BF fold
Submits and manages FastFold protein folding jobs via the Jobs API. Covers authentication, creating jobs, polling for completion, and fetching CIF/PDB URLs, metrics, and viewer links. Use when folding protein sequences with FastFold, calling the FastFold API, or scripting fold-and-wait workflows.
Submits and manages FastFold protein folding jobs via the Jobs API.
As a process F 34/100 · Will not run — References files that are not bundled: references/.env.example
How to improve
- The text references files that are not there: add them or drop the references.
- 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
-
low Secrets in code
secret-high-entropy-tokenreferences/jobs.yaml:136High-entropy token-like string (may be an id, hash or a credential)huma…nd:
Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: references/.env.example
Process rating: all ten parameters 34/100
- 0Tools and files. 1 referenced file(s) missing: references/.env.example
- 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
- 30Running it twice. 17 mutating operations with no state check
- 40Consistency. Frontmatter name (fold) differs from the folder (fastfold-ai-fold)
- 70When it triggers. States when to use, but not when not to
- 85Steps. 39 steps, 1 vague phrases
- 100Execution cost. Instruction body is 1945 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low The response is described with custom markup (6 tags): a typed call is more reliable
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
- +4No input/output examples
- -32 of 7 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 297: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 39 items
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.