Agent skill

Amanthro Data Analysis

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

A skill your agent uses when executing and reporting the analysis/interpretation for an American Anthropologist (AA) manuscript so it survives expert anonymous review — disciplined…

MITAuto-check passedData & Analytics

Install Amanthro Data Analysis

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill amanthro-data-analysis -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills amanthro-data-analysis --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/American-Anthropologist-Skills/skills/amanthro-data-analysis .claude/skills/amanthro-data-analysis && 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
amanthro-data-analysis
GitHub stars
1.2k
Token cost
~1.9k 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 executing and reporting the analysis/interpretation for an American Anthropologist (AA) manuscript so it survives expert anonymous review — disciplined…

  • Works in 6 steps: Show the evidence behind the claim.… → Pursue the alternative reading. Name the… → Disconfirming evidence. Include cases… → …
  • Honest interpretation
  • SKILL.md covers When to trigger, Analysis & interpretation…, Mixed-methods / computational… and AA four-field…, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Amanthro Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when executing and reporting the analysis/interpretation for an American Anthropologist (AA) manuscript so it survives expert anonymous review — disciplined ethnographic/qualitative inference, honest interpretation, and (for biological/archaeological work) sound quantitative analysis. Guides analysis and interpretation norms; it does not fabricate evidence or quotations.

Its SKILL.md is about 1.9k 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

  • Honest interpretation
  • (for biological/archaeological work) sound quantitative analysis

Example prompts

  • “/amanthro-data-analysis”

Workflow steps

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

  1. Show the evidence behind the claim. Ground each interpretive claim in specific, quoted, or
  2. Pursue the alternative reading. Name the strongest rival interpretation and show why the
  3. Disconfirming evidence. Include cases that complicate the argument rather than curating only
  4. Quote and represent fairly. Translate and contextualize interlocutors' words; do not flatten
  5. For quantitative subfields (biological/archaeological): report effect sizes and uncertainty, not
  6. Reflexivity in the analysis. Note how your position shaped which evidence was available and how

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

Amanthro Data Analysis loads about 1.9k tokens when it runs. Until then it costs about 100 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
~100
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

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, ~1,871 tokens.

Download SKILL.mdSave it as .claude/skills/amanthro-data-analysis/SKILL.md (or your agent's skills folder).
name
amanthro-data-analysis
description
Use when executing and reporting the analysis/interpretation for an American Anthropologist (AA) manuscript so it survives expert anonymous review — disciplined ethnographic/qualitative inference, honest interpretation, and (for biological/archaeological work) sound quantitative analysis. Guides analysis and interpretation norms; it does not fabricate evidence or quotations.

Analysis & Interpretation (amanthro-data-analysis)

AA reviewers are expert and read across four fields, so the same results section may be read by an ethnographer, an archaeologist, and a bioanthropologist at once. At AA, interpretation is analysis: qualitative inference is held to a real standard, not waved through as "context." This skill covers execution and reporting norms; design decisions live in amanthro-research-design.

When to trigger

  • Building the results/interpretation section from fieldnotes, transcripts, material, or measurements
  • A reviewer asked for more evidence, alternative readings, or robustness
  • Reconciling what interlocutors say with what they do, or pre-planned vs. emergent themes
  • Making the analysis transparent enough to be credible without exposing protected sources

Analysis & interpretation norms AA expects

  1. Show the evidence behind the claim. Ground each interpretive claim in specific, quoted, or described evidence (a fieldnote moment, an utterance, an artifact, a measurement) — not assertion.
  2. Pursue the alternative reading. Name the strongest rival interpretation and show why the evidence favors yours; report where it doesn't fit cleanly. AA values honesty about ambiguity.
  3. Disconfirming evidence. Include cases that complicate the argument rather than curating only confirming material; say what you learned from the friction.
  4. Quote and represent fairly. Translate and contextualize interlocutors' words; do not flatten them into illustrations of your theory. Attribute analytics to communities where they originate.
  5. For quantitative subfields (biological/archaeological): report effect sizes and uncertainty, not just significance; pre-specify where possible; correct for multiple comparisons; validate measures; avoid conflating ancestry with social race.
  6. Reflexivity in the analysis. Note how your position shaped which evidence was available and how you read it.

Mixed-methods / computational specifics

  • If you code text or use computational tools on field/archival data, document the procedure, validate against close reading, and treat outputs as interpretable evidence, not ground truth.
  • Keep the qualitative core legible: a table or model never replaces the interpretive argument at AA.

AA four-field evidence-and-care ledger

Before writing the results section, build an AA ledger that shows how the analysis earns trust across anonymous four-field review while protecting the people, places, materials, and communities that make the evidence possible.

