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AC session-scribe

Automatically summarize active OpenClaw session transcripts into daily memory files using a cheap LLM. Run as a system cron job — reads new transcript entries since last run, summarizes them via OpenAI or Anthropic API, and appends bullet-point notes to a daily memory file. No gateway involvement, no context bloat. Designed to pair with the supermemory skill for full memory pipeline automation. Use when you want session context preserved without relying on agent self-reporting.

ClawHub Agent Skills author: Kitsune v1.0.1 MIT-0 5 files body ≈ 1 090 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

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

IntegrationAI and agentsInfrastructureWriting 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
A
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
91
Run on models
none yet
Process rating
C
51/100
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:69
      Mentions editing / listing crontab (quoted — discussed, not commanded)
      Add to crontab (`crontab -e`) to run every hour:
      quoted

    Files scanned: 5. 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 51/100

    • 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
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 4 mutating operations with no state check
    • 40Consistency. Frontmatter name (session-scribe) differs from the folder (session-scribe-openclaw)
    • 55Failures and branches. 1 branches
    • 100Tools and files. No external tools needed
    • 100Steps. 9 steps
    • 100Execution cost. Instruction body is 1090 tokens
    • low The response is described with custom markup (9 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
    • +1No license
    • +2Single-language instructions
    • +3Description length 482: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: clean
    This skill is a disclosed transcript summarizer that can expose sensitive conversation content if configured broadly, but its behavior matches its stated purpose.
    LLM: benign (high) · VirusTotal: · 29 May 2026