SKILLEMALL.ai

CC weekly-review

Look back at the past week and set up the next one: calendar, mail, goals and files in one page, with a short plan. Use when the user asks for a weekly review, a week in review, "how did my week go" or to plan next week.

The skillemall take

The skill promises to gather your week into one page: calendar, mail, goals, and files with a short plan for next week. Triggers on "how did my week go" or "plan next week" requests.

Files contain 506 tokens, one instruction, no scripts. Tests show grade C (quality 69, process 62), no critical errors. No sandbox or model runs. Supports all major platforms: Claude, Cursor, Gemini, and others.

In practice, you'll get roughly what's described, but no guarantees on data completeness or output structure—the instruction relies on your agent's judgment. Install if a basic weekly summary without precision demands suits you.

nano-muse/nanoMuse Agent Skills author: nano-muse 1 file body ≈ 506 tokens Open the sourcegithub.com↗ analyzed 29 h ago

Look back at the past week and set up the next one: calendar, mail, goals and files in one page, with a short plan.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerPersonal productivitytype and topics are labelled automatically from the skill text
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
C
62/100
Has gaps
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

    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 frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Look back at the past week and set up the next one: calendar, mail… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
    • note frontmatter-key unknown frontmatter key "channel"

    Process rating: all ten parameters 62/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 10 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 506 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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 220: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 10 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.