Skills DirectorySkills Directory
SkillsLearnSecurityCategoriesDocsCommunityBlog
Sign InSubmit Skill
Skills Directory

Security-tested agent skills for Claude, coding agents, and AI workflows.

Directory

  • Browse Skills
  • All Skills A–Z
  • Claude Skills
  • Claude Code Skills
  • Agent Skills
  • Categories
  • Authors
  • Submit a Skill

Learn

  • Learn Hub
  • Install Claude Skills
  • Write SKILL.md
  • Skills vs MCP
  • Directories Compared

Security

  • Security
  • Methodology
  • Secure Claude Skills
  • Security Badges

Company

  • About
  • Community
  • Blog
  • API Docs
  • Advertise

2026 Skills Directory. All rights reserved.

ProTermsPrivacyRefunds
Back to skills

E1011 Phys Env

ASecurity

Use when assess the physical and psycho-physiological environments relevant to human performance under ECSS-E-ST-10-11C §4.2.1.6: categorize each environment parameter as physical (thermal, acoustic, vibration, lighting, or atmospheric composition) or psycho-physiological (workload, stress, sleep adequacy), verify that all required parameters have been assessed for the applicable mission phase, check each measured or predicted value against its human-performance acceptability bound, flag any ...

2 stars
0 votes
0 copies
0 views
Added 9/27/2026
ai-agentspythongoperformance

Works with

claude code

Security Analysis

A100/100

Scanned 9/27/2026

Install to Claude Code

$npx -y skills add ashfordeOU/aero-agent-skills --skill e1011-phys-env --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of E1011 Phys Env?

Add the live security badge to your README — it updates automatically with every re-scan.

Security grade badge for E1011 Phys Env
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/ashfordeou-e1011-phys-env/badge)](https://www.skillsdirectory.com/skills/ashfordeou-e1011-phys-env)

More formats (shields.io, HTML) on the badges page.

Download with Pro
Files
SKILL.md
---
name: e1011-phys-env
description: "Use when assess the physical and psycho-physiological environments relevant to human performance under ECSS-E-ST-10-11C §4.2.1.6: categorize each environment parameter as physical (thermal, acoustic, vibration, lighting, or atmospheric composition) or psycho-physiological (workload, stress, sleep adequacy), verify that all required parameters have been assessed for the applicable mission phase, check each measured or predicted value against its human-performance acceptability bound, flag any parameter that falls outside the acceptable range, and confirm full coverage before closing the environment characterisation. Trigger: ecss, e-st-10-system-scope, e-st-10-11c, physical-environment, psycho-physiological, human-performance, thermal, acoustic, vibration, illuminance, atmospheric-quality."
license: Apache-2.0
compliance: STANDARDS-REF
standards:
  - id: ecss
    reference-only: true
gated: false
domain: space-systems
pack: space-systems
compatibility: "agentskills.io SKILL.md; any SKILL.md host (Claude Code, Hermes, OpenClaw)"
metadata:
  domain: space-systems
  subdomain: ecss
  tags: [ecss, e-st-10-system-scope, e-st-10-11c, physical-environment, psycho-physiological, human-performance, thermal, acoustic, vibration, illuminance, atmospheric-quality]
  version: 0.1.0
  author: Aero Agent Skills
---

# ECSS Human Factors — Physical and Psycho-physiological Environment Characterisation (space-systems/ecss/e1011-phys-env)

Use when the task is the physical and psycho-physiological environment
characterisation required by ECSS-E-ST-10-11C §4.2.1.6 -- categorizing
environment parameters, verifying assessment coverage across all mission
phases, checking each parameter value against its human-performance
acceptability bound, and flagging deficiencies before the characterisation
is declared complete.

