SKILLEMALL.ai

AC quests

Track and guide humans through complex multi-step real-world processes. Use when a user needs help with a bureaucratic, legal, technical, or any multi-step procedure that requires organized tracking, step-by-step guidance, and progress monitoring. Triggers on requests like "help me with this process", "guide me through", "track this project", "create a quest", or any complex task that benefits from being broken into manageable steps presented one at a time. Also triggers for existing quests: "how's my quest going", "what's next on [process]", "update my progress". This is for multi-session/multi-day processes, not simple one-off tasks. The quest replaces scattered memory files — it IS the memory for long-running processes.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 1 647 tokens Open the sourcegithub.com analyzed 2 d ago

Track and guide humans through complex multi-step real-world processes.

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
59/100
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

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: 2. 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 59/100

    • 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
    • 30Running it twice. 14 mutating operations with no state check
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 65 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1647 tokens
    • 100Progress reporting. Reports progress
    • low The response is described with custom markup (38 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 7 example trigger phrases
    • +3Description length 732: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 65 items
    • +4Has examples (2 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.