SKILLEMALL.ai

AC memory-digest

Generate human-readable weekly/monthly reports from an AI agent's memory corpus. Use when the user asks "what did my agent do this week", "generate a work report", "记忆周报", "述职报告", "weekly digest", "monthly summary of agent activity", or when a periodic review of agent work is wanted. Reads date-named daily memory logs and produces a digest with activity timeline, project momentum, open threads, and metric changes.

ClawHub Agent Skills author: Thomaszhou v1.0.0 MIT-0 6 files body ≈ 543 tokens Open the sourceclawhub.ai analyzed 2 d ago

Generate human-readable weekly/monthly reports from an AI agent's memory corpus.

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorAI and agentsData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
54/100
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: 6. 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 54/100

    • 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
    • 70When it triggers. States when to use, but not when not to
    • 85Steps. 7 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 543 tokens

    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 4 example trigger phrases
    • +3Description length 417: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 7 items
    • +4Has examples (1 code blocks)
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: clean
    This skill reads an agent's memory logs to generate a digest, and that behavior is clearly disclosed and aligned with its purpose.
    LLM: benign (high) · VirusTotal: · 8 Sept 2026