SKILLEMALL.ai

AF openclaw-memory-canonical

Lightweight file-based memory system for single-user AI agents. Uses markdown files only: HOT/WARM/COLD/BUFFER plus SCRATCH learnings, with tiered loading, WAL-lite writes, compaction recovery, weekly review, TTL archival, health checks, and a built-in self-improvement loop. Use when setting up or improving agent memory, preventing memory bloat or repeated mistakes, promoting recurring lessons into durable files, validating memory health, or when users mention memory systems, working buffers, episodic/semantic/procedural memory, learnings, weekly review, self-improvement, or how an AI agent should remember and improve between sessions.

ClawHub Agent Skills author: ciklopentan v4.6.14 MIT-0 13 files · 4 scripts body ≈ 7 290 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process F 53/100 · Will not run — References files that are not bundled: references/package-tree.sha256

AnalyzerAI 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%
75
Run on models
none yet
Process rating
F
53/100
Will not run
References files that are not bundled: references/package-tree.sha256
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
  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: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 7290 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: references/package-tree.sha256

Process rating: all ten parameters 53/100

Will not run. References files that are not bundled: references/package-tree.sha256
  • 0Tools and files. 1 referenced file(s) missing: references/package-tree.sha256
  • 0Result and completion. Does not say what the result is
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 50When it triggers. No condition that starts the skill
  • 70Execution cost. Instruction body is 7290 tokens
  • 100Steps. 132 steps
  • 100Failures and branches. 30 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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 643: enough signal without eating the budget
  • +4Structure: 30 headings
  • +3Step-by-step instructions: 132 items
  • +4Has examples (13 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 4 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This is a disclosed local file-based memory skill that persists workspace memory files and helper scripts, with no evidence of hidden network access, credential theft, or destructive behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026