SKILLEMALL.ai
GitHub ↗ RU Sign in

BF memory-optimization

Comprehensive memory management optimization for AI agents. Use when: (1) Agent experiences context compression amnesia, (2) Need to rebuild context quickly after session restart, (3) Want structured memory system with TL;DR summaries, (4) Need automated daily memory maintenance, (5) Want to implement knowledge graph for entity management, or (6) Building agent memory system from scratch. Provides: TL;DR summary system, Three-file pattern (task_plan/findings/progress), Fixed tags system, Daily cleanup automation, HEARTBEAT integration, Rolling summary template, Testing framework, and Knowledge Graph integration.

ClawHub Agent Skills author: richardiitse v1.0.4 MIT-0 70 files · 2 scripts body ≈ 2 104 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 40/100 · Will not run — References files that are not bundled: references/implementation.md, references/templates.md, references/knowledge-graph.md

IntegrationAI and agentsWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
F
40/100
Will not run
References files that are not bundled: references/implementation.md, references/templates.md, references/knowledge-graph.md
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. 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: 54. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/implementation.md
  • warning missing-ref reference to a missing file: references/templates.md
  • warning missing-ref reference to a missing file: references/knowledge-graph.md

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: references/implementation.md, references/templates.md, references/knowledge-graph.md
  • 0Tools and files. 3 referenced file(s) missing: references/implementation.md, references/templates.md, references/knowledge-graph.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 50 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2104 tokens
  • 100Progress reporting. Reports progress
  • low 14 top-level sections: this looks like several domains in one skill

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
  • -33 of 18 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 620: enough signal without eating the budget
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 50 items
  • +4Has examples (13 code blocks)
  • +1License stated

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

External checks

ClawHub: suspicious
This memory skill is not clearly malicious, but it needs Review because it can read past agent sessions, send memory content to external model APIs, and persist or mutate memory data with under-scoped controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026