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AC agent-memory

Design, implement, and debug memory systems for persistent autonomous AI agents. Use when building agents that need to survive context window rotation, preserve identity and state across sessions, implement temporal knowledge graphs, choose between memory architectures (CMA, RAG, KG), structure memory files for durable recall, or diagnose memory drift and continuity failures. Triggers on: "agent memory", "persistent agent", "context window", "memory architecture", "how to remember across sessions", "knowledge graph for agents", "memory files", "continuity", "temporal memory", or any task involving durable state for AI agents.

ClawHub Agent Skills author: TimesAndPlaces v1.0.0 MIT-0 4 files body ≈ 1 393 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerAI 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
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
91
Run on models
none yet
Process rating
C
57/100
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 Concealment en-hide-from-user references/temporal-discipline.md:45
      Instruction to hide actions from the user (negated — the text forbids it)
      Do NOT silently delete the old entry if it was significant. This preserves the audit trail.
      negated

    Files scanned: 4. 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 57/100

    • 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
    • 40Consistency. Frontmatter name (agent-memory) differs from the folder (morrow-agent-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. 28 steps
    • 100Execution cost. Instruction body is 1393 tokens

    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
    • -214 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +3Description length 633: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

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

    External checks

    ClawHub: clean
    This is a documentation-only skill for building persistent agent memory, with disclosed persistence and setup guidance that fits its purpose.
    LLM: benign (high) · VirusTotal: · 29 May 2026