SKILLEMALL.ai

BC claw-mem2db

Standalone OpenClaw skill that automatically (and on demand) captures the agent's chat conversations and tool-call chains into a local SQLite + FTS5 database, then replays them as token-budgeted context on the next prompt — so the agent doesn't forget across restarts, compaction, or new sessions. Captures `message_received` / `message_sent` (chat, with explicit cues like "remember this" / "note this" promoted to `decision` observations; bilingual EN + zh-CN cue dictionaries built in) and `after_tool_call` (tool); reads on `before_prompt_build` via hybrid recall (FTS5 search on the latest user message merged with the recent tail). Use when the user wants long-lived agent memory, wants to search past chats and tool-call observations, wants explicit "remember this" cues to stick, wants memory to survive restarts / compaction, or wants to export / import memory across machines. Zero-config — `openclaw plugins install @chainofclaw/claw-mem` is sufficient; works fully on its own with no chain interaction and no external services. Exposes agent-callable tools (`mem-search`, `mem-status`, `mem-forget`) and a CLI namespace (`openclaw mem ...`). Session summaries default to the OpenClaw inference surface (`openclaw infer model run`) so no extra API key is needed. Optional companion: install `coc-soul` alongside to upload memory snapshots on-chain, register a DID identity, mirror to P2P decentralized storage, and recover from a different device after corruption — together they enable "digital immortality" / "silicon-based persistence" for an AI agent.

ClawHub Agent Skills author: Meshes & Parallels v2.3.1 MIT-0 6 files body ≈ 5 089 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions

IntegrationAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
75/100
safety, quality, tests
Safety 60%
100
Quality 40%
38
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1566 chars, limit 1024
  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Standalone OpenClaw skill that automatically (and on demand) captu… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning body-long SKILL.md body ≈ 5089 tokens (recommended < 5000); move details to references/
  • note description-budget description takes 1566 of the ~15000-char shared budget for all skills

Process rating: all ten parameters 62/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 5089 tokens
  • 100Steps. 56 steps
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 top-level sections: this looks like several domains in one skill

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)
  • +3Description length 1566: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 56 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 38.

External checks

ClawHub: suspicious
This is a coherent persistent-memory skill, but it broadly records and reinjects chat/tool history and asks users to bypass normal install safety checks.
LLM: suspicious (high) · VirusTotal: · 29 May 2026