/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

Dataset intelligence, indexing & provenance

When a user points AIWG at files, an API, a corpus, or another dataset and asks for search, indexing, traceability, provenance, lineage, synchronization, or safe retirement—even when they do not know those terms.

Example: `aiwg discover "make this searchable"`. Phrases below pass straight to `aiwg discover`.

| Need | Phrase | Bundle | |---|---|---| | Start from only a source and outcome | `use this data` | dataset-intelligence | | Recommend custom indexing safely | `make this searchable` | dataset-intelligence | | Add evidence-bearing traceability | `trace this dataset` | dataset-intelligence | | Explain record or field lineage | `where did this record come from` | dataset-intelligence | | Verify freshness and provenance | `verify this dataset` | dataset-intelligence | | Resume an incremental source | `sync this source` | dataset-intelligence | | Retire data and derived artifacts safely | `retire this dataset` | dataset-intelligence |

The same governed intake and handoff envelopes serve SDLC, research, knowledge-base, media, marketing, ops, and project-local bundles. All execution delegates to `aiwg dataset`; the addon does not create a shadow runtime.

Schemas & data contracts

When the user describes a record, configuration file, event, API payload, message, import/export, or other structured data—even when they do not know to ask for a schema.

Example: `aiwg discover "define data shape"`. Phrases below pass straight to `aiwg discover`.

| Need | Phrase | Bundle | |---|---|---| | Decide whether structured data needs a contract | `define data shape` | schema-governance | | Explain schemas and continue examples-first | `why do I need a schema` | schema-governance | | Author and register a canonical schema | `create a schema` | schema-governance | | Evolve an existing contract safely | `change an existing schema` | schema-governance | | Review correctness and readiness | `review a data contract` | schema-governance |

The SDLC discovery track invokes this addon automatically for persistent or exchanged data. Users should describe the data need; they need not choose a dialect, identifier, lifecycle, or compatibility policy themselves.

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 |

Session history and exploration

Use `aiwg discover "spelunk session data"` to find catalog exploration. Useful phrases include `search past conversations`, `trace session tool calls`, `compare provider sessions`, and `harvest session decisions`. `session-explore` handles search, timelines, analytics, citations and coverage; `session-harvest` handles candidate review and promotion. `session-analyst` and the `session-investigation` flow compose bounded evidence collection and synthesis. The singular `session` launcher and cost-history telemetry serve different needs.

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 | |