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Run the full DEFT smart-data-augmentation loop for NVIDIA TAO Grounding DINO object detection: zero-shot baseline inference, KPI analysis, per-class gap analysis, SigLIP embedding of weak images, unique-neighbor mining against a source pool, ODVG dataset staging, and retraining — repeated for a fixed number of iterations. Also prepares the source pool the loop mines from, as a separate run: Co-DETR pseudo-labeling, folding to the target classes, KITTI→COCO→ODVG conversion, and embedding. Use for prompts like "run the DEFT OD loop", "run smart data augmentation for grounding dino", "mine and retrain my detection model", "improve OD mAP with gap analysis and mining", "prep the source pool", or "pseudo-label my unlabeled images for mining"; do not use for standalone TAO training, one-off inference, or gap analysis alone.

ClawHub Agent Skills author: NVIDIA 1 file body ≈ 2 985 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

Run the full DEFT smart-data-augmentation loop for NVIDIA TAO Grounding DINO object detection: zero-shot baseline inference, KPI analysis, per-class gap…

As a process C 57/100 · Has gaps — References files that are not bundled: scripts/init_deft_state.py, scripts/deft_python.sh, scripts/commit_stage.py

AnalyzerDockerAI and agentsData and analyticstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
95
Quality 40%
79
Run on models
none yet
Process rating
C
57/100
Has gaps
References files that are not bundled: scripts/init_deft_state.py, scripts/deft_python.sh, scripts/commit_stage.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Skill Task Bash Write

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/init_deft_state.py
  • warning missing-ref reference to a missing file: scripts/deft_python.sh
  • warning missing-ref reference to a missing file: scripts/commit_stage.py
  • warning missing-ref reference to a missing file: references/prep-source-pool.md
  • warning missing-ref reference to a missing file: references/preflight.md
  • warning missing-ref reference to a missing file: references/pipeline-and-state.md
  • warning missing-ref reference to a missing file: references/*.md
  • warning missing-ref reference to a missing file: references/scripts-and-agents.md
  • warning missing-ref reference to a missing file: references/tao-analyze-gaps-od-map.md
  • warning missing-ref reference to a missing file: references/tao-generate-image-embeddings.md
  • warning missing-ref reference to a missing file: references/tao-mine-od-images.md
  • warning missing-ref reference to a missing file: references/stage-mined-data.md
  • warning missing-ref reference to a missing file: references/grounding-dino.md
  • warning missing-ref reference to a missing file: references/tao-analyze-detection-kpi.md
  • warning missing-ref reference to a missing file: references/data-layout.md

Process rating: all ten parameters 57/100

Will not run. References files that are not bundled: scripts/init_deft_state.py, scripts/deft_python.sh, scripts/commit_stage.py
  • 0Tools and files. 15 referenced file(s) missing: scripts/init_deft_state.py, scripts/deft_python.sh, scripts/commit_stage.py
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 100Steps. 22 steps
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2985 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 11 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +3Description length 829: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 6 example trigger phrases
  • +4Description says when NOT to use the skill
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 22 items
  • +4Has examples (2 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.