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Agent skill

Rss Experiments

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

A skill your agent uses when designing or auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments, trial protocols and per-condition…

MITAuto-check passed

Install Rss Experiments

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

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

GitHub CLI
$ gh skill install brycewang-stanford/Awesome-Journal-Skills rss-experiments --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/RSS-Skills/skills/rss-experiments .claude/skills/rss-experiments && 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
rss-experiments
GitHub stars
1.2k
Token cost
~1.5k tokens
SKILL.md length
697 words
Files
1
Skills in repo
2,387
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when designing or auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments, trial protocols and per-condition…

  • Works in 3 steps: Define 4-6 mutually exclusive failure… → Label every failed trial at collection… → Report the distribution per condition; a…
  • Auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments
  • SKILL.md covers Start from the claim, derive…, Hardware trial protocol, Simulation and hardware: split… and Failure attribution, the RSS…, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Rss Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments, trial protocols and per-condition counts, mechanism-isolating ablations, simulation-versus-hardware evidence splits, and failure attribution that supports a scientific claim rather than a demo reel.

Its SKILL.md is about 1.5k 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

  • Auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments
  • Trial protocols and per-condition counts
  • Mechanism-isolating ablations
  • Simulation-versus-hardware evidence splits

Example prompts

  • “/rss-experiments”

Workflow steps

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

  1. Define 4-6 mutually exclusive failure categories before the campaign (perception,
  2. Label every failed trial at collection time, from logs — retrospective labeling
  3. Report the distribution per condition; a method that shifts failure mass between

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

Rss Experiments loads about 1.5k tokens when it runs. Until then it costs about 90 tokens; SKILL.md has 697 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~90
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

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). 697 words, ~1,489 tokens.

Download SKILL.mdSave it as .claude/skills/rss-experiments/SKILL.md (or your agent's skills folder).
name
rss-experiments
description
Use when designing or auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments, trial protocols and per-condition counts, mechanism-isolating ablations, simulation-versus-hardware evidence splits, and failure attribution that supports a scientific claim rather than a demo reel.

RSS Experiments

Design experiments that test the paper's claim, not experiments that showcase the system. At RSS the evaluation section is where the scientific claim either becomes falsifiable or is exposed as marketing.

Start from the claim, derive the conditions

Write the claim, then derive what evidence its logical form demands:

Claim formEvidence the form demands
"X causes the improvement"Ablation removing only X; everything else frozen
"the gain transfers"Held-out tasks/objects/platforms named before running
"method M has property P"The condition where P would break, deliberately tested
"T is the bottleneck"Failure attribution showing T dominates the failure mass
"faster/cheaper at equal quality"Matched-quality comparison, not best-vs-default

An experiment matrix that cannot be traced back to a claim row is footage, not evidence — cut it or move it to the supplement.

Hardware trial protocol

  • Pre-register internally: success criterion, reset procedure, object/task distribution, and stopping rule written down before the campaign. Post-hoc criteria are how demo bias enters honest labs.
  • Per-condition counts in every table. "n = 25 per object, 6 objects" is auditable; "extensive trials" is not.
  • Report the denominator. All attempts count — aborted runs, resets, operator interventions. A human silently rescuing the robot between trials is part of the system and must appear in the protocol.
  • Small-n honesty: hardware budgets cap trials, so use interval estimates suited to small samples and let the language match their width. Twenty trials support "in our setting"; they do not support "reliably in general."

Simulation and hardware: split the ledger

  • Label every number sim or real; never average across the boundary.
  • Simulation earns its place by scale (sweeps, distributions) or danger (failure regimes hardware cannot safely visit); hardware earns the claim's headline.
  • If the claim mentions the physical world, at least one decisive result must be physical. A sim-only paper must scope its claim to simulation explicitly.
  • When sim and real disagree, that gap is a finding — report it, do not tune it away silently.

