New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Jmgmt Contribution Framing

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when turning results into an explicit theoretical contribution for a Journal of Management (JOM) manuscript — the "what new theory do we learn?" statement and the discussion…

MITAuto-check passedData & Analytics

Install Jmgmt Contribution Framing

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmgmt-contribution-framing -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jmgmt-contribution-framing --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/Journal-of-Management-Skills/skills/jmgmt-contribution-framing .claude/skills/jmgmt-contribution-framing && 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
jmgmt-contribution-framing
GitHub stars
1.2k
Token cost
~1.4k tokens
SKILL.md length
628 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when turning results into an explicit theoretical contribution for a Journal of Management (JOM) manuscript — the "what new theory do we learn?" statement and the discussion…

  • Works in 5 steps: Restates the core finding in theoretical… → Theoretical implications — for each,… → Practical implications — specific to the… → …
  • Tasks that involve Data analysis
  • SKILL.md covers When to trigger, The contribution question JOM…, Types of theoretical… and Writing the discussion, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Jmgmt Contribution Framing is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when turning results into an explicit theoretical contribution for a Journal of Management (JOM) manuscript — the "what new theory do we learn?" statement and the discussion section. Frames the contribution; it does not build the original theory (jmgmt-theory-development) or run the analysis (jmgmt-data-analysis).

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Data analysis. 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

  • Tasks that involve Data analysis

Example prompts

  • “what new theory do we learn?”
  • “/jmgmt-contribution-framing”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Restates the core finding in theoretical terms (one short paragraph, no number-dump).
  2. Theoretical implications — for each, name the literature, state what changes, and how. This is the heart of the section, and where…
  3. Practical implications — specific to the phenomenon and grounded in the findings, never boilerplate.
  4. Limitations — honest, but framed as boundary conditions and future directions, not fatal confessions.
  5. Future research — concrete questions the study opens, tied to the mechanism/boundary.

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

Jmgmt Contribution Framing loads about 1.4k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 628 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~87
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

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). 628 words, ~1,445 tokens.

Download SKILL.mdSave it as .claude/skills/jmgmt-contribution-framing/SKILL.md (or your agent's skills folder).
name
jmgmt-contribution-framing
description
Use when turning results into an explicit theoretical contribution for a Journal of Management (JOM) manuscript — the "what new theory do we learn?" statement and the discussion section. Frames the contribution; it does not build the original theory (jmgmt-theory-development) or run the analysis (jmgmt-data-analysis).

Contribution Framing & Discussion (jmgmt-contribution-framing)

When to trigger

  • Results are in but the "so what for theory" is vague or missing
  • The discussion restates findings instead of advancing the theory
  • A reviewer says the contribution is "incremental," "descriptive," or "unclear"
  • You can describe what you found but not what the field now knows that it did not before
  • A meta-analysis reports corrected effects but never says what theory they settle
  • The practical-implications paragraph is generic ("managers should pay attention to X")

The contribution question JOM asks

JOM's gate is the theoretical contribution: what do we now understand about the theory that we did not before? Strong results with no theoretical advance read as a technical report, not a JOM paper — and that is true for empirical papers and for meta-analyses/reviews, which must move theory, not merely tally effects. Because JOM is a general-management journal, the best contributions also travel across subfields: a mechanism uncovered in HR that informs strategy or entrepreneurship is exactly the kind of cross-cutting advance JOM prizes.

Types of theoretical contribution (claim the right one)

Contribution typeWhat it does
Reveal a new mechanismExplains why an established effect occurs
Establish a boundaryShows when an effect holds, weakens, or reverses
Adjudicate competing theoriesShows which of two rival predictions holds, and why
Integrate / bridgeConnects two literatures into a more general account
Challenge an assumptionOverturns a taken-for-granted premise (problematization)
Introduce/refine a constructConceptualizes and validates a construct that earns its keep
Synthesize & redirect(Review/meta) consolidates a literature and sets the agenda

State explicitly which type(s) you claim; do not gesture at "contributing to the literature."

