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 agentmods add skills/facets-cloud/praxis-cli/use-ignpx skills add Facets-cloud/praxis-cli --skill use-iggit clone --depth 1 https://github.com/Facets-cloud/praxis-cliWhat 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 | $0.00116 | $0.03754 |
| Opus 5 | $0.00058 | $0.01877 |
| Sonnet 5 | $0.00023 | $0.00751 |
| Haiku 4.5 | $0.00012 | $0.00375 |
Grade A, and why
use-ig 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 2d ago.
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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using ig to answer questions about a project
Praxis-MCP read route — queries run server-side via praxis mcp ig; no local
ig needed. (If praxis is unavailable, ig ships a native local-ig copy of
this skill instead. Only one is ever installed at a time.)
ig holds a prebuilt catalog for a project — a set of code and infra graphs
joined at their shared boundaries. When a question spans repos or services, or
asks "what connects to X / what breaks if I change X", query the catalog before
grepping source. Every read runs on the Praxis server under org-managed
credentials; your laptop needs no ig binary, no synced tree, no cloud secrets.
The division of labor: ig states facts (graphs, provenance, source file+line). YOU decide, act, and ask the user. The MCP tools are read-only — they never build, never prompt, never touch your working copy.
Boundary — the six praxis mcp ig tools are the whole read interface. Do
not reach for a local ig/graphify binary and do not try to praxis ig sync;
in this route reads execute server-side. (Building/refreshing a catalog is a
separate CI/setup concern that still uses ig on a builder host — not your job
here.)
First: list the catalogs
There is NO default catalog and every read tool needs BOTH a catalog and a
member. Start here:
praxis mcp ig ig_list_catalogs
It returns the org's catalogs as name + version, each with its members.
Pick the <catalog> your question is about and pass it as --arg catalog=<name>
to every other tool. A repo can be a member of MORE THAN ONE catalog (e.g.
control-plane in both capillary-cloud and saas-cp); because calls edges
only appear between members present in the SAME catalog, "who calls
control-plane" is catalog-relative — ask each catalog.
The member arg — pick your lens (REQUIRED)
A catalog is a graph of graphs, so a node is addressed per member. Every
read tool takes --arg member=<m> and it selects which graph you read:
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.
- 2d ago First seen · 234 lines · 116 tokens per session scan A aa3730a5dec6
use-ig is a skill published in the GitHub repository Facets-cloud/praxis-cli (2 stars, last pushed 2d ago), licensed MIT. It adds 116 tokens to every session and 3,754 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.