Failure attribution, the RSS signature move

A distribution over failure causes is often more scientifically valuable than the success rate above it. Build one:

  1. Define 4-6 mutually exclusive failure categories before the campaign (perception, planning, timing, slip, hardware fault, other).
  2. Label every failed trial at collection time, from logs — retrospective labeling from memory drifts optimistic.
  3. Report the distribution per condition; a method that shifts failure mass between categories is telling you how it works.
Show full SKILL.md (297 more words)Show less

Trial-budget arithmetic (do this before the campaign)

Hardware time is the binding constraint, so budget it explicitly:

text
conditions:      2 methods x 3 environments x (1 ablation + 1 transfer) = 10
trials/condition: 25            -> 250 attempts
minutes/attempt (incl. reset):   6 -> 25 robot-hours
overhead (faults, recalibration, re-runs): x1.5 -> ~38 robot-hours
robot access: 4 h/day           -> ~10 working days, before any surprise

If the arithmetic says the matrix cannot finish before the January freeze, shrink the matrix, not the per-condition counts — fewer conditions with auditable n beat a full grid of anecdotes. Reserve the final week for zero data collection.

Reporting floor

Non-negotiables for the results section, independent of subfield:

  • Trials per condition and total attempts, including discards, with discard rules.
  • Success criterion stated operationally (what sensor reading / judge decides).
  • Dispersion for every stochastic number — interval, quartiles, or all raw points when n is small; never a bare mean.
  • Failure distribution table matched to the labeled categories.
  • Wall-clock and compute for learned components; trial duration for hardware.
  • Which numbers are sim and which are real, in the table itself, not the caption.

Baseline fairness

  • Give baselines the same tuning budget, sensor stream, and reset quality as the proposed method, and say so in one sentence.
  • Prefer the strongest published configuration over a reimplementation you cannot make fast; if reimplementing, report your version's score on a setting where the original published numbers exist.

Vignette: turning a demo matrix into a claim test

A draft evaluates a new whole-body controller on eight household tasks and reports success rates — a demo matrix. The claim, once written down, is "torque-limit awareness, not trajectory optimality, drives the reliability gain." The redesign: (1) an ablation running the same controller with torque-awareness disabled, same eight tasks; (2) a stress condition where torque limits bind hard (heavy objects) versus one where they never bind — the claim predicts the gain concentrates in the first; (3) failure attribution distinguishing torque-saturation failures from tracking failures. Three conditions replaced five decorative tasks, and the paper gained a falsifiable spine without new hardware.

Output format

text
[Claim -> condition map] <each claim row -> experiment>
[Protocol status] pre-registered / drifting / post-hoc
[Counts] <per-condition n, denominators, interventions>
[Sim/real ledger] clean split / mixed (fix)
[Failure attribution] <categories + dominant mass>
[Decisive missing run] <the one experiment to add next>

© 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 RSS-Skills/skills/rss-experiments of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

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Questions about Rss Experiments

What does Rss Experiments do?

A skill your agent uses when designing or auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments, trial protocols and per-condition…. Rss Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments, trial protocols and per-condition counts, mechanism-isolating ablations, simulation-versus-hardware evidence splits, and failure attribution that supports a scientific claim rather than a demo reel.

When should I use Rss Experiments?

Rss Experiments fits situations like: auditing the experimental campaign for an RSS (Robotics: Science and Systems) paper — hypothesis-shaped robot experiments; trial protocols and per-condition counts; mechanism-isolating ablations; simulation-versus-hardware evidence splits.

How do I install Rss Experiments in Claude Code?

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

How do I install Rss Experiments in Codex?

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

Can I use Rss Experiments 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 rss-experiments -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rss-experiments, .gemini/skills/rss-experiments, .github/skills/rss-experiments and .opencode/skills/rss-experiments in your project.

What does Rss Experiments need to run?

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

Does Rss Experiments 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 Rss Experiments 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 Rss Experiments use?

Rss Experiments 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 Rss Experiments use?

About 1.5k tokens (SKILL.md is roughly 6k 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 Rss Experiments?

Skills that share tags, products or a category with Rss Experiments: Google SEO APIs (AgriciDaniel/claude-seo, 18k stars), Analytics (Nexus-JPF/note-companion, 869 stars), GEO Monthly Delta Report (zubair-trabzada/geo-seo-claude, 11k stars) and Paid Ads Audit (AgriciDaniel/claude-ads, 9.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Rss Experiments?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,216 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.