SKILLEMALL.ai

BC safe-self-improving

A privacy-first, consent-based self-improvement skill for AI agents. Captures learnings, errors, best practices with auto-sanitization and duplicate detection. Includes smart skill synthesis — auto-generates new skill drafts from recurring patterns. No hooks, no cross-session, no silent modification. All operations require user confirmation.

ClawHub Agent Skills author: hjfl888 v1.3.1 MIT-0 5 files body ≈ 2 228 tokens Open the sourceclawhub.ai analyzed 29 h ago

A privacy-first, consent-based self-improvement skill for AI agents.

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

AnalyzerDockerAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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. 90 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2228 tokens
  • low The response is described with custom markup (4 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

  • +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
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -233 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 343: enough signal without eating the budget
  • +4Structure: 22 headings
  • +3Step-by-step instructions: 90 items
  • +4Has examples (11 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: suspicious
The skill is mostly local and consent-based, but its privacy promises conflict with documented options to install generated skills and publish them externally.
LLM: suspicious (high) · VirusTotal: · 17 Jun 2026