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BC smart-memory

Context-aware memory for AI agents with dual retrieval modes — fast vector search or curated Focus Agent synthesis. SQLite backend, zero configuration, local embeddings.

modbender/skill-library-mcp Agent Skills author: modbender MIT 23 files · 1 script body ≈ 1 939 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Context-aware memory for AI agents with dual retrieval modes — fast vector search or curated Focus Agent synthesis.

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationDockerAI 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
B
89/100
safety, quality, tests
Safety 60%
89
Quality 40%
88
Run on models
none yet
Process rating
C
59/100
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Dangerous commands medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

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 · 3

    ✓ No critical or high findings

    Medium and low: 3
    • medium Dangerous commands cmd-pipe-to-shell-known-host README.md:41
      Pipe-to-shell installer from a well-known host (still executes remote code)
      curl -sL https://raw.githubusercontent.com/BluePointDigital/smart-memory/main/install.sh | bash
    • medium Dangerous commands cmd-pipe-to-shell-known-host skills/vector-memory/README.md:42
      Pipe-to-shell installer from a well-known host (still executes remote code)
      curl -sL https://raw.githubusercontent.com/YOUR_USERNAME/vector-memory-openclaw/main/install.sh | bash
    • low Dangerous commands cmd-pipe-to-shell-known-host install.sh:3
      Pipe-to-shell installer from a well-known host (still executes remote code) (code comment)
      # Usage: curl -sL https://raw.githubusercontent.com/BluePointDigital/smart-memory/main/install.sh | bash
      comment

    Files scanned: 23. 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 59/100

    • 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. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 100Tools and files. No external tools needed
    • 100Steps. 31 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1939 tokens
    • low 14 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 169: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 31 items
    • +4Has examples (17 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.