Writing the discussion

A strong JOM discussion typically:

  1. Restates the core finding in theoretical terms (one short paragraph, no number-dump).
  2. Theoretical implications — for each, name the literature, state what changes, and how. This is the heart of the section, and where cross-subfield reach should be made explicit.
  3. Practical implications — specific to the phenomenon and grounded in the findings, never boilerplate.
  4. Limitations — honest, but framed as boundary conditions and future directions, not fatal confessions.
  5. Future research — concrete questions the study opens, tied to the mechanism/boundary.

Match the discussion to the front end: the contributions claimed here must be the ones promised in the introduction. A reviewer will check that the intro's promise and the discussion's delivery line up.

Show full SKILL.md (237 more words)Show less

Contribution framing for a meta-analysis / review

  • A meta-analysis's contribution is the theoretical resolution the pooled evidence provides — which moderator pattern adjudicates which theory — plus the agenda its credibility intervals and heterogeneity reveal.
  • A review's contribution is its organizing framework and forward research agenda; the discussion should make the agenda concrete and theory-anchored, not a generic "more research is needed."

The contribution statement (intro and discussion)

Draft 2–4 explicit contribution sentences and place a version in the introduction and the discussion. Each: "We contribute to [literature] by showing [mechanism/boundary/integration/adjudication], which advances [theory] because [reason]."

Checklist

  • The contribution type is named (mechanism/boundary/adjudication/integration/assumption/construct/synthesis)
  • 2–4 explicit contribution sentences, consistent between intro and discussion
  • Discussion advances theory; cross-subfield reach made explicit where it exists
  • Practical implications specific to the phenomenon and the findings
  • Limitations framed as boundary conditions + future directions
  • Future research follows from the mechanism/boundary, not generic
  • (Meta/review) the theoretical resolution / forward agenda is explicit
  • Intro's promised contribution = discussion's delivered contribution

Anti-patterns

  • Restating results as "contribution": "We found A affects B" is a finding, not a contribution
  • Incremental framing: "we extend prior work to a new setting" with no theoretical change
  • Over-claiming: a paradigm shift a single study cannot support
  • Boilerplate practical implications: "managers should be aware of X," untethered from data
  • Coefficient-census meta-analysis: corrected effects with no theory they settle
  • Intro/discussion mismatch: promising one contribution, delivering another
  • Limitations as confession: flaws listed without converting them into boundary conditions

Output format

【Contribution type(s)】mechanism / boundary / adjudication / integration / assumption / construct / synthesis
【Contribution sentences】1–4 (intro = discussion)
【Theoretical implications】per literature: what changes and why ...
【Cross-subfield reach】who else in management this informs ...
【Practical implications】specific, grounded ...
【Limitations → boundaries】...
【Future research】concrete, mechanism-tied ...
【Intro↔discussion consistency】yes/no
【Next step】jmgmt-tables-figures

© 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 Journal-of-Management-Skills/skills/jmgmt-contribution-framing of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

Jmgmt Contribution Framing 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.

Jmgmt Contribution Framing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Jmgmt Contribution Framing this skillbrycewang-stanford/Awesome-Journal-Skills1.2k—~1.4kAutomated safety check: PassMIT
Exploratory Data Analysisspacering-net/codeg3.9k14 repos~3.6kAutomated safety check: PassMIT
Excel and CSV Data Analysisbytedance/deer-flow84k4 repos~2.2kAutomated safety check: PassMIT
Exploratory Data AnalysisOleafly/Oleafly2122 repos~3.4kAutomated safety check: NotesMIT
Pandas ProJeffallan/claude-skills12k1 repos~1.5kAutomated safety check: PassMIT
Python Executorcortega26/chile-hub1132 repos~1.5kAutomated safety check: PassMIT

Similar skills

  • Exploratory Data Analysis

    spacering-net/codeg

    Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats.

