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DD msa-search-nim

Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog search, UniRef30/ColabFold env searches, A3M or FASTA alignments, paired MSA search for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment. For local deployment, download the databases in parallel with aria2c and launch via NIM_MODEL_NAME (the recommended default fast path, ~14 min vs over 80 min for the built-in downloader); a plain docker run uses the slow built-in downloader.

ClawHub Agent Skills author: NVIDIA 1 file body ≈ 4 339 tokens Open the sourceclawhub.ai↗ analyzed 6 d ago

Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM.

As a process D 39/100 · Unfinished process — References files that are not bundled: references/api.md, references/science.md, references/parameters.md

GeneratorDockerInfrastructuretype 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
D
76/100
safety, quality, tests
Safety 60%
80
Quality 40%
70
Run on models
none yet
Process rating
D
39/100
Unfinished process
References files that are not bundled: references/api.md, references/science.md, references/parameters.md
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.

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. 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 · 4

✓ No critical or high findings

Medium and low: 4
  • medium Broad scope meta-broad-allowed-tools SKILL.md:1
    Broad tool permissions pre-approved: Bash
    allowed-tools: Bash Read Write AskUserQuestion
  • medium Exfiltration net-credential-use SKILL.md:78
    Credential used in a network call (verify the destination is the intended service)
    curl -fsS -H "Authorization: Bearer $NGC_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:222
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "Authorization: Bearer $NGC_API_KEY" \
  • medium Exfiltration net-credential-use SKILL.md:257
    Credential used in a network call (verify the destination is the intended service)
    curl -s -H "Authorization: Bearer $NGC_API_KEY" \

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/api.md
  • warning missing-ref reference to a missing file: references/science.md
  • warning missing-ref reference to a missing file: references/parameters.md
  • warning missing-ref reference to a missing file: references/validation.md
  • warning missing-ref reference to a missing file: references/examples.md
  • note frontmatter-key unknown frontmatter key "permissions"

Process rating: all ten parameters 39/100

Will not run. References files that are not bundled: references/api.md, references/science.md, references/parameters.md
  • 0Tools and files. 5 referenced file(s) missing: references/api.md, references/science.md, references/parameters.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (msa-search-nim) differs from the folder (bionemo-msa-search-nim)
  • 70Execution cost. Instruction body is 4339 tokens
  • 100Steps. 24 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Running it twice. Mutating operations check current state

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
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +3Description length 540: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 24 items
  • +4Has examples (14 code blocks)
  • +1License stated

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