AF dataecho-memory
Persistent memory for agents — durable across sessions, machines, sandboxes, and agent platforms, stored in a private DataEcho cloud drive. Use at the start of a session to recall who the user is and what you were working on; whenever the user says "remember this", "don't forget", "like last time", or references past sessions; when you learn a durable preference, make a decision worth keeping, or finish a milestone; and when handing state to another agent or a future session.
Persistent memory for agents — durable across sessions, machines, sandboxes, and agent platforms, stored in a private DataEcho cloud drive.
As a process F 44/100 · Will not run — References files that are not bundled: scripts/memory.sh
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 · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: scripts/memory.sh
Process rating: all ten parameters 44/100
- 0Tools and files. 1 referenced file(s) missing: scripts/memory.sh
- 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
- 30Running it twice. 4 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 70When it triggers. States when to use, but not when not to
- 100Steps. 11 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1563 tokens
- 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 480: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 11 items
- +4Has examples (4 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.