SKILLEMALL.ai

AC manager-first-90-days

Plan a new manager's first 90 days — first-time or new-to-team — as listen/decide/move phases: the 1:1 listening tour with real questions, the early-judgment traps, the quick-wins filter, and the 30/60/90 artifacts. Use when asked I just became a manager what do I do, plan my first 90 days as a manager, first-time manager advice, taking over an existing team, or new manager 30-60-90 plan. Produces the phased plan, the listening-tour question set, the doer-to-enabler mindset shift, the team assessment framework, and the day-one and week-6 artifacts.

mohitagw15856/pm-claude-skills Agent Skills author: mohitagw15856 MIT 1 file body ≈ 1 318 tokens Open the sourcegithub.com analyzed 2 d ago

Plan a new manager's first 90 days — first-time or new-to-team — as listen/decide/move phases: the 1:1 listening tour with real questions, the early-judgment…

As a process C 60/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

AnalyzerOperations and projectsData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
60/100
Has gaps
Failures and branches w 10
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: manager-first-90-days (mohitagw15856/pm-claude-skills)

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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 60/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 23 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1318 tokens
    • low 11 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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 554: enough signal without eating the budget
    • +4Structure: 13 headings
    • +3Step-by-step instructions: 23 items
    • +3Output format is stated explicitly

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.