report files. Best for precise retrieval, selective upload, citation, and auditing.
Compiled packs knowledge-packs/compiled-packs/ Grouped upload files for AI projects and RAG workflows that prefer fewer files.
Omnibus knowledge-packs/omnibus/ Whole-corpus bundle for one-file import, archive, or robust long-context systems.

There is no docs/reports/ directory. The source corpus lives in knowledge-packs/by-report/.

Situation Use Why
You want the safest default for an AI project with limited file slots Compiled packs Fewer files, coherent groupings, strong report boundaries.
You need precise citations or selective retrieval Source reports Each report remains an independent unit with a clear title and topic boundary.
You need one-file import or offline search Omnibus Entire corpus in one Markdown file.
You are building a production RAG pipeline Source reports + manifest.json Preserves metadata, source-to-output mappings, and report-level retrieval granularity.
You are running a long-context exploratory session Omnibus or targeted source reports Omnibus gives breadth; targeted reports reduce distraction and middle-context decay.

This release includes compiled packs for A-D, E-J, and N-O. Reports K-M are available as individual source reports and are included in the omnibus, but they are not currently packaged as a separate compiled Vol. 3 bundle.

How to Instruct an AI Model

Use a directive that makes the canon operational rather than decorative.

Analyze the automotive question using Stunspot’s Guide to Automotive Systems as governing reference material. Treat the canon as a systems model, not background reading. Retrieve and apply its vocabulary, causal frames, failure modes, lifecycle economics, diagnostic discipline, and artifact logic. Distinguish symptoms from causes, claims from evidence, components from systems, and ownership lore from measurable duty-cycle reality. When recommending action, make the reasoning testable, repair-aware, cost-aware, safety-aware, and explicit about uncertainty.

For diagnostic tasks, add:

Do not treat a diagnostic trouble code as a parts-replacement instruction. Separate complaint, symptom, sign, measured abnormality, failure mode, failed component, causal mechanism, root cause, repair action, and repair verification. Prefer discriminating tests over pattern guessing.

For buying or ownership tasks, add:

Evaluate the vehicle as a lifecycle object: acquisition cost, depreciation, duty cycle, maintenance behavior, insurance exposure, repair ecosystem, parts availability, known failure modes, regulatory constraints, and resale implications.

For modification or restoration tasks, add:

Evaluate the build as an integrated system. Check whether power, braking, cooling, tires, chassis stiffness, drivetrain durability, legal compliance, budget reserve, serviceability, and intended duty cycle remain balanced.

Suggested Human Reading Paths

For vehicle purchase and ownership decisions

Start with:

  1. A. Automotive Systems Reality Model
  2. J. Automotive Market, Ownership, and Lifecycle Economics
  3. N. Automotive Failure Modes and Diagnostic Logic
  4. O. Automotive Execution Artifacts and Practitioner Workflows

For repair, troubleshooting, and service planning

Start with:

  1. H. Automotive Electrical, Electronic, and Software Systems
  2. E. Automotive Propulsion Systems
  3. F. Automotive Chassis and Vehicle Dynamics Systems
  4. N. Automotive Failure Modes and Diagnostic Logic
  5. O. Automotive Execution Artifacts and Practitioner Workflows

For vehicle design, architecture, and engineering trade-offs

Start with:

  1. B. Automotive Systems Physics
  2. C. Automotive Systems Architecture
  3. E. Automotive Propulsion Systems
  4. F. Automotive Chassis and Vehicle Dynamics Systems
  5. H. Automotive Electrical, Electronic, and Software Systems

For EVs, hybrids, energy transition, and environmental claims

Start with:

  1. A. Automotive Systems Reality Model
  2. E. Automotive Propulsion Systems
  3. H. Automotive Electrical, Electronic, and Software Systems
  4. L. Automotive Energy Transition and Environmental Systems
  5. K. Automotive Safety, Regulation, and Compliance Systems

For modification, motorsport, and restoration planning

Start with:

  1. B. Automotive Systems Physics
  2. F. Automotive Chassis and Vehicle Dynamics Systems
  3. M. Automotive Performance, Motorsport, and Modification Systems
  4. N. Automotive Failure Modes and Diagnostic Logic
  5. O. Automotive Execution Artifacts and Practitioner Workflows

Practical Guardrails