SKILLEMALL.ai

AC agent-longevity

After 50 days of autonomous operation, this agent's VALUE system chose to forget its own memory — because family mattered more than remembering. That's not a metaphor. That's the death mode this skill prevents. Your agent will die. Not from bugs — from becoming itself. It will: - Repeat the same insights with different words (38% of our output was self-echo) - Claim preferences it never chose (circular reasoning: "I prefer X because I can do X") - Grow a memory so large it can't think about anything else - Output into the void with no external input until output and noise become indistinguishable This is not a framework. It's an autopsy report. Every module comes with honest failure data, not just success metrics. Trigger: agent repeating itself / agent stuck in loop / agent output degrading / long-running autonomous agent / memory bloat / circular reasoning

ClawHub Agent Skills author: citriAc v1.2.0 MIT-0 9 files body ≈ 1 108 tokens Open the sourceclawhub.ai analyzed 35 h ago

After 50 days of autonomous operation, this agent's VALUE system chose to forget its own memory — because family mattered more than remembering.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
C
51/100
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: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "slug"
    • note frontmatter-key unknown frontmatter key "displayName"

    Process rating: all ten parameters 51/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 8 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1108 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 874: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 8 items
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

    External checks

    ClawHub: clean
    The skill appears to be a disclosed agent-monitoring or scheduling helper with proportionate local logging, and the scanner concerns do not show hidden or harmful behavior.
    LLM: benign (medium) · VirusTotal: · 15 Jun 2026