SKILLEMALL.ai

AC daily-learning

Standardized daily learning framework for AI agents. Unified workflow: study a topic → write notes to local workspace → ingest to shared knowledge base (optional). Use when: (1) setting up daily cron learning tasks, (2) an agent needs the standard learning protocol, (3) consolidating learning output to wiki. Triggers on "定时学习", "daily learning", "学习任务", "learning cron", "学习流程". NOT for: one-off Q&A, immediate task execution, non-structured knowledge gathering.

ClawHub Agent Skills author: mayf3 v1.0.0 MIT-0 5 files · 1 script body ≈ 1 346 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ReferenceAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
100/100
safety, quality, tests
Safety 60%
100
Quality 40%
100
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    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: 5. 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 60/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 28 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1346 tokens
    • low The response is described with custom markup (7 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

    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 464: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (5 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 1 scripts are documented

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

    External checks

    ClawHub: suspicious
    This daily-learning skill is mostly coherent, but it tells recurring agents to inspect prior user context and turn inferred needs into durable learning plans without clear consent or limits.
    LLM: suspicious (medium) · VirusTotal: · 5 Jun 2026