Agent skill

Smr Derivation And Properties

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

A skill your agent uses when stating assumptions, identification, and analytical properties (bias, consistency, efficiency, asymptotics, validity conditions) of a method in a Sociological Methods &…

MITAuto-check passed

Install Smr Derivation And Properties

skills CLI
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill smr-derivation-and-properties -a claude-code

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills smr-derivation-and-properties --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/Sociological-Methods-and-Research-Skills/skills/smr-derivation-and-properties .claude/skills/smr-derivation-and-properties && 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
smr-derivation-and-properties
GitHub stars
1.2k
Token cost
~1.2k tokens
SKILL.md length
566 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when stating assumptions, identification, and analytical properties (bias, consistency, efficiency, asymptotics, validity conditions) of a method in a Sociological Methods &…

  • Works in 5 steps: Target / estimand — the population… → Assumptions — each one labeled by the… → Estimator / statistic — the exact object… → …
  • Stating assumptions
  • SKILL.md covers The traceable chain, Assumption ledger, Property claims at SMR (what… and Proof economy for a…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Smr Derivation And Properties is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when stating assumptions, identification, and analytical properties (bias, consistency, efficiency, asymptotics, validity conditions) of a method in a Sociological Methods & Research (SMR) paper. Audits the theory and proof economy; does not design the Monte Carlo or the real-data illustration.

Its SKILL.md is about 1.2k 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

  • Stating assumptions
  • Analytical properties (bias
  • Validity conditions) of a method in a Sociological Methods & Research (SMR) paper

Example prompts

  • “/smr-derivation-and-properties”

Workflow steps

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

  1. Target / estimand — the population quantity or hypothesis the method addresses.
  2. Assumptions — each one labeled by the role it plays (existence, identification, consistency,
  3. Estimator / statistic — the exact object computed from data.
  4. Properties — bias (finite-sample and asymptotic), consistency conditions, efficiency relative
  5. Failure boundary — where the assumptions fail and what happens to the property there.

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

Smr Derivation And Properties loads about 1.2k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 566 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~82
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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). 566 words, ~1,204 tokens.

Download SKILL.mdSave it as .claude/skills/smr-derivation-and-properties/SKILL.md (or your agent's skills folder).
name
smr-derivation-and-properties
description
Use when stating assumptions, identification, and analytical properties (bias, consistency, efficiency, asymptotics, validity conditions) of a method in a Sociological Methods & Research (SMR) paper. Audits the theory and proof economy; does not design the Monte Carlo or the real-data illustration.

SMR Derivation and Properties

Use this for theory integrity. SMR is a methods journal: a property that is asserted but neither derived nor argued is the most common reviewer wound. You do not need Econometrica-level generality, but every claim about what the method does must be traceable from stated assumptions.

The traceable chain

A reader should be able to follow, in order:

  1. Target / estimand — the population quantity or hypothesis the method addresses.
  2. Assumptions — each one labeled by the role it plays (existence, identification, consistency, asymptotic normality, finite-sample approximation, computation).
  3. Estimator / statistic — the exact object computed from data.
  4. Properties — bias (finite-sample and asymptotic), consistency conditions, efficiency relative to the incumbent, the variance estimator, and the regime where it holds.
  5. Failure boundary — where the assumptions fail and what happens to the property there.

If any link is missing, that link is what the report will quote back.

Assumption ledger

Build this before rewriting the theory section:

text
Assumption | Role | Where used | Empirical/sim check | If weakened

Use it to (a) delete decorative assumptions, (b) expose missing ones, and (c) tie each assumption to something a sociologist can recognize in real data. SMR readers are applied methodologists: a condition stated only for proof convenience must say whether it can be relaxed and whether the simulation probes its boundary.

