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Aiwg Language Map

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jmagly
aiwg-language-map

AIWG addons + extensions language map — categories, curated discover phrases, and per-bundle pointers covering everything beyond the framework quickrefs

Overview

Publisherjmagly
Repositoryaiwg
Skill nameaiwg-language-map
Stars
225
Forks
34
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jmagly on GitHub. Read the source before you install it.

Installation

Install the Aiwg Language Map AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/aiwg-language-map
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jmagly/aiwg.git /tmp/aiwg
mkdir -p .claude/skills
cp -r /tmp/aiwg/agentic/code/addons/aiwg-utils/skills/aiwg-language-map .claude/skills/aiwg-language-map
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aiwg Language Map in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aiwg Language Map on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aiwg Language Map is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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> --body — 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.


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.

NeedPhraseBundle
Start from only a source and outcomeuse this datadataset-intelligence
Recommend custom indexing safelymake this searchabledataset-intelligence
Add evidence-bearing traceabilitytrace this datasetdataset-intelligence
Explain record or field lineagewhere did this record come fromdataset-intelligence
Verify freshness and provenanceverify this datasetdataset-intelligence
Resume an incremental sourcesync this sourcedataset-intelligence
Retire data and derived artifacts safelyretire this datasetdataset-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.

NeedPhraseBundle
Decide whether structured data needs a contractdefine data shapeschema-governance
Explain schemas and continue examples-firstwhy do I need a schemaschema-governance
Author and register a canonical schemacreate a schemaschema-governance
Evolve an existing contract safelychange an existing schemaschema-governance
Review correctness and readinessreview a data contractschema-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.

NeedPhraseBundle
Iterative AI coding loop (Ralph)ralph loopagent-loop
Loop status / abort / resume / attachralph attach (or ralph abort / ralph resume / ralph status)agent-loop
External crash-resilient loop variantralph external loopagent-loop
Recursive decomposition of a huge corpusrlm queryrlm
RLM batch processing with parallel sub-agentsrlm batch fan-outrlm
Bounded autonomous issue-to-codeguided implementationguided-implementation
Inner generator/critic eval loop in a pipelineeval loopnlp-prod
KAMI-based agent quality eval frameworkagent quality evalaiwg-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.

NeedPhraseBundle
Semantic memory ingestmemory ingestsemantic-memory
Lint memory entriessemantic memory lintsemantic-memory
Capture / query memorymemory querysemantic-memory
Filter context to remove distractorsfilter distractors from contextcontext-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.

NeedPhraseBundle
Apply a voice profile to existing contentapply voice profilevoice-framework
Create a new voice profilecreate voice profilevoice-framework
Blend two voicesblend voice profilesvoice-framework
Analyze content's current voiceanalyze voicevoice-framework
Detect AI-pattern artifacts in writingai pattern detectionwriting-quality
Improve output diversity (verbalized sampling)verbalized samplingverbalized-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.

NeedPhraseBundle
LLM inference pipeline architecturepipeline architectnlp-prod
Pipeline pattern selectionpipeline patternnlp-prod
Cost optimizer for LLM workloadsllm cost optimizernlp-prod
Eval-loop generator/criticeval loopnlp-prod
Productionize an inference pipelinepipeline productionizenlp-prod
Prompt engineeringprompt engineernlp-prod
Agentic or bounded classificationagentic classificationdecision-engine (opt-in)
Jev decision engineJev decision enginedecision-engine (opt-in)
Reduce frontier tokens with bounded decisionsclassify to reduce frontier tokensdecision-engine (opt-in)
Offload a yes/no, pick-one, or 1-N decision to Jevoffload decisions to jevdecision-engine (opt-in)
Set up Jev with a tokenset up jevdecision-engine (opt-in)
Questions sharing authorized stateshared-state batchingdecision-engine (opt-in)
Offline decision fixtures and readiness plansdecision playgrounddecision-engine (opt-in)

decision-engine requires explicit installation: aiwg use decision-engine. It is excluded from bulk installs. Discover decision-offload for single-question offload via aiwg decision ask (enabled by aiwg decision setup jev); discover decision-evaluate for pinned classification requests; evaluation requires AIWG_DECISION_ENABLED=1 plus an explicit backend binding and approved projection for live egress. Library batching also needs host policies; discovering the skill does not enable it. Discover decision-playground to list and run offline recorded fixtures without credentials or inference. Its live-plan command only reports readiness; live evaluation requires the separate explicit opt-in and host configuration. These phrases use deterministic discovery and require no model inference.

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.

NeedPhraseBundle
Coordinate doc analysis (master orchestrator)doc analystdoc-intelligence
Scrape documentation sitesdoc scraperdoc-intelligence
Extract from PDFspdf extractordoc-intelligence
Run an OpenProse programprose runprose-integration
Validate / parse a Prose programprose validateprose-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.

