A skill your agent uses when targeting Applied Energy or deciding whether a systems-level energy manuscript fits this venue.

MITAuto-check passed

Install Applied Energy

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill applied-energy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills applied-energy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Engineering-Technology-Journal-Skills/skills/applied-energy .claude/skills/applied-energy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
applied-energy
GitHub stars
1.2k
Token cost
~2k tokens
SKILL.md length
787 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when targeting Applied Energy or deciding whether a systems-level energy manuscript fits this venue.

  • Targeting Applied Energy
  • SKILL.md covers Journal positioning, When to trigger, Scope & topic fit and Method & evidence bar, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Deciding whether a systems-level energy manuscript fits this venue

What it does

Applied Energy is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when targeting Applied Energy or deciding whether a systems-level energy manuscript fits this venue. Encodes the journal's fit, the systems-scope-and-quantified-impact bar, modeling and assessment rigor, house style, the systems-vs-single-device routing, official-submission re-check, and desk-reject heuristics.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的… The licence is MIT.

When your agent uses it

  • Targeting Applied Energy
  • Deciding whether a systems-level energy manuscript fits this venue

Example prompts

  • “/applied-energy”

What it can do on your machine

Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Applied Energy loads about 2k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 787 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~83
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 787 words, ~1,968 tokens.

Download SKILL.mdSave it as .claude/skills/applied-energy/SKILL.md (or your agent's skills folder).
name
applied-energy
description
Use when targeting Applied Energy or deciding whether a systems-level energy manuscript fits this venue. Encodes the journal's fit, the systems-scope-and-quantified-impact bar, modeling and assessment rigor, house style, the systems-vs-single-device routing, official-submission re-check, and desk-reject heuristics.

Applied Energy (applied-energy)

Journal positioning

Applied Energy (Elsevier) is a systems-level energy research venue: energy conversion and integration, energy-systems modeling and optimization, decarbonization pathways, demand and efficiency, and techno-economic and environmental assessment. Its center of gravity is the system, not the device — how technologies, sectors, and markets combine to deliver, store, or save energy, and what the quantified energetic, economic, and environmental consequences are. The single most common misfit is a single-material or single-component study (for example, one electrode or one catalyst) submitted as if it were systems research; such work belongs at a materials or device venue. A paper succeeds when its contribution is a generalizable systems-level insight backed by transparent, validated analysis. This skill is a fit / venue-selection / re-framing tool. It does not replace the journal's current official author guidelines. Before submitting, re-check the live Applied Energy Guide for Authors on the Elsevier site.

When to trigger

  • The author names Applied Energy for an energy-systems modeling, integration, decarbonization, efficiency, or techno-economic/environmental-assessment manuscript.
  • A paper must be re-framed from a device/component result into a systems-level question with quantified energy/economic/environmental impact — or re-routed if it is in fact device-level.
  • The author is deciding between Applied Energy's systems scope and a device venue (journal-of-power-sources) or a materials venue (energy-storage-materials).
  • The author needs the journal's systems-scope and assessment rigor bar and desk-reject heuristics.

Scope & topic fit

  • Energy-systems modeling and optimization: power, heat, transport, and multi-vector systems; dispatch, planning, and integration of variable renewables and storage.
  • Energy conversion and integration at the system/process level, including hybrid and sector-coupled configurations and waste-heat/energy recovery.
  • Decarbonization pathways and scenario analysis: emissions, cost, and feasibility of transitions across technologies, sectors, or regions.
  • Demand-side, efficiency, buildings, and flexibility: demand response, load modeling, and end-use efficiency with system-level consequences.
  • Techno-economic analysis (TEA) and life-cycle/environmental assessment (LCA) of energy technologies and systems, with transparent assumptions.
  • Data-driven and machine-learning methods for energy systems when the contribution is a generalizable systems insight, not a black-box fit to one dataset.

Method & evidence bar

  • The central claim is a systems-level insight: a result about how a system performs, integrates, or decarbonizes, with quantified energy, economic, and/or environmental outcomes — not a component performance number.
  • Models must be transparent and validated/benchmarked where possible; assumptions, boundaries (system boundary, time horizon, spatial scope), and data sources stated.
  • Techno-economic and environmental results require disclosed cost/emission factors, functional units, and uncertainty/sensitivity analysis; point estimates without sensitivity are weak.
  • Generalizability and scenario robustness must be shown: results should hold or be characterized across cases, not depend on one favorable assumption set.
  • Comparisons must use a fair baseline/counterfactual; reproducibility requires that data, parameters, and (where applicable) code be sufficiently described.
Show full SKILL.md (349 more words)Show less

Structure & house style

  • Standard research-article structure (introduction, methods/model, results, discussion); the journal uses highlights and a graphical abstract — re-check current article types and requirements on the live guide.
  • The introduction frames the energy-systems gap and the decision/insight at stake, not a component novelty; methods state the system boundary, scope, and assumptions explicitly.
  • Figures are load-bearing: system diagrams, scenario/optimization results, cost and emission breakdowns, and sensitivity/uncertainty plots.
  • Supporting information carries full model formulations, parameter tables, and data; main-text figures must support the systems claim on their own.

