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AF memory-anchor

Long-term memory for OpenClaw agents — SQLite hybrid recall (FTS5 + keyword + associative expansion + optional LLM embeddings), raw/curated anchors, session briefings, and an explicit INDEX/REINDEX pipeline. Use when the user wants durable memory across sessions, Persistent Sage-style recall, or to remember/ search facts, preferences, and past conversations.

ClawHub Agent Skills author: g00sifer Development Lab v0.1.0 MIT-0 2 files body ≈ 1 551 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Long-term memory for OpenClaw agents — SQLite hybrid recall (FTS5 + keyword + associative expansion + optional LLM embeddings), raw/curated anchors, session…

As a process F 44/100 · Will not run — References files that are not bundled: references/DESIGN.md

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
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
F
44/100
Will not run
References files that are not bundled: references/DESIGN.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 0. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/DESIGN.md

Process rating: all ten parameters 44/100

Will not run. References files that are not bundled: references/DESIGN.md
  • 0Tools and files. 1 referenced file(s) missing: references/DESIGN.md
  • 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 (memory-anchor) differs from the folder (memory-anchor-openclaw-2)
  • 55Failures and branches. 1 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 15 steps
  • 100Execution cost. Instruction body is 1551 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 360: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 15 items
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This is a coherent long-term memory skill, but it asks agents to silently retain and index conversation-derived facts with broad scope and limited user control.
LLM: suspicious (high) · 17 Jul 2026