SKILLEMALL.ai
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BD taskline-ai

AI-powered natural language task management through MyTaskline.com. Transform complex requests like "Ask Sarah to review the Mobile project docs by Friday with high priority" into fully structured tasks with automatic project creation, people assignment, smart date parsing, and priority detection. Features complete intent recognition, multi-entity parsing, and seamless integration with the MyTaskline.com platform for personal and team productivity.

modbender/skill-library-mcp Agent Skills author: modbender MIT 14 files body ≈ 1 804 tokens Open the sourcegithub.com↗ analyzed 2 d ago

AI-powered natural language task management through MyTaskline.com. Transform complex requests like "Ask Sarah to review the Mobile project docs by Friday…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationData and analyticstype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
D
49/100
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 · 0

✓ No critical or high findings

Files scanned: 14. 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 49/100

  • 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. 5 mutating operations with no state check
  • 40Consistency. Frontmatter name (taskline-ai) differs from the folder (taskline)
  • 50Failures and branches. 0 branches, has a failure section
  • 85Steps. 50 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Execution cost. Instruction body is 1804 tokens
  • low 10 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
  • -253 emoji in the instructions: noise for the model
  • -32 of 7 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 452: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 50 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.