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AD memory-dreaming

A Markdown + JSON memory framework with conversation archiving for AI agents. Provides persistent long-term memory with biologically-inspired decay, recall boosting, temporal fact chains, dream-cycle consolidation, and channel-agnostic conversation archiving with AI-generated summaries. No vector database, graph store, or external service required. Use when you need: agent memory that persists across sessions, conversation context across channels/groups/topics, fact lifecycle tracking (supersession), or automated memory maintenance via dream cycles.

ClawHub Agent Skills author: Peter Rossi v0.1.2 MIT-0 13 files body ≈ 2 046 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 47/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 13. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 47/100

    • 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. 10 mutating operations with no state check
    • 40Consistency. Frontmatter name (memory-dreaming) differs from the folder (openclaw-memory-dreaming)
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 60Failures and branches. 2 branches
    • 100Steps. 31 steps
    • 100Execution cost. Instruction body is 2046 tokens
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (13 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 555: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 31 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (4 of 4)
    • +3All 6 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: suspicious
    This skill appears purpose-built rather than deceptive, but it handles persistent memory, full chat archives, external summarization, and plaintext secrets in ways users should review carefully before installing.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026