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Personal knowledge base for capturing and retrieving information about people, places, restaurants, games, tech, events, media, ideas, and organizations. Use when: user mentions a person, place, restaurant, landmark, game, device, event, book/show, idea, or company. Trigger phrases: "remember", "note that", "met this person", "visited", "played", "what do I know about", etc. Brain entries take precedence over daily logs for named entities.

modbender/skill-library-mcp Agent Skills author: modbender MIT 10 files body ≈ 2 594 tokens Open the sourcegithub.com↗ analyzed 35 h ago

Personal knowledge base for capturing and retrieving information about people, places, restaurants, games, tech, events, media, ideas, and organizations.

As a process D 38/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferencePersonal productivitySoftware 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
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
D
38/100
Unfinished process
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

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 · 0

    ✓ No critical or high findings

    Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "setup"
    • note frontmatter-key unknown frontmatter key "permissions"

    Process rating: all ten parameters 38/100

    • 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. 11 mutating operations with no state check
    • 40Consistency. Frontmatter name (brain) differs from the folder (2nd-brain)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 85Steps. 59 steps, 3 vague phrases
    • 100Execution cost. Instruction body is 2594 tokens
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill
    • low The response is described with custom markup (4 tags): a typed call is more reliable

    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)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 6 example trigger phrases
    • +3Description length 443: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 59 items
    • +4Has examples (11 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.