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AC synthetic-supermemory

Full automated memory pipeline for OpenClaw agents. Scribe session transcripts into structured daily memory files, ingest them into Supermemory for semantic recall, and retrieve enriched context at session startup. Use when you want persistent memory across sessions without agent self-reporting. Triggers on requests like "set up memory", "remember sessions", "recall context", "ingest memory files", "search memories", or any task requiring long-term agent memory. Requires SUPERMEMORY_API_KEY and OPENAI_API_KEY (or ANTHROPIC_API_KEY).

ClawHub Agent Skills author: Kitsune v2.1.0 MIT-0 9 files body ≈ 1 097 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureAI and agentstype 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
97/100
safety, quality, tests
Safety 60%
99
Quality 40%
93
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-cron-mention SKILL.md:58
      Mentions editing / listing crontab (quoted — discussed, not commanded)
      ## Cron setup (add via `crontab -e`)
      quoted

    Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50Steps. 2 steps
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1097 tokens
    • low The response is described with custom markup (8 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +3Description length 538: enough signal without eating the budget
    • +4Structure: 8 headings
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 5 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    This skill appears purpose-built for persistent agent memory, but it needs review because it can repeatedly process private transcripts and send derived content to external services.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026