NeedPhraseBundle
TDD enforcement / test gatestdd enforcetesting-quality
Mutation testing analystmutation analystsdlc-complete (agent)
Mutation test runnermutation testtesting-quality
UAT plan generation via MCPuat planuat-mcp
UAT execution via MCPuat executeuat-mcp

Skill / extension authoring

When the user is building new AIWG content (skills, addons, framework changes).

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

NeedPhraseBundle
End-to-end skill creation orchestratorskill architectskill-factory
Build a skillskill builderskill-factory
Enhance / validate / package a skillskill packagerskill-factory
Validate an AIWG addonvalidate addonaiwg-dev
Validate a framework componentvalidate componentaiwg-dev
AIWG devkit build helpersdevkit buildaiwg-dev
Link-check across docslink checkaiwg-dev

Color & UX

When the user needs color theory or palette tooling.

Example: aiwg discover "color palette". Phrases below pass straight to aiwg discover.

NeedPhraseBundle
Generate or review a color palettecolor palettecolor-palette
Color theory fundamentals referencecolor theorycolor-palette
Accessibility / contrast reviewcolor accessibilitycolor-palette

Daemon & background work

When the user needs persistent sessions or background orchestration.

Example: aiwg discover "daemon init". Phrases below pass straight to aiwg discover.

NeedPhraseBundle
Concierge daemon entry / initdaemon initaiwg-utils
Concierge agent (front-of-house)concierge daemondaemon
Mission Control (background mission orchestrator)mc dispatchagent-loop
Star-prompt for repo recommendationsstar promptstar-prompt

Setup & installer

When the user is bootstrapping a project or installing AIWG itself.

Example: aiwg discover "setup manifest generate". Phrases below pass straight to aiwg discover.

NeedPhraseBundle
Generate a reproducible installer manifestsetup manifest generateagentic-installer
Validate / run a setup manifestsetup manifest validate runagentic-installer

Hooks & integration

When the user needs platform-level lifecycle hooks, HITL gates, or external bridges.

Example: aiwg discover "hitl patterns". Phrases below pass straight to aiwg discover.

NeedPhraseBundle
HITL gate patterns (rule)hitl patternssdlc-complete (rule)
HITL gates (rule, ensemble review)hitl gatessdlc-complete (rule)
Factory AI compat agentfactory compatsdlc-complete (agent)

aiwg-hooks, agent-persistence, and droid-bridge now expose discoverable activation drivers. Their low-level hooks, HITL gates, and MCP server remain implementation artifacts reached through those drivers.

Core meta-utilities

For everything else AIWG-internal — context regeneration, workspace management, index/query, validation, and project-status surfaces — see aiwg-utils-quickref. It's already loaded; this map points back at it.


Extension domains (ops-complete)

Operational extensions live under agentic/code/extensions/<domain>/ and extend ops-complete. They carry both skills AND rules. The framework-level ops-quickref lists the high-level extension model; this section gives you direct phrases.

Each block opens with one explicit aiwg discover example; remaining phrases are bare — pass straight to aiwg discover.

sys — per-host operations

Hardware, OS, boot chains, fleet host documentation, immutable bases, hardware safety procedures.

aiwg discover "sys host profile"   # → sys-host-profiler agent (1.00)
sys host audit                     # → sys-host-audit skill
luks tpm dual phase                # → sec-luks-dual-phase rule (sys+sec border)
sys host independence              # → sys-host-independence rule

net — network operations

VLANs, DNS, firewalls, Cloudflare tunnels, UniFi config, network state authority, cert expiry gates, tunnel safety.

aiwg discover "vlan audit"   # → net-vlan-audit (1.00)
net dns check                # → net-dns-check skill
net change blast radius      # → net-change-blast-radius rule
tunnel safety                # → net-tunnel-safety rule
net state authority          # → net-state-authority rule

sec — security operations

PKI, LUKS, TPM2, YubiKey, SSH CA, access auditing, cert expiry gates, key material handling.

aiwg discover "sec cert scan"   # → sec-cert-scan
sec access snapshot             # → sec-access-snapshot
sec access audit frequency      # → sec-access-audit-frequency rule
sec cert expiry gate            # → sec-cert-expiry-gates rule
key material handling           # → sec-key-material-handling rule
sec luks dual phase             # → sec-luks-dual-phase rule

dev — CI/CD and build automation

Pipeline safety, idempotent builds, secret hygiene, self-contained CI builders.

aiwg discover "pipeline safety ci"   # → dev-pipeline-safety rule
idempotent build                     # → dev-idempotent-builds rule
ci secret hygiene                    # → dev-secret-hygiene rule
self contained ci builder            # → dev-ci-self-contained rule

it — IT ops, CMDB, asset management

Asset registry, service deployments, disaster recovery, change control, asset authority, DR validation.

aiwg discover "it asset sync"   # → it-asset-sync skill (0.81)
it asset authority              # → it-asset-authority rule
it service health               # → it-service-health rule
it dr validation                # → it-dr-validation rule
it change control               # → it-change-control rule

stream — streaming media operations

Transcoders, restreaming, platform integrations, stream pipeline gates.

aiwg discover "stream deploy"   # → stream-deploy skill
stream pipeline gates           # → stream-pipeline-gates rule
stream safety                   # → stream-safety rule

api-adapter — external API integrations

Reserved for external API adapter scaffolding. Currently a placeholder bundle with no skills or rules; phrases will appear as adapters are added.


