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CB faa-hf-std

Knowledge base from the FAA Human Factors Design Standard (HF-STD-001B, 2016) — the FAA's consolidated human-factors / human-systems-integration design criteria for systems it manages, operates, or maintains. Use for requirement-level design rules on: general human-factors design principles; automation and function allocation; designing equipment for maintenance; displays, controls, and visual indicators; alarms, audio, and voice communications; the computer-human interface (information presentation, coding, interaction styles, windows, dialogue); keyboards, input devices, and workstation/workplace ergonomics; system security, personnel safety, environment, anthropometry, and user documentation; plus intended-use and tailoring guidance. It is a quantified, source-attributed synthesis (heavily citing MIL-STD-1472G, MIL-HDBK-759C, DOD-HFDG-ATCCS, NUREG-0700, NASA-STD-3000, ANSI, OSHA, ISO 9241). Complements nasa-hsi as a parallel HSI tradition from the civil-aviation domain. Scope limits: it is design criteria, NOT an HSI lifecycle/process model (use nasa-hsi, nasa-npr-7123, or dau-se-guidebook for process); it does NOT reproduce the third-party standards it cites; it is FAA-acquisition-oriented (ATC, towers, TRACONs, airway facilities), not a general consumer-UX guide; and it is thin on modern touchscreen/mobile, web, and AI-driven interface patterns and on quantitative human-reliability analysis.

jgsystemsconsulting/jgs-se-knowledge-packs Agent Skills author: jgsystemsconsulting 15 files body ≈ 4 184 tokens Open the sourcegithub.com↗ analyzed 29 h ago

Knowledge base from the FAA Human Factors Design Standard (HF-STD-001B, 2016) — the FAA's consolidated human-factors / human-systems-integration design…

As a process B 67/100 · Nearly there — weak spots: when it triggers, failures and branches, running it twice

ReferenceSoftware developmentWriting and documentsAI and agentstype 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
C
81/100
safety, quality, tests
Safety 60%
97
Quality 40%
56
Run on models
none yet
Process rating
B
67/100
Nearly there
Failures and branches w 10
0
When it triggers w 12
20
Running it twice w 4
30
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Risky intent intent-offensive-security chapters/ch04-faa-hf-std-displays-controls-and-visual-indicators.md:16
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - **DOD-HFDG-ATCCS (1992)** — the DoD Human Factors Design Guide for the Army Tactical Command and Control System; cited for prototype evaluation, resolution and character-formation criteria, stereosc
  • low Risky intent intent-offensive-security chapters/ch08-faa-hf-std-input-devices-and-workplace-design.md:16
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - **DOD-HFDG-ATCCS (1992), including V2.0** — the DoD Human Factors Design Guide for Army Tactical Command and Control Systems. Source for numeric-keypad layout, QWERTY arrangement, cursor-movement ke
  • low Risky intent intent-offensive-security chapters/ch08-faa-hf-std-input-devices-and-workplace-design.md:17
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - **DON UISNCCS (1992)** — the Department of the Navy User Interface Specifications for Navy Command and Control Systems. The exclusive source for the pointer/pointing-device behavioural model: pointe

Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1419 chars, limit 1024

Process rating: all ten parameters 67/100

  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 60Result and completion. Output format stated, no completion criterion
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4184 tokens
  • 100Tools and files. No external tools needed
  • 100Steps. 70 steps
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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 1419: 120–800 characters recommended
  • +4No input/output examples
  • +2Single-language instructions
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 70 items
  • +3Output format is stated explicitly
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.