SKILLEMALL.ai

AD failure-memory

Automatic failure pattern recording and recall system. Prevents repeating the same mistakes by logging errors with context, root cause, and resolution. Use when: (1) a command/task fails and you want to record why, (2) starting a new task and want to check for known pitfalls, (3) reviewing accumulated failure patterns for learning, (4) agent makes an error and needs to log it for future prevention. Triggers: 'log failure', 'check failures', 'failure report', 'what went wrong', 'mistake log', or any error/failure during agent work.

ClawHub Agent Skills author: Voidlight v1.0.0 3 files · 1 script body ≈ 710 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentsInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 3. 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 49/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 40Consistency. Frontmatter name (failure-memory) differs from the folder (failure-memory-log)
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 100Steps. 20 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 710 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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 536: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 20 items
    • +4Has examples (6 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: clean
    This is a straightforward local failure log skill, with the main risk being that sensitive error details could be saved on disk if the user does not manage the log carefully.
    LLM: benign (high) · VirusTotal: · 29 May 2026