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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.

ClawHub Agent Skills author: Mohammed Abuhalib v0.1.0 MIT-0 2 files body ≈ 1 563 tokens Open the sourceclawhub.ai↗ analyzed 19 h ago

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

IntegrationAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
F
44/100
Will not run
References files that are not bundled: scripts/memory.sh
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • 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-ref reference to a missing file: scripts/memory.sh

Process rating: all ten parameters 44/100

Will not run. References files that are not bundled: scripts/memory.sh
  • 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.

External checks

ClawHub: clean
This is a disclosed cloud-backed memory skill, with privacy considerations but no artifact-backed malicious or deceptive behavior.
LLM: benign (medium) · VirusTotal: · 9 Jul 2026