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 commands/l3digitalnet/claude-code-plugins/rungit clone --depth 1 https://github.com/L3DigitalNet/Claude-Code-PluginsWhat 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 | $0.00039 | $0.00691 |
| Opus 5 | $0.00019 | $0.00345 |
| Sonnet 5 | $0.00008 | $0.00138 |
| Haiku 4.5 | $0.00004 | $0.00069 |
Grade A, and why
run 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/qt-suite:run — Run Test Suite
Run the project's Qt test suite and report results. Auto-detect whether this is a Python/PySide6 or C++ project based on project files.
Step 1: Read Config and Detect Project Type
Read .qt-test.json if present — use project_type, build_dir, and test_dir fields.
If no config file:
- Check for
CMakeLists.txt→ C++ project (CTest) - Check for
pyproject.tomlorsetup.cfgorpytest.ini→ Python project (pytest) - Check for both → run both suites
Step 2: Run Python Tests (pytest)
When project type is python or both:
# Set offscreen platform for headless CI compatibility
QT_QPA_PLATFORM=offscreen pytest tests/ -v --tb=short
If an argument was provided (e.g., /qt-suite:run test_calculator):
QT_QPA_PLATFORM=offscreen pytest tests/ -v --tb=short -k "test_calculator"
For projects with pyproject.toml defining qt_api = pyside6, no extra flags are needed — pytest-qt picks up the config automatically.
Step 3: Run C++ Tests (CMake + CTest)
When project type is cpp or both:
Determine build directory from config (build_dir field) or default to build/.
# Configure if build dir doesn't exist yet
cmake -B build -DCMAKE_BUILD_TYPE=Debug
# Build (incremental — only changed files)
cmake --build build --parallel 4
# Run all tests
cd build && ctest --output-on-failure --parallel 4
If an argument was provided (e.g., /qt-suite:run TestCalculator):
cd build && ctest -R "TestCalculator" --output-on-failure
Step 4: Report Results
Parse and report the test outcomes clearly:
Python (pytest):
Test Results: 23 passed, 1 failed, 2 skipped (4.2s)
FAILED tests/test_calculator.py::TestCalculator::test_divide_by_zero
AssertionError: ZeroDivisionError not raised
(show the full failure message)
C++ (CTest):
Test Results: 18/20 tests passed (2.1s)
FAILED: TestMainWindow (exit code 1) (show stdout from the failing test)
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 · 94 lines · 39 tokens per session scan A 510c6d3923d7
run is a command published in the GitHub repository L3DigitalNet/Claude-Code-Plugins (6 stars, last pushed 2d ago), licensed MIT. It adds 39 tokens to every session and 691 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.