    3.9k GitHub starsUsed in 14 repos~3.6k tokens
    Data & AnalyticsAuto-check passed

  • Excel and CSV Data Analysis

    bytedance/deer-flow

    Analyzes uploaded Excel and CSV files with SQL through DuckDB, producing schema inspections, statistical summaries and exports to CSV, JSON or Markdown.

    84k GitHub starsUsed in 4 repos~2.2k tokens
    Data & AnalyticsAuto-check passed

  • Exploratory Data Analysis

    Oleafly/Oleafly

    Perform bounded, local exploratory analysis of explicitly supported scientific files.

    212 GitHub starsUsed in 2 repos~3.4k tokens
    Data & AnalyticsAuto-check: notes

  • Pandas Pro

    Jeffallan/claude-skills

    Handles pandas DataFrame work: cleaning, merging, groupby aggregation, pivots, time-series resampling and memory tuning, with checks on dtypes, shapes and nulls.

    12k GitHub starsUsed in 1 repo~1.5k tokens
    Data & AnalyticsAuto-check passed

  • Python Executor

    cortega26/chile-hub

    Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh).

    113 GitHub starsUsed in 2 repos~1.5k tokens
    Data & AnalyticsAuto-check passed

  • Agentic Kaggle Workflow

    FrankS-IntelLab/agentic-kaggle-skill

    Takes a Kaggle competition from rules and validation design through baselines, ensembling and notebook architecture to a scored submission.

    188 GitHub stars~4k tokensUpdated 3 mo ago
    Data & AnalyticsAuto-check passed

More from brycewang-stanford/Awesome-Journal-Skills

All 2,387 skills in this repo

  • Aaag Data Analysis

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…

    1.2k GitHub stars~1.3k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Literature Positioning

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…

    1.2k GitHub stars~1.3k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Rebuttal

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…

    1.2k GitHub stars~1.4k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Research Design

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…

    1.2k GitHub stars~1.4k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Review Process

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…

    1.2k GitHub stars~1.3k tokensUpdated 13 days ago
    Auto-check passed

  • Aaag Submission

    brycewang-stanford/Awesome-Journal-Skills

    A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…

    1.2k GitHub stars~1.6k tokensUpdated 13 days ago
    Auto-check passed

Categories

Questions about Jmgmt Contribution Framing

What does Jmgmt Contribution Framing do?

A skill your agent uses when turning results into an explicit theoretical contribution for a Journal of Management (JOM) manuscript — the "what new theory do we learn?" statement and the discussion…. Jmgmt Contribution Framing is an agent skill from brycewang-stanford/Awesome-Journal-Skills." statement and the discussion section.

When should I use Jmgmt Contribution Framing?

Jmgmt Contribution Framing fits situations like: tasks that involve Data analysis.

How do I install Jmgmt Contribution Framing in Claude Code?

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

How do I install Jmgmt Contribution Framing in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jmgmt-contribution-framing -a codex`. Or copy the skill folder (Journal-of-Management-Skills/skills/jmgmt-contribution-framing in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jmgmt-contribution-framing in your project. Codex loads it when a task matches its description.

Can I use Jmgmt Contribution Framing 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 jmgmt-contribution-framing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jmgmt-contribution-framing, .gemini/skills/jmgmt-contribution-framing, .github/skills/jmgmt-contribution-framing and .opencode/skills/jmgmt-contribution-framing in your project.

What does Jmgmt Contribution Framing need to run?

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

Does Jmgmt Contribution Framing 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 Jmgmt Contribution Framing 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 Jmgmt Contribution Framing use?

Jmgmt Contribution Framing 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 Jmgmt Contribution Framing use?

About 1.4k tokens (SKILL.md is roughly 5.8k 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 Jmgmt Contribution Framing?

Skills that share tags, products or a category with Jmgmt Contribution Framing: Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 84k stars), Exploratory Data Analysis (Oleafly/Oleafly, 212 stars) and Pandas Pro (Jeffallan/claude-skills, 12k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jmgmt Contribution Framing?

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.