workspace-hub

Conventions for creating consistent Mermaid diagrams including decision node layout, edge ordering, and flowchart direction rules.

Generate interactive validation reports with quality scoring, missing data analysis, and type checking. Combines Pandas validation, Plotly visualization, and YAML configuration for comprehensive data quality reporting.

Class-level Hermes local configuration and setup workflows, including config audit gotchas and Windows installation.

Guide AI model selection based on task complexity, cost constraints, and latency requirements

Sub-skill of modular-architecture-documentation: 1. Module Definition Framework (+9).

Sub-skill of modular-architecture-documentation: Overview (+6).

Sub-skill of repo-structure: Tier Classification (Determines Which Rules Apply).

Automate invoice generation for engineering consulting services using YAML configuration and Word document templates.

Sub-skill of complexity-scorer: 3. Context-Aware Scoring (+1).

Score task complexity using keyword matching, heuristic analysis, and configurable threshold rules

Batch translate engineering Excel calculation files from Spanish to English preserving formulas

Convert OrcaFlex .dat binary model files to enriched YAML fixtures using worldenergydata public databases (vessel fleet, riser components, pipe schedules). All stages run on licensed-win-1. Output committed to digitalmodel as test fixtures.

Convert engineering Excel workbooks to Python code using Codex Desktop cowork on Windows. Benchmarked superior output with 24 vs 7 functions and 81 vs 53 tests for Ballymore jumper.

Convert engineering Excel workbooks to Python code using Codex Desktop cowork on Windows. Proven superior quality vs Linux openpyxl extraction (24 vs 7 functions, 81 vs 53 tests). Validated on Ballymore jumper installation analysis.

Convert Excel calculation spreadsheets to Python code — extract formulas, build dependency graphs, generate pytest tests using cell values as assertions, and produce dark-intelligence archive YAMLs.

DEPRECATED — superseded by gmail-extract-and-act. Extract emails from Gmail to /mnt/ace/<repo>/, archive to repos, commit, then delete. Uses archive-everything model.

Periodic relationship maintenance via email — identify contacts due for outreach, draft personalized check-ins, queue for user approval. Supports per-account tone and cadence.

Normalize, classify, and manage contact databases across 3 Gmail accounts. Clean CSV exports, deduplicate, tag categories, flag touchbase/unsubscribe candidates.

The email-as-queue workflow — extract structured data from emails, act on it, track thread state, and delete emails when topics complete. Email is transient; extracted data is persistent.

Class-level Gmail and email operations: multi-account setup, OAuth, triage, extraction, archiving, attachments, unsubscribe, and touchbase workflows.

Daily multi-account Gmail inbox triage — scan unread, classify by urgency, cross-reference contacts, generate actionable digest. Supports ace/personal/skestates accounts.

Layer 3 domain sub-skill for extracting naval architecture data from SNAME PNA, IMO stability codes, IACS structural rules, and classification society guidelines. Provides detection heuristics for stability constants, resistance equations, hull form coefficients, hydrostatic curves, IMO stability criteria, and structural scantling tables. type: reference

Classify and extract structured content from engineering documents using a 3-layer taxonomy: generic content types, engineering patterns, and domain sub-skills. Use when ingesting standards, reports, or technical manuals into structured data for downstream analysis. type: reference

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

Sanitize OrcaFlex models by stripping client-identifiable references, converting binary .dat to YAML .yml, and organizing into the reference model library.

OrcaFlex marine dynamic analysis — modeling, analysis, post-processing, and validation

Unit-safe engineering calculations with provenance tracking, dimensional consistency verification, and unit conversion across SI, inch, and metric systems.

Class-level GitHub issue lifecycle operations: issue creation, planning/execution routing, labels, evidence fields, roadmap anchors, closeout races, and visual planning.

Canonical names, abbreviations, and relationship vocabulary for the workspace-hub ecosystem. Load this when naming repos, modules, machines, files, or expanding acronyms to ensure consistency across humans and agents. type: reference

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.

Review the workspace-hub LLM-wiki/document-intelligence ecosystem, identify high-leverage gaps, and create grounded GitHub feature issues without duplicating existing work.

Execute a multi-issue architecture/planning wave in a plan-gated repo, then safely transition approved issues into implementation with file-based Codex prompts, local approval markers, subprocess monitoring, and cleanup handling for sandbox/hook edge cases.

Class-level workspace knowledge, LLM-wiki, repo mission contracts, stale doc references, semantic taxonomy, and knowledge-source reconnaissance.

Score task complexity using keyword matching and heuristics

Guide AI model selection based on task complexity, cost constraints, and latency requirements

Orchestrator checklist to verify worker outputs against the approved plan before accepting artifacts. Phase 2 of orchestrator/worker context enforcement (#2020).

Operator checklist for instantiating a new per-client private llm-wiki repo under workspace-hub [#2746](https://github.com/vamseeachanta/workspace-hub/issues/2746) + [#2731](https://github.com/vamseeachanta/workspace-hub/issues/2731) D4 (amended) naming convention `llm-wiki-<client>`.

Compact live-execution checklist companion for the canonical gh-work-planning route. Use for fast operational tracking during issue planning without replacing the full route.

Mandatory planning workflow for ALL GitHub issues — plan, review, approve, then implement.

Plan and operate a Hermes-led control plane that routes AI provider work across workstations using quota urgency, machine readiness, GitHub issue gates, and a dispatch ledger.