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AF logseq-import

Import notes from Logseq pages into the slipbox. Use when user pastes a Logseq page with properties and bulleted notes. Parses page-level properties, extracts each bullet as an individual note, handles nested bullets by adding parent context, then runs slipbot for each.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 177 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Import notes from Logseq pages into the slipbox.

As a process F 43/100 · Will not run — References files that are not bundled: url

ProcedureWriting and documentsResearchSoftware developmenttype 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
F
43/100
Will not run
References files that are not bundled: url
Tools and files w 18
0
Result and completion w 14
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: url

Process rating: all ten parameters 43/100

Will not run. References files that are not bundled: url
  • 0Tools and files. 1 referenced file(s) missing: url
  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 12 mutating operations with no state check
  • 40Consistency. Frontmatter name (logseq-import) differs from the folder (slipbot-logseq-importer)
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 54 steps, 1 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 1177 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
  • +1No license
  • +2Single-language instructions
  • +3Description length 270: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 54 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.