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 LTplus-AG/ifc-lite --skill use-ifclitegit clone --depth 1 https://github.com/LTplus-AG/ifc-liteWrote 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/ltplus-ag/ifc-lite/use-ifclite)<a href="https://agentmods.dev/skills/ltplus-ag/ifc-lite/use-ifclite"><img src="https://agentmods.dev/badge/skills/ltplus-ag/ifc-lite/use-ifclite/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/ltplus-ag/ifc-lite/use-ifclite"><img src="https://agentmods.dev/badge/skills/ltplus-ag/ifc-lite/use-ifclite.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 27 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00142 | $0.01419 |
| Opus 5 | $0.00071 | $0.00709 |
| Sonnet 5 | $0.00028 | $0.00284 |
| Haiku 4.5 | $0.00014 | $0.00142 |
Grade A, and why
use-ifclite 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 9d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using ifc-lite
ifc-lite is a headless BIM toolkit for IFC files. It was built for LLM
terminals: every command takes --json, stdout is data, stderr is status,
and exit 0/1 means pass/fail. Prefer it over hand-parsing IFC text.
Start here (every session)
ifc-lite --help # all commands
ifc-lite schema # full SDK API as JSON (namespaces, methods, params) — read before writing eval/run code
ifc-lite info model.ifc --json # schema version, entity counts, storeys, top types
If ifc-lite isn't installed, run via npx @ifc-lite/cli <command> or install
with npm install -g @ifc-lite/cli.
Core workflows
# Query entities (use exact IFC type names; comma-separated for multiple)
ifc-lite query model.ifc --type IfcWall --json
ifc-lite query model.ifc --type IfcWall --where "Pset_WallCommon.IsExternal=true" --json
ifc-lite query model.ifc --type IfcWall --all --json # props, quantities, materials, classifications, relationships
ifc-lite query model.ifc --type IfcDoor --count
# Inspect one entity fully
ifc-lite props model.ifc --id 42
# Export
ifc-lite export model.ifc --format csv --type IfcWall --columns Name,GlobalId,Pset_WallCommon.FireRating --out walls.csv
ifc-lite export model.ifc --format ifc --schema IFC4 --out filtered.ifc
# Validate against IDS rules (exit 1 on failure)
ifc-lite ids model.ifc requirements.ids --json
# Create IFC from scratch (30+ element types)
ifc-lite create wall --start 0,0,0 --end 5,0,0 --height 3 --thickness 0.2 --out wall.ifc
# Combine / convert / compare
ifc-lite merge arch.ifc struct.ifc mep.ifc --out federated.ifc
ifc-lite convert model.ifc --schema IFC4 --out v4.ifc
ifc-lite diff v1.ifc v2.ifc --by-entity --json
# Structural sanity check
ifc-lite validate model.ifc --json
The power tools: eval and run
When no flag fits, drop into the full SDK. The bim object is the entire
@ifc-lite/sdk surface (discover it with ifc-lite schema).
ifc-lite eval model.ifc "bim.query().byType('IfcWall').count()"
ifc-lite eval model.ifc "bim.storeys().map(s => s.name)"
ifc-lite run analysis.js model.ifc # a .js file with `bim` available globally
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 121 lines · 142 tokens per session scan A 9220a855236c
use-ifclite is a skill published in the GitHub repository LTplus-AG/ifc-lite (363 stars, last pushed today), licensed MPL-2.0. It adds 142 tokens to every session and 1,419 once invoked, about $0.0007 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 2026-08-30.
Other skills, from other repositories
cw-land
Use when turning verified Codewhale work into commits, branches, or a merge: choosing direct-main vs. worktree vs. integration branch, preserving contributor credit, and honoring the gate artifact before merging.
cw-gates
Use before claiming any Codewhale change is done, green, or ready to land: the focused-to-broad verification ladder, the budget checks CI enforces, and the rules for what counts as a passing test.
contributor-onboarding
Help a new contributor get productive on this checkout - inspect sync state against main, build, run the repository's exact verification gate, and produce a local what's-new digest. Never fetches, pulls, or modifies a dirty tree on its own. Explicit-only.
codew-release-qa-sweep
Use before claiming Codewhale release work is done: run the full gate sweep and list the manual QA targets.
gh-close-issues
Close resolved Codewhale issues only after verifying the landed commit/behavior, with a positive crediting comment; never from title alone.
gh-credit-harvest
Harvest one community PR into a release branch with authorship and credit preserved, verified green, and a warm thank-you.