field-dev-code-recon

Extract field development information from external sources (LinkedIn posts, technical content), map against digitalmodel codebase coverage, document gaps, and create actionable GitHub issues.

5 stars

Best use case

field-dev-code-recon is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Extract field development information from external sources (LinkedIn posts, technical content), map against digitalmodel codebase coverage, document gaps, and create actionable GitHub issues.

Teams using field-dev-code-recon 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/field-dev-code-recon/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/field-dev-code-recon/SKILL.md"

Manual Installation

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

How field-dev-code-recon Compares

Feature / Agentfield-dev-code-reconStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Extract field development information from external sources (LinkedIn posts, technical content), map against digitalmodel codebase coverage, document gaps, and create actionable GitHub issues.

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.

Related Guides

SKILL.md Source

# Field Development Code Reconnaissance

## Trigger

User asks to review field development content from external sources (LinkedIn posts, articles, documentation) and extract actionable intelligence with codebase mapping.

## Workflow

### Phase 1: Content Extraction

1. Use browser_navigate to load the source URL
2. Dismiss sign-in dialogs (click @e1 button if present)
3. Use browser_snapshot with full=true to capture complete content
4. Extract: component names, standards references, operational workflows, hazards, mitigations
5. Close browser when done

### Phase 2: Code Coverage Mapping

Map extracted components against digitalmodel using STRONG/PARTIAL/NO coverage tiers:

- **STRONG**: Native Python module with tests covering the functionality
- **PARTIAL**: OrcaFlex skill only, incomplete module, or partial standards coverage
- **NO COVERAGE (GAP)**: No code exists at all

Use this terminal command to survey existing modules:
```bash
cd /mnt/local-analysis/workspace-hub/digitalmodel
find src/digitalmodel -name '*.py' -not -path '*/__pycache__/*' | sort
```

For standards/skills coverage:
```bash
skills_list  # in Python/hermes context
```

Mapping template:
| Component | Standard/Ref | Coverage | digitalmodel Module |
|-----------|-------------|----------|-------------------|
| ...       | API 17D      | STRONG   | subsea/trees/     |
| ...       | DNV-RP-F101 | PARTIAL  | (skill only)      |
| ...       | --          | GAP      | --                |

### Phase 3: Documentation

Create `docs/field-development/<topic-name>-mapping.md` with:
- Source descriptions and extraction date
- Component inventory with standards references
- Operational workflow diagrams (ASCII)
- Full coverage map (STRONG/PARTIAL/GAP tables)
- API/standards quick reference table
- Architecture diagram (ASCII) showing component relationships

### Phase 4: Issue Creation

Create one GitHub issue per GAP or significant PARTIAL component:

```bash
cd /mnt/local-analysis/workspace-hub/digitalmodel
gh issue create \
  --title "Implement <component name> (<standard ref>)" \
  --label "cat:engineering,enhancement,priority:medium" \
  --body "<structured body>"
```

Issue body must include:
- Background/context linking to the mapping doc
- Problem statement
- Scope (module path, key capabilities)
- Acceptance criteria (checklist of files to create)
- Standards references
- Related issues

### Phase 5: Commit & Push

```bash
git add docs/field-development/
git commit -m "docs: <topic> coverage mapping (#issue-numbers)"
git push
```

## Pitfalls

- `search_files` with very broad paths (entire workspace) hits output limits — prefer targeted `terminal` find commands in digitalmodel/src/
- LinkedIn content is gated behind sign-in — dismiss the dialog, then snapshot
- Don NOT create issues for STRONG coverage areas
- Prioritize medium over low for core subsea components, low for operational/installation topics
- All issues should reference the mapping doc at top of body
- Commit documentation BEFORE creating issues (or reference pending issue numbers)

## Standards Quick Reference

API 17 subsea: 17A=General, 17B=Flexible pipe, 17D=Trees, 17E=Umbilicals, 17F=Controls, 17G=Workover riser, 17H=ROV interface, 17P=Manifolds/Structures, 17R=Connectors/Jumpers, 17W=Capping stack

Key DNV standards: DNV-OS-F101 (pipe), DNV-RP-F105 (free span), DNV-RP-F109 (on-bottom), DNV-RP-B401 (CP), DNV-RP-H103 (hydro), DNV-ST-N001 (marine ops)

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