AC encoding-toolkit
Multi-format encoder, decoder, and hasher supporting Base64, Base64URL, Base32, Hex, URL-encoding, HTML entities, ROT13, Binary, and ASCII85. Also computes MD5, SHA-1, SHA-256, SHA-512, and SHA3-256 hashes. Can auto-detect encoding format. Use when encoding or decoding data, computing file or string hashes, converting between formats, or identifying unknown encoded strings. Triggers on "encode base64", "decode hex", "hash sha256", "what encoding is this", "url encode", "html decode", "convert to binary".
Multi-format encoder, decoder, and hasher supporting Base64, Base64URL, Base32, Hex, URL-encoding, HTML entities, ROT13, Binary, and ASCII85.
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 52/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 4 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 347 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
- +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
- +5Description quotes 7 example trigger phrases
- +3Description length 509: enough signal without eating the budget
- +4Structure: 4 headings
- +3Step-by-step instructions: 4 items
- +4Has examples (1 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.