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BC nist-csf

Knowledge base from NIST Cybersecurity Framework (CSF) 2.0. Use for cybersecurity risk management, GOVERN, IDENTIFY, PROTECT, DETECT, RESPOND, RECOVER, Organizational Profiles, CSF Tiers, supply chain risk, enterprise risk management, incident response. Covers the CSF 2.0 document only; does not cover specific SP 800-53 controls, NIST RMF system authorization steps, or sector-specific regulations.

jgsystemsconsulting/jgs-se-knowledge-packs Agent Skills author: jgsystemsconsulting 14 files body ≈ 2 947 tokens Open the sourcegithub.com↗ analyzed 86 min ago

Knowledge base from NIST Cybersecurity Framework (CSF) 2.0. Use for cybersecurity risk management, GOVERN, IDENTIFY, PROTECT, DETECT, RESPOND, RECOVER…

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

ReferenceInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
98
Quality 40%
81
Run on models
none yet
Process rating
C
60/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Risky intent intent-offensive-security chapters/ch05-detect-de.md:25
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      - **Monitoring only the network perimeter**: DE.CM covers networks (DE.CM-01), physical environments (DE.CM-02), personnel (DE.CM-03), external providers (DE.CM-06), and computing environments (DE.CM-
    • low Risky intent intent-offensive-security patterns.md:44
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (detector / deny-list definition)
      **When to use**: When planning recovery capabilities; when validating that the organization can actually recover from ransomware or destructive attacks.
      detector

    Files scanned: 14. 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

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 82 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2947 tokens
    • 100Progress reporting. Reports progress

    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
    • +2Single-language instructions
    • +3Description length 400: enough signal without eating the budget
    • +4Structure: 18 headings
    • +3Step-by-step instructions: 82 items
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.