## Domain quick reference

- §4.2.1.6 splits the environment into two families: physical parameters
  (thermal: temperature and humidity; acoustic: noise level; vibration:
  whole-body rms acceleration; lighting: illuminance; atmospheric
  composition: oxygen and CO2 partial pressures) and psycho-physiological
  parameters (workload index, stress/fatigue index, sleep adequacy metric).
  Each parameter is categorized into exactly one family before its value
  is checked.
- Each parameter carries an acceptability bound derived from HFE literature
  and applicable standards; a value within [lower_bound, upper_bound] is
  within the range that supports sustained human performance. A value
  outside that range is flagged as an exceedance, regardless of family.
- Coverage is required across all mission phases and operational contexts
  defined in the human-centred design planning document; a parameter not
  yet assessed is a coverage gap, which is itself a finding, not a pass.
- Bounds may be tightened by mission-specific requirements (e.g. a stricter
  CO2 limit for long-duration missions) by overriding the default bounds
  map; the default bounds supplied by this skill are the common HFE
  baseline, not a mission design rule.

## Workflow

1. Inventory every environment parameter relevant to the mission profile
   and categorize each one as physical or psycho-physiological. Reject an
   unrecognized parameter before it enters the assessment.
2. Verify that all required parameters have been assessed; list any
   parameter with no measured or predicted value as a coverage gap.
   Do not treat an uncovered parameter as implicitly acceptable.
3. For each assessed parameter, retrieve the applicable human-performance
   acceptability bound (use the default if no mission-specific bound is
   defined) and check whether the measured or predicted value falls within
   [lower_bound, upper_bound].
4. Flag every parameter whose value lies outside its bound as an
   exceedance; record the parameter name, measured value, and the bound
   that was violated.
5. Aggregate exceedances and coverage gaps into the environment review
   result; the characterisation is acceptable only when both lists are
   empty.
6. Report the full review result to the human-centred design planning
   process for integration into the context-of-use description.

## Pitfalls

- Omitting psycho-physiological parameters from the characterisation and
  treating only the physical environment as complete -- §4.2.1.6 explicitly
  requires both families; a characterisation with no workload, stress, or
  sleep data is an incomplete assessment.
- Using default bounds without checking for mission-specific overrides --
  long-duration or confined-habitat missions routinely impose tighter CO2
  and noise limits than the HFE baseline; applying the default bound in
  those contexts can mask a real exceedance.
- Treating a parameter with no assessed value as within bounds -- an
  unassessed parameter represents an unknown risk, not compliance; the
  correct finding is a coverage gap, not a pass.
- Collapsing all acoustic exceedances into a single "noise problem" flag
  without recording the measured value -- the exact level is needed to size
  the required mitigation and to verify that the corrected design brings the
  value back within bound.

## Behavior contract (gate 3)

The parameter categorization, bounds checking, coverage assessment, and
environment review aggregation logic is exercised by the gate 3 contract
test: scripts/test_e1011_phys_env.py against
scripts/e1011_phys_env_logic.py (stdlib unittest, offline). Run:
python3 scripts/test_e1011_phys_env.py

## Compliance

- ECSS standards are freely downloadable (ESA); cite the source and
  paraphrase per standards-map.yaml.
- compliance: STANDARDS-REF, gated: false.

Attribution

ashfordeOUashfordeOU
View sourceMore from ashfordeOU →
SSkills DirectorySkills Directory

Know which skills are safe — weekly.

Best new skills + every skill we flagged as malicious. From the team that scanned 103,619.

Join free

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments (0)

No comments yet. Be the first to comment!

SSkills DirectorySkills Directory

Know which skills are safe — weekly.

Best new skills + every skill we flagged as malicious. From the team that scanned 103,619.

Join free

Related Skills

Caveman

Ultra-compressed communication mode that cuts output tokens while keeping technical accuracy. Levels: lite, full, ultra and the wenyan variants. Use for /caveman, "caveman mode", "talk like caveman", "be brief" or "less tokens".

1074701 votes

Hyperplan

Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...

694821 votes

Mcp Code Execution

Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.

3351 votes

catchup

Recovers the conversation and failed tool calls of a previous Codex, Claude Code, Antigravity, Cline, Copilot CLI, Cursor, DeepSeek Harness, Kimi, OpenCode, Pi Agent, or ZCode session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", asks to recover/summarize a previous session before continuing, or asks to diagnose or report a catchup failure. Do NOT use for the current conversation, git history, or any non-agent log.

691 votes

math-skill

A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling

381 votes
View all in ai-agents →