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AC todoist

Integrate with Todoist task management using natural language. Use when the user wants to manage Todoist tasks or projects through conversational commands like "show my tasks for today", "add 'call dentist' to my todo list", "complete my task about the meeting", or any mention of Todoist, tasks, due dates, or project management.

modbender/skill-library-mcp Agent Skills author: modbender MIT 5 files body ≈ 632 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Integrate with Todoist task management using natural language.

As a process C 58/100 · Has gaps — weak spots: failures and branches, consistency, running it twice

IntegrationPersonal productivitytype 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
94/100
safety, quality, tests
Safety 60%
95
Quality 40%
93
Run on models
none yet
Process rating
C
58/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
Running it twice w 4
30
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 README.md:36
      Privilege escalation / world-writable permissions
      sudo cp todoist.skill /usr/lib/node_modules/openclaw/skills/

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "credentials"

    Process rating: all ten parameters 58/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (todoist) differs from the folder (todoist-natural-language)
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 35 steps, 1 vague phrases
    • 100Execution cost. Instruction body is 632 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

    • +4Description does not say when NOT to use the skill (false activations)
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 3 example trigger phrases
    • +3Description length 330: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 35 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.