AC prompt-archaeology
Excavate forgotten solutions, code snippets, and decisions from past conversation sessions. Use when the user is re-solving a problem you've likely solved before, hunting for a lost snippet, or wants to mine session history for buried knowledge instead of starting from scratch.
Excavate forgotten solutions, code snippets, and decisions from past conversation sessions.
As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 278 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
Process rating: all ten parameters 58/100
- 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
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 60Consistency. The Hermes dialect needs category and tags
- 100Steps. 53 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 3099 tokens
- 100Running it twice. No mutating operations
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
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
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 278: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 53 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.