Categories at a glance (cheat sheet)

When you don't know which domain a need falls into:

If the user mentions...Look in...
"loop", "iterate", "ralph", "recursive", "decompose huge file"Loops & iteration
"remember", "memory", "context", "wiki", "ingest"Memory & state
"voice", "writing style", "AI pattern", "diversity", "samples"Voice & writing quality
"inference", "pipeline cost", "model selection"NLP & inference pipelines
"documentation scrape", "extract from PDF", "Prose program"Documentation intelligence
"test enforcement", "TDD", "UAT", "MCP test"Testing & quality
"build a skill", "scaffold addon", "framework dev"Skill / extension authoring
"color", "palette", "contrast", "WCAG"Color & UX
"daemon", "background mission", "mission control"Daemon & background work
"installer", "setup manifest", "bootstrap"Setup & installer
"hooks", "permissions", "session", "droid", "HITL"Hooks & integration
"host", "BIOS", "boot chain", "OS install"Extensions / sys
"VLAN", "DNS", "firewall", "tunnel"Extensions / net
"cert", "LUKS", "YubiKey", "PKI", "access audit"Extensions / sec
"CI", "pipeline", "build", "secret in workflow"Extensions / dev
"CMDB", "asset", "DR", "service deploy", "change control"Extensions / it
"transcoder", "restream", "live broadcast"Extensions / stream

Project-local extensions (pilot → promote)

Addons, extensions, and frameworks don't all have to come from upstream. Users can pilot content bundles per-project under .aiwg/{extensions,addons,frameworks}/<name>/, validate them, then promote to upstream when mature. Use .aiwg/plugins/<name>/ only when wrapping a bundle for marketplace delivery. Curated phrases:

bash
aiwg new-bundle <name>                         # scaffold; auto-builds project graph
aiwg new-extension <name> --starter rule       # extension with starter rule
aiwg new-addon <name>                          # addon
aiwg new-framework <name>                      # framework
aiwg new-plugin <name>                         # marketplace delivery wrapper
aiwg new-bundle <name> --dry-run               # preview without writing
aiwg list --project-local                      # inventory + validation status
aiwg doctor --project-local                    # health check (counts, drift, shadows)
aiwg use <name>                                # deploy single project-local bundle
aiwg promote <name>                            # graduate to upstream (hash-verified)
aiwg promote <name> --to corpus <path>         # promote to private corpus instead
aiwg promote <name> --dry-run                  # preview promotion

After scaffolding, the bundle is immediately discoverable via aiwg discover "<name>" — the project graph is rebuilt automatically. No manual aiwg index build --graph project step needed.

When this map doesn't have a phrase that fits

Don't enumerate from memory. Run aiwg discover with the user's natural-language phrasing — the index is forgiving. If you get zero results, try:

  1. A broader phrase (e.g., drop a noun)
  2. A different vocabulary (e.g., "deploy" vs "publish" vs "release")
  3. aiwg index stats --graph framework to confirm the index is built and populated
  4. Check whether the bundle is even installed via aiwg list
  5. If the workspace has project-local bundles, also check aiwg index stats --graph project

If after that the capability genuinely doesn't exist, you can tell the user honestly — but only after the search ran and came up empty. The skill-discovery HIGH rule mandates the search before declining.

See also

  • aiwg-utils-quickref — core meta-utility surface and the canonical discover→show pipeline
  • Per-framework quickrefs: sdlc-quickref, forensics-quickref, research-quickref, media-curator-quickref, marketing-quickref, ops-quickref, security-engineering-quickref, knowledge-base-quickref
  • skill-discovery HIGH rule — discovery is mandatory before declining or improvising

Frequently asked questions

What does the Aiwg Language Map AI skill do?

AIWG addons + extensions language map — categories, curated discover phrases, and per-bundle pointers covering everything beyond the framework quickrefs

Why use Aiwg Language Map on TypingMind?

Because you install it once and use it with any model. Aiwg Language Map is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aiwg Language Map in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jmagly/aiwg/tree/main/agentic/code/addons/aiwg-utils/skills/aiwg-language-map. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aiwg Language Map?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aiwg Language Map?

As many as you like. As long as a model supports skills, you can use Aiwg Language Map with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aiwg Language Map AI skill free?

Yes. It is published on GitHub by jmagly under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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