Borrowing it
Nothing to install: this file belongs to akiojin/llmlb. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/akiojin/llmlb/develop/.claude/skills/gwt-project-index/SKILL.mdgit clone --depth 1 https://github.com/akiojin/llmlbWrote 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/akiojin/llmlb/gwt-project-index)<a href="https://agentmods.dev/skills/akiojin/llmlb/gwt-project-index"><img src="https://agentmods.dev/badge/skills/akiojin/llmlb/gwt-project-index/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/akiojin/llmlb/gwt-project-index"><img src="https://agentmods.dev/badge/skills/akiojin/llmlb/gwt-project-index.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00029 | $0.00375 |
| Opus 5.5 | $0.00012 | $0.00150 |
| Sonnet 5.5 | $0.00006 | $0.00075 |
| Haiku 4.5 | $0.00003 | $0.00038 |
Grade A, and why
gwt-project-index scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Project Structure Index
gwt maintains a vector search index of all project files using ChromaDB embeddings.
File search command
Run in terminal to find files related to a feature or concept:
~/.gwt/runtime/chroma-venv/bin/python3 ~/.gwt/runtime/chroma_index_runner.py \
--action search \
--db-path "$GWT_PROJECT_ROOT/.gwt/index" \
--query "your search query" \
--n-results 10
On Windows, use ~/.gwt/runtime/chroma-venv/Scripts/python.exe as the Python executable.
File search output format
JSON object with ranked results:
{"ok": true, "results": [
{"path": "src/git/issue.rs", "description": "GitHub Issue commands", "distance": 0.12},
{"path": "src/lib/components/IssuePanel.svelte", "description": "Issue list panel", "distance": 0.25}
]}
When to use
- Task start: search for files related to the assigned feature
- Bug investigation: find files that might relate to the bug
- Feature addition: locate existing similar implementations
- Architecture understanding: discover how components are organized
Environment
GWT_PROJECT_ROOT: absolute path to the project root (set by gwt at pane launch)
Notes
- File index is auto-generated when the project is opened in gwt
- Uses semantic similarity (not just keyword matching)
- Lower distance values indicate higher relevance
- For Issue search, use
gwt-issue-searchinstead
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.
- 8d ago First seen · 52 lines · 29 tokens per session scan A 73e2f7a57b42
gwt-project-index is a skill published in the GitHub repository akiojin/llmlb (10 stars, last pushed 3d ago), licensed MIT. It adds 29 tokens to every session and 375 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-21.
Other skills, from other repositories
autorag-doctor
Diagnose and repair a broken or half-working AutoRAG install so every configured source is both indexed and searchable. Checks AutoRAG, MinSync, Jikji, the embedding backend, and every CLI-backed datasource (lazykatok, discrawl, slacrawl, wacrawl, telecrawl, notcrawl, mailcrawl, rclone, Spotlight), then fixes orphan…
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.
rag-blueprint
NVIDIA RAG Blueprint — deploy, configure, troubleshoot, and manage. Handles any RAG action: deploy, install, start, enable, disable, toggle, change, configure, troubleshoot, debug, fix, shutdown, stop, or tear down any RAG feature or service (Agentic RAG, VLM, guardrails, query rewriting, models, search, ingestion…
profile-vllm-performance
Profile vLLM and RT-VLM inference to identify and remove GPU-idle gaps, underfilled batches, transfer stalls, serialized multimodal work, scheduler gaps, or KV pressure. Use this skill when GPU utilization is unexpectedly low or bursty, capacity trails another runtime, TTFT or ITL regresses, or multimodal prefill and…
rag-architecture-review
Review an existing Retrieval-Augmented Generation system and find why it underperforms. Use when asked to review or audit a RAG pipeline, diagnose wrong/ungrounded answers from a 'chat with your docs' feature, or improve an already-built knowledge assistant. Produces a staged review — ingestion, chunking, retrieval…
rag-index-decisions
After making a non-obvious architectural decision, solving a novel bug, agreeing on a coding standard, or reaching a conclusion worth remembering, index it back into the knowledge base so the next occurrence is one search away. Uses adddocument or addfromurl. Closes the feedback loop that makes a RAG-backed team…