SKILLEMALL.ai

CC nasa-schedule

Knowledge base from the NASA Schedule Management Handbook (2024 edition, Revision 2). Use for program/project schedule management of aerospace and large engineering efforts: the five sub-functions (Planning, Development, Assessment & Analysis, Maintenance & Control, Documentation & Communication); the Integrated Master Schedule (IMS) and the WBS/OBS/CBS breakdown structures; the Schedule Management Plan (SMP) and Basis of Estimate (BoE); Critical Path Method, logic, durations, float and margin; tiered schedule assessment (Requirements/Health/Basis checks, the Shock Test) and the Dimensions of Schedule Reliability; Schedule Risk Analysis (SRA), Integrated Cost-Schedule Risk Analysis (ICSRA), Monte Carlo, correlation, merge bias, and the Joint Confidence Level (JCL); baselining, replan vs. rebaseline, EVM schedule metrics (SPI, BEI, CEI, HMI, Earned Schedule, CPLI), and status/progress/forecast reporting. This is NASA's scheduling doctrine grounded in NPR 7120.5 and the GAO Schedule Assessment Guide. Thin on the underlying mechanics of CPM, statistics, and EVM (it points to companion handbooks); aerospace/space-flight-centric; anchored to the 2024 second edition.

jgsystemsconsulting/jgs-se-knowledge-packs Agent Skills author: jgsystemsconsulting 14 files body ≈ 3 272 tokens Open the sourcegithub.com↗ analyzed 29 h ago

Knowledge base from the NASA Schedule Management Handbook (2024 edition, Revision 2).

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ReferenceData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
81/100
safety, quality, tests
Safety 60%
100
Quality 40%
53
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Shorten the description to 1024 characters.
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

  • error description-long description is 1179 chars, limit 1024

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 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. 2 mutating operations with no state check
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Tools and files. No external tools needed
  • 100Steps. 66 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3272 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)
  • +3Description length 1179: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +2Single-language instructions
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 66 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.