SKILLEMALL.ai

BB deepsleep

Two-phase daily memory persistence for AI agents. v3.0: unified pack+dispatch cron, smart silence handling, memory importance tiers, cross-group correlation, OQ health tracking, and schedule priority system.

ClawHub Agent Skills author: JeffChang2024 v3.0.0 MIT-0 6 files body ≈ 3 225 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process B 66/100 · Nearly there — weak spots: result and completion, when it triggers

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
66
Run on models
none yet
Process rating
B
66/100
Nearly there
Result and completion w 14
0
When it triggers w 12
20
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 66/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 68 steps
  • 100Failures and branches. 5 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3225 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 14 top-level sections: this looks like several domains in one skill

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
  • -228 emoji in the instructions: noise for the model
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 207: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 68 items
  • +4Has examples (9 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 66.

External checks

ClawHub: suspicious
This skill appears to be a group-memory and briefing tool, but it requests broad cross-chat visibility, stores and reuses conversation history, and can send proactive briefs without sufficiently clear scoping or consent boundaries.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026