SKILLEMALL.ai
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BC project-orchestrator

AI agent orchestrator with Neo4j knowledge graph, Meilisearch search, and Tree-sitter parsing. Use for coordinating multiple coding agents on complex projects with shared context and plans.

modbender/skill-library-mcp Agent Skills author: modbender MIT 80 files · 4 scripts body ≈ 2 499 tokens Open the sourcegithub.com↗ analyzed 2 d ago

AI agent orchestrator with Neo4j knowledge graph, Meilisearch search, and Tree-sitter parsing.

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationDockerSoftware developmentAI 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%
95
Quality 40%
75
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Dangerous commands cmd-privilege docs/setup/installation.md:283
      Privilege escalation / world-writable permissions
      sudo chown -R $USER:$USER ./workspace

    Files scanned: 80. 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 53/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 7 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 11 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2499 tokens
    • 100Progress reporting. Reports progress
    • low 11 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
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • -2localhost URLs: will not work for another user
    • -34 of 4 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 189: enough signal without eating the budget
    • +4Structure: 28 headings
    • +3Step-by-step instructions: 11 items
    • +4Has examples (17 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.