SKILLEMALL.ai
GitHub ↗ RU Sign in

BD mindgraph-rs

A high-performance, structured knowledge graph memory system for AI agents. Provides 18 cognitive tools for reasoning, decision tracking, and goal management. Runs fully local — no cloud, no external graph store. PRIVACY: The server never calls home. The optional extraction scripts read session transcripts and send summaries to your configured LLM provider (Gemini/Anthropic/Moonshot) — same provider your agent already uses. Extraction and dreaming are opt-in.

modbender/skill-library-mcp Agent Skills author: modbender MIT 17 files · 2 scripts body ≈ 1 615 tokens Open the sourcegithub.com↗ analyzed 2 d ago

A high-performance, structured knowledge graph memory system for AI agents.

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

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
B
87/100
safety, quality, tests
Safety 60%
97
Quality 40%
73
Run on models
none yet
Process rating
D
46/100
Unfinished process
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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
  • low Dangerous commands cmd-background-process scripts/start.sh:41
    Starts a background / autostarted process (code comment)
    # Use nohup + setsid to fully detach from gateway process group.
    comment
  • low Dangerous commands cmd-background-process scripts/start.sh:43
    Starts a background / autostarted process
    nohup setsid "$BINARY" >> "$LOGFILE" 2>&1 &
  • low Dangerous commands cmd-background-process scripts/start.sh:45
    Starts a background / autostarted process
    disown $!

Files scanned: 17. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 46/100

  • 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
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 4 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (node) that frontmatter does not declare
  • 85Steps. 31 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1615 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

  • +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
  • -36 of 10 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 463: enough signal without eating the budget
  • +4Structure: 17 headings
  • +3Step-by-step instructions: 31 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.