SKILLEMALL.ai

BF Memory Leak Detector

Static memory leak pattern scanner for Node.js, Python, Go, and Java. Analyzes source files to detect event listener leaks (addEventListener without corresponding removeEventListener), unbounded cache growth (Maps/objects grown in closures without eviction), setInterval/setTimeout references that prevent GC, large buffer allocations inside request handlers, global variable accumulation, circular reference patterns, and missing cleanup in class destructors/useEffect. For Node.js also runs --expose-gc heap snapshot diff (before/after load test) to confirm leaks at runtime. Zero external service — pure static analysis + optional local Node.js heap. Triggers on "memory leak", "heap growing", "OOM in production", "memory usage", "event listener leak", "setInterval not cleared", "/mem-leak".

ClawHub Agent Skills author: Lucius Pang v1.0.3 MIT-0 2 files body ≈ 4 040 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process F 32/100 · Will not run — References files that are not bundled: \w+

AnalyzerSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
86/100
safety, quality, tests
Safety 60%
100
Quality 40%
64
Run on models
none yet
Process rating
F
32/100
Will not run
References files that are not bundled: \w+
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
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.
  2. The text references files that are not there: add them or drop the references.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • warning missing-ref reference to a missing file: \w+
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 32/100

Will not run. References files that are not bundled: \w+
  • 0Tools and files. 1 referenced file(s) missing: \w+
  • 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
  • 40Consistency. Frontmatter name (Memory Leak Detector) differs from the folder (phy-memory-leak-detector)
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 4040 tokens
  • 100Steps. 9 steps

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
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +5Description quotes 7 example trigger phrases
  • +3Description length 796: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 9 items
  • +4Has examples (13 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.

External checks

ClawHub: clean
This is a coherent local developer skill for finding memory leaks, with a caution that its optional runtime profiling example can create local load and profiler files.
LLM: benign (high) · VirusTotal: · 29 May 2026