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AC synapse

Self-learning memory engine for OpenClaw agents. Analyzes agent interactions, extracts operator intelligence, updates structured profiles, and improves recall accuracy over time. Integrates with OpenClaw memory_search and memory_get tools. Supports daily learning cycles, preference extraction, pattern detection, and cross-session memory consolidation. Triggers on phrases like "remember this", "what do you know about", "update memory", "what have you learned", "my preferences are", "search memories".

ClawHub Agent Skills author: shadoprizm v1.1.0 MIT-0 4 files body ≈ 1 104 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

IntegrationAI and agentsInfrastructuretype 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
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Consistency w 8
40
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: 4. 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 62/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 40Consistency. Frontmatter name (synapse) differs from the folder (synapse-memory)
  • 55Failures and branches. 1 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Tools and files. No external tools needed
  • 100Steps. 30 steps
  • 100Execution cost. Instruction body is 1104 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress

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
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 6 example trigger phrases
  • +3Description length 504: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: suspicious
Synapse is a coherent local memory skill, but it can automatically read prior conversations and build persistent profiles without clear user opt-in, review, or deletion controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026