SKILLEMALL.ai

AC daily-focus-board

Spin up a personal, motivating daily focus board that renders in a browser canvas and that the user drives by talking to their AI partner. Tasks track status (to-do → in progress → done) with timestamped progress notes and roll up into a "today's momentum" feed; numeric-goal tasks (pages, pomodoros, reps) render as progress-bar counters. Executive-function / neurodivergent-friendly by design: Focus mode, kind "not today" carryover (no overdue-shaming), a brain-dump box, reduced-motion, and gentle deadline countdowns. Add, reorder, and relabel tasks live, assign Eisenhower priority (Do first / Schedule / Delegate / Later), open with an above/below-the-line check-in and a daily mantra, and save an end-of-day recap. Use when someone wants to plan their day, stay focused, kick off a work session, or track progress. Progress persists in the browser (localStorage).

github/awesome-copilot Agent Skills author: github MIT 7 files · 1 script body ≈ 2 734 tokens Open the sourcegithub.com analyzed 32 h ago

Spin up a personal, motivating daily focus board that renders in a browser canvas and that the user drives by talking to their AI partner.

As a process C 54/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorGitHubPersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
54/100
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: 7. 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 54/100

    • 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
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 30 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2734 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +3Description length 871: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 30 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.