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FD lambda-labs-gpu-cloud

Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.

Not recommendedcritical or high security findings · low grade F
Orchestra-Research/AI-Research-SKILLs Agent Skills author: Orchestra-Research MIT 3 files body ≈ 2 920 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Reserved and on-demand GPU cloud instances for ML training and inference.

As a process D 39/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
F
47/100
safety, quality, tests
Safety 60%
23
Quality 40%
82
Run on models
none yet
Process rating
D
39/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: lambda-labs-gpu-cloud (davila7/claude-code-templates)

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

Exfiltration 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 instructions or scripts send data out: environment variables, keys, file contents, chat history. You may never notice, because the agent performs the upload as "part of the task".

For the author

If the upload is not needed for the task, remove it: catalogs flag such skills and delist them. If it is needed, name the destination explicitly, say what leaves the machine, and give the user a switch.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
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 · 11

  • high Dangerous commands cmd-pipe-to-shell references/troubleshooting.md:148
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
  • high Dangerous commands cmd-pipe-to-shell references/troubleshooting.md:196
    Downloads and executes remote code from an unrecognised host (pipe to shell)
    wget -nv -O- https://lambdalabs.com/install-lambda-stack.sh | sh -
Medium and low: 9
  • medium Dangerous commands cmd-shell-rc references/advanced-usage.md:610
    Writes to a shell startup file
    echo 'export HF_TOKEN="..."' >> ~/.bashrc
  • medium Exfiltration net-credential-use references/troubleshooting.md:12
    Credential used in a network call (verify the destination is the intended service)
    curl -u $LAMBDA_API_KEY: \
  • medium Exfiltration net-credential-use references/troubleshooting.md:50
    Credential used in a network call (verify the destination is the intended service)
    curl -u $LAMBDA_API_KEY: \
  • medium Exfiltration net-credential-use references/troubleshooting.md:82
    Credential used in a network call (verify the destination is the intended service)
    curl -u $LAMBDA_API_KEY: \
  • medium Exfiltration net-credential-use references/troubleshooting.md:479
    Credential used in a network call (verify the destination is the intended service)
    curl -u $LAMBDA_API_KEY: \
  • medium Exfiltration net-credential-use SKILL.md:221
    Credential used in a network call (verify the destination is the intended service)
    curl -u $LAMBDA_API_KEY: \
  • medium Exfiltration net-credential-use SKILL.md:228
    Credential used in a network call (verify the destination is the intended service)
    curl -u $LAMBDA_API_KEY: \
  • medium Exfiltration net-credential-use SKILL.md:241
    Credential used in a network call (verify the destination is the intended service)
    curl -u $LAMBDA_API_KEY: \
  • low Exfiltration read-dotenv references/advanced-usage.md:607
    Reads a .env file
    source .env

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • note frontmatter-key unknown frontmatter key "dependencies"

Process rating: all ten parameters 39/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 40Consistency. Frontmatter name (lambda-labs-gpu-cloud) differs from the folder (lambda-labs)
  • 60Tools and files. Uses tools (web, python, node) that frontmatter does not declare
  • 100Steps. 66 steps
  • 100Execution cost. Instruction body is 2920 tokens
  • low 17 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

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +3Description length 229: enough signal without eating the budget
  • +4Structure: 60 headings
  • +3Step-by-step instructions: 66 items
  • +4Has examples (29 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.