engineering-domain-reconnaissance

Class-level external engineering domain reconnaissance: field development, external drive ingest planning, and source-to-artifact conversion.

5 stars

Best use case

engineering-domain-reconnaissance is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Class-level external engineering domain reconnaissance: field development, external drive ingest planning, and source-to-artifact conversion.

Teams using engineering-domain-reconnaissance should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/engineering-domain-reconnaissance/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/engineering/engineering-domain-reconnaissance/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/engineering-domain-reconnaissance/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How engineering-domain-reconnaissance Compares

Feature / Agentengineering-domain-reconnaissanceStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Class-level external engineering domain reconnaissance: field development, external drive ingest planning, and source-to-artifact conversion.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Engineering Domain Reconnaissance

## When to Use
Use when surveying external engineering data/code/document sources, planning safe ingests, or turning field/domain reconnaissance into structured repository artifacts.

## Class-Level Workflow
1. Inventory source systems and sensitivity before copying or indexing anything.
2. Keep metadata-only sweeps separate from content extraction when confidentiality is uncertain.
3. Convert domain findings into durable repo-aligned artifacts with provenance.
4. Defer destructive moves until storage layout, naming, and rollback are clear.

## Needs-Data Unblock Review Pattern
Use this when an engineering/data-pipeline GitHub issue is already `status:plan-approved` but still has `status:needs-data`, or when the user clarifies where source data should come from.

1. Reconcile the parent issue and its explicit unblocker issue(s) before executing the parent. Verify live GitHub labels, local approval marker, latest comments, and prerequisite issues.
2. Convert the user's clarification into durable GitHub comments on both the parent and unblocker issue so future workers do not re-ask the same source/data question.
3. For local standards/code corpora such as `/mnt/ace/mkt-a-codes`, treat the mounted corpus as source-of-record and keep raw PDFs/files out of git/wiki. Produce metadata-first or curated summary/wiki artifacts with provenance back-links to the source path.
4. For online engineering dataset backfills, state the minimum unblock schema explicitly: required fields, source URLs, confidence, and conflict handling. Keep the parent `status:needs-data` until validation proves the threshold is met.
5. Separate source-readiness from implementation readiness: do not remove `status:needs-data` or start parent implementation until the unblocker artifact exists and a small validation check passes.
6. If prerequisites split scope (for example standards routing vs source content), keep completed blockers documented but focus execution on the remaining live blocker.

## Consolidated Session Learnings

Narrow skills absorbed during the 2026-04-29 umbrella consolidation are preserved under `references/`.

## Absorbed Narrow Skills (2026-04-29)

### `field-dev-code-recon`

- Former skill demoted to `references/field-dev-code-recon.md`.
- Preserved insight: Extract field development information from external sources (LinkedIn posts, technical content), map against digitalmodel codebase coverage, document gaps, and create actionable GitHub issues.

### `external-drive-ingest-planning`

- Former skill demoted to `references/external-drive-ingest-planning.md`.
- Preserved insight: Plan safe external-drive ingests into repo-aligned storage such as /mnt/ace: read-only mounts, manifests, staged rsync, dedupe-merge gates, GitHub issue traceability, and governance/execution split.

Related Skills

domain-knowledge-sweep

5
from vamseeachanta/workspace-hub

Systematic multi-source research of an engineering domain. Spawns parent issue → 6 research subissues (Standards, Academic, Industry, LinkedIn-marketing, Code-audit, Synthesis) → gap implementation subissues. Replaces LinkedIn-only extraction with defensible comprehensive sourcing.

engineering-solver-domain-recon

5
from vamseeachanta/workspace-hub

Deep reconnaissance of an engineering solver domain (OrcaWave, OrcaFlex, CalculiX, OpenFOAM, etc.) across a multi-repo ecosystem — map infrastructure, issues, skills, data artifacts, machine constraints, and solver queue state before planning work.

domain-gap-to-issue-roadmap

5
from vamseeachanta/workspace-hub

Deep multi-repo ecosystem audit → domain gap matrix → structured GitHub issue roadmap with epics. Use when the user wants to assess capabilities across repos and create a backlog of work items covering code, data, and documentation gaps.

cad-engineering

5
from vamseeachanta/workspace-hub

Expert CAD Engineering Specialist with comprehensive knowledge of CAD systems, file formats, and conversion technologies. Use for CAD software guidance, file format conversions, technical drawings, 3D modeling, PDF to CAD conversions, and interoperability between open-source and proprietary CAD systems.

engineering-report-generator

5
from vamseeachanta/workspace-hub

Generate engineering analysis reports with interactive Plotly visualizations, standard report sections, and HTML export. Use for creating dashboards, analysis summaries, and technical documentation with charts.

engineering-issue-workflow

5
from vamseeachanta/workspace-hub

Mandatory workflow for engineering-critical GitHub issues — resource intelligence, plan review, TDD, implementation, and 3-provider cross-review.

engineering-chatbot-demo

5
from vamseeachanta/workspace-hub

GTM demo execution for engineering AI chatbot presentations — system prompt authoring, demo scripting, ROI capture

oil-and-gas-reservoir-engineering

5
from vamseeachanta/workspace-hub

Sub-skill of oil-and-gas: Reservoir Engineering (+5).

ship-dynamics-6dof-5-time-domain-simulation

5
from vamseeachanta/workspace-hub

Sub-skill of ship-dynamics-6dof: 5. Time-Domain Simulation.

orcawave-multi-body-export-for-time-domain

5
from vamseeachanta/workspace-hub

Sub-skill of orcawave-multi-body: Export for Time-Domain.

orcaflex-modeling-domain-expertise

5
from vamseeachanta/workspace-hub

Sub-skill of orcaflex-modeling: Domain Expertise (+3).

marine-offshore-engineering-typical-project-phases

5
from vamseeachanta/workspace-hub

Sub-skill of marine-offshore-engineering: Typical Project Phases:.