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CD tao-setup

One-time session setup and orchestration map for the TAO skill bank. Run this first when the TAO skills were installed individually (e.g. from a public skills catalog) so the session gets the cross-skill discovery flow, credential checks, and host preflight that the bundled plugin hook would otherwise inject automatically. Trigger phrases include "set up TAO skills", "TAO session setup", "prepare TAO environment", "TAO getting started".

ClawHub Agent Skills author: NVIDIA 1 file body ≈ 1 608 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

One-time session setup and orchestration map for the TAO skill bank.

As a process D 46/100 · Unfinished process — References files that are not bundled: references/skill_info.yaml, scripts/tao_job_record.py, scripts/nccl_allreduce_probe.py

ProcedureDockerKubernetesSoftware developmentInfrastructureAI and agentstype 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
87/100
safety, quality, tests
Safety 60%
93
Quality 40%
78
Run on models
none yet
Process rating
D
46/100
Unfinished process
References files that are not bundled: references/skill_info.yaml, scripts/tao_job_record.py, scripts/nccl_allreduce_probe.py
Tools and files w 18
0
Result and completion w 14
0
Progress reporting w 2
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 · 3

✓ No critical or high findings

Medium and low: 3
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Read Bash
  • low Exfiltration read-dotenv SKILL.md:33
    Reads a .env file
    set -a; source /path/to/.env; set +a   # omit if already exported
  • low Exfiltration read-dotenv SKILL.md:59
    Reads a .env file (detector / deny-list definition)
    Load a user-approved env file with `set -a; source /path/to/.env; set +a` in the
    detector

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: references/skill_info.yaml
  • warning missing-ref reference to a missing file: scripts/tao_job_record.py
  • warning missing-ref reference to a missing file: scripts/nccl_allreduce_probe.py
  • warning missing-ref reference to a missing file: scripts/install-codex-agents.sh

Process rating: all ten parameters 46/100

Will not run. References files that are not bundled: references/skill_info.yaml, scripts/tao_job_record.py, scripts/nccl_allreduce_probe.py
  • 0Tools and files. 4 referenced file(s) missing: references/skill_info.yaml, scripts/tao_job_record.py, scripts/nccl_allreduce_probe.py
  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1608 tokens

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +5Description quotes 4 example trigger phrases
  • +3Description length 440: enough signal without eating the budget
  • +4Structure: 6 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (2 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.