Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jmagly/aiwg --skill aiwg-language-mapgit clone --depth 1 https://github.com/jmagly/aiwgWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jmagly/aiwg/aiwg-language-map)<a href="https://agentmods.dev/skills/jmagly/aiwg/aiwg-language-map"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/aiwg-language-map/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jmagly/aiwg/aiwg-language-map"><img src="https://agentmods.dev/badge/skills/jmagly/aiwg/aiwg-language-map.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.04460 |
| Opus 5 | $0.00015 | $0.02230 |
| Sonnet 5 | $0.00006 | $0.00892 |
| Haiku 4.5 | $0.00003 | $0.00446 |
Grade A, and why
aiwg-language-map scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 403 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Identify which capability domain below the user's need belongs to
- Pick a curated phrase from that domain
- Run
aiwg discover "<phrase>"and surface the top match (or top-3) to the user - Fetch the body with
aiwg show skill <name>— neverfind/ls/Readon 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.
| 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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday Changed · +10 lines c91bdd611faf
- 5d ago Changed · +44 lines 7df9eeaa3014
- 6d ago First seen · 349 lines · 31 tokens per session scan A c0ecea628972
aiwg-language-map is a skill published in the GitHub repository jmagly/aiwg (210 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 4,460 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
surge
Use when a user provides a PRD, spec, or detailed requirements document and needs a full project delivered through iterative expert orchestration — multi-round analyze/research/design/implement/QA cycles with convergence detection. NOT for: single-file edits, quick prototypes, simple Q&A, or tasks without a written…
goal-writer
Drafts a goal+rider document pair that briefs an autonomous coding agent on one round of work — a goal file under 4,000 characters (sized to fit the /goal command in both Claude Code and Codex) plus an unbounded rider with phased plans and named depth tests. Use when the user says "draft a goal", "write a goal+rider"…
horizon
Run a durable Horizon workflow for a multi-feature goal with bounded autonomous retries and an audit trail.
check-in
Record a Parallax protocol checkpoint with concrete evidence before gated implementation work.
debug
Perform an evidence-based Parallax diagnosis or post-build audit and verify the repair.
hyperplan
Harden a non-trivial plan through a three-round adversarial critique and evidence-based synthesis.