/aiwg-language-map

name: aiwg-language-map
namespace: aiwg
platforms: [all]
kernel: true
description: AIWG addons + extensions language map — categories, curated discover phrases, and per-bundle pointers covering everything beyond the framework quickrefs

AIWG Language Map — Addons + Extensions

This is your always-loaded directory for the AIWG **addon and extension** surface. It's the orientation layer for the ~214 skills that live outside the 8 frameworks. Frameworks have their own per-framework quickrefs (`sdlc-quickref`, `forensics-quickref`, etc.); this map covers everything else: addons (utilities, loops, voice, testing, etc.) and ops extensions (sys/net/sec/dev/it/stream).

How to use this map

1. Identify which **capability domain** below the user's need belongs to 2. Pick a **curated phrase** from that domain 3. Run `aiwg discover "<phrase>"` and surface the top match (or top-3) to the user 4. Fetch the body with `aiwg show skill <name>` — never `find` / `ls` / `Read` on storage paths

If a phrase doesn't fit the user's exact need, paraphrase. `aiwg discover` is forgiving with natural language.

**The discover→show pattern is mandatory.** See `aiwg-utils-quickref` for the canonical pipeline and `skill-discovery` HIGH rule for enforcement.

Map layout

The map has two sections:

• **Addon capability domains** — user-need clusters (memory, loops, voice, etc.) routed to addons
• **Extension domains** — operational scopes (sys, net, sec, etc.) for `ops-complete`

Each section opens with an explicit `aiwg discover "<phrase>"` example. Table rows underneath show **bare phrases** — pass them straight to `aiwg discover` (the verb is implied by the section header). Phrases have been verified against the index; each surfaces the listed bundle in the top results.

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Addon capability domains

Loops & iteration

When the user needs an iterative coding loop, recursive context decomposition, eval gates, or guided autonomous implementation.

Example: `aiwg discover "ralph loop"`. Phrases below pass straight to `aiwg discover`.

| Need | Phrase | Bundle | |---|---|---| | Iterative AI coding loop (Ralph) | `ralph loop` | agent-loop | | Loop status / abort / resume / attach | `ralph attach` (or `ralph abort` / `ralph resume` / `ralph status`) | agent-loop | | External crash-resilient loop variant | `ralph external loop` | agent-loop | | Recursive decomposition of a huge corpus | `rlm query` | rlm | | RLM batch processing with parallel sub-agents | `rlm batch fan-out` | rlm | | Bounded autonomous issue-to-code | `guided implementation` | guided-implementation | | Inner generator/critic eval loop in a pipeline | `eval loop` | nlp-prod | | KAMI-based agent quality eval framework | `agent quality eval` | aiwg-evals |

Memory & state

When the user needs persistent agent memory, semantic ingestion, or context curation.

Example: `aiwg discover "memory ingest"`. Phrases below pass straight to `aiwg discover`.

| Need | Phrase | Bundle | |---|---|---| | Semantic memory ingest | `memory ingest` | semantic-memory | | Lint memory entries | `semantic memory lint` | semantic-memory | | Capture / query memory | `memory query` | semantic-memory | | Filter context to remove distractors | `filter distractors from context` | context-curator |

> `llm-wiki` has a first-class discoverable driver for profile selection and > activation; its topology composes with the knowledge-base framework and > semantic-memory drivers. `auto-memory` remains a template bundle pending its > own operational driver.

Voice & writing quality

When the user is producing or reviewing prose and needs voice consistency, AI-pattern detection, or related text quality work.

Example: `aiwg discover "apply voice profile"`. Phrases below pass straight to `aiwg discover`.

| Need | Phrase | Bundle | |---|---|---| | Apply a voice profile to existing content | `apply voice profile` | voice-framework | | Create a new voice profile | `create voice profile` | voice-framework | | Blend two voices | `blend voice profiles` | voice-framework | | Analyze content's current voice | `analyze voice` | voice-framework | | Detect AI-pattern artifacts in writing | `ai pattern detection` | writing-quality | | Improve output diversity (verbalized sampling) | `verbalized sampling` | verbalized-sampling |

NLP & inference pipelines

When the user is building or optimizing LLM inference systems.

Example: `aiwg discover "pipeline architect"`. Phrases below pass straight to `aiwg discover`.

| Need | Phrase | Bundle | |---|---|---| | LLM inference pipeline architecture | `pipeline architect` | nlp-prod | | Pipeline pattern selection | `pipeline pattern` | nlp-prod | | Cost optimizer for LLM workloads | `llm cost optimizer` | nlp-prod | | Eval-loop generator/critic | `eval loop` | nlp-prod | | Productionize an inference pipeline | `pipeline productionize` | nlp-prod | | Prompt engineering | `prompt engineer` | nlp-prod |

Documentation intelligence

When the user is scraping, extracting, or analyzing documentation as a corpus.

Example: `aiwg discover "doc analyst"`. Phrases below pass straight to `aiwg discover`.

| Need | Phrase | Bundle | |---|---|---| | Coordinate doc analysis (master orchestrator) | `doc analyst` | doc-intelligence | | Scrape documentation sites | `doc scraper` | doc-intelligence | | Extract from PDFs | `pdf extractor` | doc-intelligence | | Run an OpenProse program | `prose run` | prose-integration | | Validate / parse a Prose program | `prose validate` | prose-integration |

Testing & quality

When the user needs test enforcement, UAT, or quality automation.

Example: `aiwg discover "tdd enforce"`. Phrases below pass straight to `aiwg discover`.

| Need | Phrase | Bundle | |---|---|---| | TDD enforcement / test gates | `tdd enforce` | testing-quality | | Mutation testing analyst | `mutation analyst` | sdlc-complete (agent) | | Mutation test runner | `mutation test` | testing-quality | | UAT plan generation via MCP | `ua