engineering-domain-reconnaissance
Class-level external engineering domain reconnaissance: field development, external drive ingest planning, and source-to-artifact conversion.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/engineering-domain-reconnaissance/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How engineering-domain-reconnaissance Compares
| Feature / Agent | engineering-domain-reconnaissance | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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.
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