CC feedback-learning
Classify and record explicit corrective feedback without turning skills or instructions into append-only knowledge dumps.
Claims to classify and log corrective feedback without turning instructions into knowledge dumps. In reality—a single 974-token file with logic description, no scripts or examples.
Scores paint a mixed picture: quality 72%, process 64%, grade C. No critical issues, but no standout findings either. Supported across all major platforms from Claude to DeepSeek, yet without model runs or sandbox testing, it's unclear how this performs in practice. One file, no broken links.
Install if you need a conceptual framework for feedback handling that doesn't bloat your knowledge base. Skip it if you want a ready-to-use tool.
Classify and record explicit corrective feedback without turning skills or instructions into append-only knowledge dumps.
As a process C 64/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
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") - note
edit-residuethe text marks something as outdated (lines 85): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 64/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 5 mutating operations with no state check
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 26 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 974 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
- +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
- +1No license
- +2Single-language instructions
- +3Description length 121: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 26 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.