CC agent-pseudocode
Agent skill for pseudocode - invoke with $agent-pseudocode
The skill promises pseudocode assistance—apparently generates or parses algorithms in semi-textual format. One file, no critical errors, safety score maxed out. Code quality sits at 69, execution process at 57. Never ran on models, never tested in sandbox. Works across nine platforms from Claude to DeepSeek.
Actual output remains a mystery: no data on what goes in and out, syntax accuracy, or performance on complex algorithms. You can install it if you need a supplementary tool without guarantees. If reliability matters—test it on your own examples first.
Agent skill for pseudocode - invoke with $agent-pseudocode
As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
The same skill appears in 1 more place: RA-Skills
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 0
✓ No critical or high findings
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 57/100
- 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
- 50When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 21 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2063 tokens
- 100Running it twice. No mutating operations
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)
- +3Description length 58: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 13 headings
- +3Step-by-step instructions: 21 items
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.