Claim typeEvidence burdenAA-specific check
Ethnographic interpretationfieldnote moment, situated quote, practice, or interactionalternative reading named; interlocutor dignity preserved
Archaeological/material claimartifact/context record, stratigraphy, archive, or material tracesource limits and silences stated, not hidden
Biological/quantitative claimmeasure validity, uncertainty, comparison set, effect sizeancestry, population, and social categories not conflated
Linguistic/semiotic claimtranscript, translation, interactional sequence, or form-function linktranscript supports the indexical/social claim
Public or multimodal claimimage, sound, exhibit, media artifact, or public-facing evidenceconsent, caption, and analytic function are explicit
Community/accountability claimcollaborative interpretation, feedback, or provenance trailanalytics attributed; extractive phrasing removed

Use the ledger to decide what can be shown in the manuscript, what belongs in protected notes, and what must remain undisclosed for ethical reasons. The goal is not maximal transparency; it is accountable evidence that an AA reviewer can audit without compromising participants or collections.

Show full SKILL.md (295 more words)Show less

Transparency while you work (not at the end)

  • Maintain an audit trail linking claims to sources (evidence tables, coded excerpts) so the argument is checkable without de-anonymizing protected interlocutors.
  • For quantitative work: set/report seeds, pin software versions, keep exhibit numbers matched to outputs (see amanthro-transparency-and-data).

Anti-patterns

  • Cherry-picked quotes that illustrate the thesis with no disconfirming cases
  • Interpretation asserted with no grounding evidence ("informants felt…")
  • Treating qualitative evidence as merely decorative around a "real" quantitative result (or the reverse)
  • Stars-only tables with no effect sizes/intervals (biological/archaeological work)
  • Extracting community analytics without attribution; speaking for rather than with interlocutors
  • Numbers or quotations the underlying record cannot support

Output format

【Main claim】the interpretive/empirical result
【Evidence】what grounds it (fieldnote / utterance / material / measure)
【Four-field mode】ethnographic / material / biological-quant / linguistic / multimodal / community-accountability
【Alternative reading】named and adjudicated?
【Disconfirming evidence】included and addressed? [Y/N]
【Quant rigor】(if applicable) effect size + uncertainty + validation
【Care check】privacy, consent/provenance, attribution, and dignity protected? [Y/N]
【Reflexivity】position's effect on the analysis noted? [Y/N]
【Next】amanthro-tables-figures

What AA reviewers probe, by mode

ModeThe check a referee runs firstThe fix that earns trust
Ethnographic / interpretiveIs the claim grounded in shown evidence, or asserted?Quote/describe the moment; name the rival reading
Archival / materialAre the silences and biases of the source read, not ignored?Read against the grain; state what the archive cannot show
Biological / quantitativeIs uncertainty honest and "race" handled carefully?Effect sizes + intervals; ancestry ≠ social race; validated measures
LinguisticDoes the transcript support the indexical claim?Show the discourse data; tie form to social meaning
MultimodalDoes the media argue, or merely illustrate?Make the image/sound carry analytic weight, with consent

Calibration anchors (hedged)

  • The bar is anthropological significance with honest interpretation, not within-subfield novelty or statistical decoration; a rich case with a thin argument rarely clears AA review.
  • AA practices methodological pluralism and an ethics of care — match the inference standard to the mode, and never trade interlocutors' dignity for a sharper claim.
  • Specific transparency mechanics can change — confirm against the journal's current guidelines.

Supplementary resources

© 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 American-Anthropologist-Skills/skills/amanthro-data-analysis of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Questions about Amanthro Data Analysis

What does Amanthro Data Analysis do?

A skill your agent uses when executing and reporting the analysis/interpretation for an American Anthropologist (AA) manuscript so it survives expert anonymous review — disciplined…. Amanthro Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when executing and reporting the analysis/interpretation for an American Anthropologist (AA) manuscript so it survives expert anonymous review — disciplined ethnographic/qualitative inference, honest interpretation, and (for biological/archaeological work) sound quantitative analysis.

When should I use Amanthro Data Analysis?

Amanthro Data Analysis fits situations like: honest interpretation; (for biological/archaeological work) sound quantitative analysis.

How do I install Amanthro Data Analysis in Claude Code?

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

How do I install Amanthro Data Analysis in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill amanthro-data-analysis -a codex`. Or copy the skill folder (American-Anthropologist-Skills/skills/amanthro-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/amanthro-data-analysis in your project. Codex loads it when a task matches its description.

Can I use Amanthro Data Analysis 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 amanthro-data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/amanthro-data-analysis, .gemini/skills/amanthro-data-analysis, .github/skills/amanthro-data-analysis and .opencode/skills/amanthro-data-analysis in your project.

What does Amanthro Data Analysis need to run?

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

Does Amanthro Data Analysis 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 Amanthro Data Analysis 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 Amanthro Data Analysis use?

Amanthro Data Analysis 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 Amanthro Data Analysis use?

About 1.9k tokens (SKILL.md is roughly 7.5k 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 Amanthro Data Analysis?

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Who maintains Amanthro Data Analysis?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 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.