SKILLEMALL.ai

BC executing-nist-rmf-authorization-to-operate

Drive a federal system through the NIST Risk Management Framework (SP 800-37 Rev 2) to an Authorization to Operate (ATO): Prepare, Categorize (FIPS 199), Select a control baseline (FIPS 200 / SP 800-53 Rev 5), Implement, Assess (SP 800-53A), Authorize, and Monitor continuously. Use when a system needs an ATO or a renewal, when working a FISMA/FedRAMP authorization package, when building or reviewing an SSP, SAR, or POA&M, when categorizing a system as Low/Moderate/High impact, when selecting or tailoring a control baseline, or when standing up continuous monitoring (ConMon) after authorization. Covers ATO, conditional ATO (cATO), and the artifacts assessors expect. Keywords: NIST RMF, 800-37, ATO, authorization to operate, FISMA, FedRAMP, SSP, SAR, POA&M, FIPS 199, FIPS 200, 800-53, 800-53A, control baseline, security categorization, continuous monitoring, authorizing official, system boundary, ongoing authorization.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 5 files · 1 script body ≈ 1 852 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Drive a federal system through the NIST Risk Management Framework (SP 800-37 Rev 2) to an Authorization to Operate (ATO): Prepare, Categorize (FIPS 199)…

As a process C 64/100 · Has gaps — weak spots: when it triggers, failures and branches, running it twice

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
C
64/100
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

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: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 64/100

    • 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
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 34 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1852 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 930: 120–800 characters recommended
    • +4No input/output examples
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +2Single-language instructions
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 34 items
    • +3Output format is stated explicitly
    • +3All 1 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.