Property claims at SMR (what reviewers expect)

  • Consistency / unbiasedness: state the conditions, not just the conclusion. "Consistent under MAR and correct outcome model" is a claim; "performs well" is not.
  • Efficiency: relative to what? Name the comparison estimator and the regime.
  • Inference validity: give the variance estimator and the conditions under which its coverage is nominal; SMR papers routinely live or die on coverage, not point estimates.
  • Robustness: be explicit about what the method is and is not robust to (e.g., doubly robust to one of two models, not to both failing).
Show full SKILL.md (264 more words)Show less

Proof economy for a methods-journal audience

  • Keep the main argument legible in the body; route long algebra to an appendix, but never hide a load-bearing step there with only "it can be shown."
  • Match the rigor to the claim: a closed-form bias correction needs a derivation; an evaluation paper needs a clear analytical reason the failure occurs, not a theorem for its own sake.
  • Separate theorem (proved), result (derived under stated conditions), and finding (observed in simulation). Label them so a reviewer never has to guess the evidentiary status.

Pair every property with a finite-sample check

Each analytical property should name the simulation exhibit that demonstrates it at realistic sample sizes — SMR treats an unpaired asymptotic claim as unfinished. Hand the boundary cases to smr-simulation-studies so the Monte Carlo stresses exactly the assumption most likely to fail.

Checklist

  • Estimand, assumptions, estimator, and properties are stated in that order before derivations.
  • Each assumption is labeled by role and tied to a recognizable data feature.
  • Consistency/efficiency/inference claims state conditions and the comparison method.
  • The variance estimator and its coverage conditions are given.
  • The failure boundary is stated, not hidden.
  • Each property names the simulation exhibit that checks it in finite samples.
  • Proved / derived / simulated claims are labeled distinctly.

Anti-patterns

  • Asserted properties: "our estimator is consistent and efficient" with no conditions or proof.
  • Decorative assumptions: regularity conditions never used or never tied to data.
  • Hidden load-bearing steps: a key derivation replaced by "it can be shown."
  • Evidence laundering: simulation regularities phrased as theorems.
  • Coverage silence: a new estimator with no variance estimator or coverage argument.

Output format

text
[Theory status] defensible / needs repair / not ready
[Estimand] <population quantity or hypothesis>
[Critical assumptions] <assumption -> role>
[Properties claimed] <bias / consistency / efficiency / coverage, with conditions>
[Property gaps] <missing condition, variance estimator, or failure boundary>
[Next SMR skill] smr-simulation-studies

© 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 Sociological-Methods-and-Research-Skills/skills/smr-derivation-and-properties of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

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CSS At Propertythedaviddias/Front-End-Checklist74k—~602Automated safety check: PassMIT

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Questions about Smr Derivation And Properties

What does Smr Derivation And Properties do?

A skill your agent uses when stating assumptions, identification, and analytical properties (bias, consistency, efficiency, asymptotics, validity conditions) of a method in a Sociological Methods &…. Smr Derivation And Properties is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when stating assumptions, identification, and analytical properties (bias, consistency, efficiency, asymptotics, validity conditions) of a method in a Sociological Methods & Research (SMR) paper.

When should I use Smr Derivation And Properties?

Smr Derivation And Properties fits situations like: stating assumptions; analytical properties (bias; validity conditions) of a method in a Sociological Methods & Research (SMR) paper.

How do I install Smr Derivation And Properties in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill smr-derivation-and-properties -a claude-code`. Or copy the skill folder (Sociological-Methods-and-Research-Skills/skills/smr-derivation-and-properties in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/smr-derivation-and-properties in your project. Claude Code loads it when a task matches its description.

How do I install Smr Derivation And Properties in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill smr-derivation-and-properties -a codex`. Or copy the skill folder (Sociological-Methods-and-Research-Skills/skills/smr-derivation-and-properties in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/smr-derivation-and-properties in your project. Codex loads it when a task matches its description.

Can I use Smr Derivation And Properties 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 smr-derivation-and-properties -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/smr-derivation-and-properties, .gemini/skills/smr-derivation-and-properties, .github/skills/smr-derivation-and-properties and .opencode/skills/smr-derivation-and-properties in your project.

What does Smr Derivation And Properties need to run?

SKILL.md names no scripts, command-line tools or credentials: Smr Derivation And Properties is instructions for the agent only.

Does Smr Derivation And Properties 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 Smr Derivation And Properties 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 Smr Derivation And Properties use?

Smr Derivation And Properties 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 Smr Derivation And Properties use?

About 1.2k tokens (SKILL.md is roughly 4.8k 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 Smr Derivation And Properties?

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Who maintains Smr Derivation And Properties?

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.