Official-submission checklist

  • Before giving submission-ready advice, read ../../resources/source-basis.md and ../../resources/official-source-map.md; start from the Elsevier anchors, then cite the current Applied Energy Guide for Authors page you checked.
  • Search the live site for "Applied Energy guide for authors" and follow the current Elsevier/Editorial Manager version.
  • Re-check article types, highlights and graphical-abstract requirements, and length/figure expectations.
  • Confirm data/code-availability expectations and assumption/parameter-reporting norms for TEA/LCA and modeling work.
  • Re-check competing-interests, funding, author-contribution (CRediT), and AI-use disclosure requirements.
  • If the live official instructions conflict with this skill, the official instructions win.

Pre-submission self-check

  • The contribution is a systems-level insight with quantified energy/economic/environmental impact, not a single-device result.
  • System boundary, time horizon, spatial scope, and key assumptions are stated explicitly.
  • Models are transparent and validated/benchmarked; data sources are disclosed.
  • TEA/LCA results include functional units, cost/emission factors, and uncertainty/sensitivity analysis.
  • Results are shown to be robust across scenarios against a fair baseline/counterfactual.
  • Highlights and graphical abstract represent the systems-level advance.

Common desk-reject triggers

  • Single-material/single-device study (one electrode, one catalyst) framed as systems research.
  • TEA/LCA with undisclosed assumptions, no functional unit, or no uncertainty/sensitivity analysis.
  • Optimization/modeling tuned to one case with no generalizable systems insight.
  • Black-box machine-learning fit to one dataset with no transferable energy-systems finding.
  • Results that depend on a single favorable assumption set with no robustness check.
  • Component-performance paper with system relevance asserted only in the abstract.

Re-routing decision

  • Device-level electrochemical power (cells, fuel cells, supercapacitors) → journal-of-power-sources.
  • Electrode/electrolyte materials and mechanism → energy-storage-materials.
  • Applied catalysis/separation process at the unit level → chemical-engineering-journal.
  • Authoritative energy/combustion review synthesis → progress-in-energy-and-combustion-science.
  • Highest-profile cross-cutting energy advance → nature-energy, joule, or energy-and-environmental-science (different selectivity/format; re-check).

Output format

text
[Fit] High / Medium / Low (one-line reason)
[Target] Applied Energy
[Topic tags] <2–3 closest energy-systems subtopics>
[Systems insight] <the system-level claim and quantified impact in one line>
[Scope/boundary] <system boundary, horizon, and key assumptions stated?>
[Rigor] <validation + uncertainty/sensitivity + fair baseline present?>
[Top risk] <the single most likely reason for rejection>
[Official items to re-check] <article type / highlights / data-code / TEA-LCA reporting / disclosures>
[Re-route suggestion] <if device/material-level or out of scope, a better-matched venue>

© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in Engineering-Technology-Journal-Skills/skills/applied-energy of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Applied Energy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Energy Procurementaffaan-m/ECC276k2 repos~2.5kAutomated safety check: PassApache-2.0
Energy Procurementsickn33/agentic-awesome-skills47k2 repos~7.4kAutomated safety check: PassMIT
Detecting Bluetooth Low Energy Attacksmukul975/Anthropic-Cybersecurity-Skills34k—~3.4kAutomated safety check: PassApache-2.0

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Questions about Applied Energy

What does Applied Energy do?

A skill your agent uses when targeting Applied Energy or deciding whether a systems-level energy manuscript fits this venue. Applied Energy is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when targeting Applied Energy or deciding whether a systems-level energy manuscript fits this venue.

When should I use Applied Energy?

Applied Energy fits situations like: targeting Applied Energy; deciding whether a systems-level energy manuscript fits this venue.

How do I install Applied Energy in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill applied-energy -a claude-code`. Or copy the skill folder (Engineering-Technology-Journal-Skills/skills/applied-energy in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/applied-energy in your project. Claude Code loads it when a task matches its description.

How do I install Applied Energy in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill applied-energy -a codex`. Or copy the skill folder (Engineering-Technology-Journal-Skills/skills/applied-energy in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/applied-energy in your project. Codex loads it when a task matches its description.

Can I use Applied Energy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill applied-energy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/applied-energy, .gemini/skills/applied-energy, .github/skills/applied-energy and .opencode/skills/applied-energy in your project.

What does Applied Energy need to run?

SKILL.md names no scripts, command-line tools or credentials: Applied Energy is instructions for the agent only.

Does Applied Energy access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Applied Energy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Applied Energy use?

Applied Energy is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Applied Energy use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Applied Energy?

Skills that share tags, products or a category with Applied Energy: Touch Targets (thedaviddias/Front-End-Checklist, 74k stars), Energy Procurement (affaan-m/ECC, 276k stars), Energy Procurement (affaan-m/ECC, 276k stars) and Energy Procurement (sickn33/agentic-awesome-skills, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Applied Energy?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.

Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.