Trade Hypothesis Ideator
tradermonty/claude-trading-skills
Generate falsifiable trade strategy hypotheses from market data, trade logs, and journal snippets.
A skill your agent uses when the market-data design and microstructure measurement are the bottleneck for a Journal of Financial Markets (JFM) manuscript — TAQ/order-book cleaning, liquidity and…
Install Jfm Empirical Design
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-empirical-design -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfm-empirical-design --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-Financial-Markets-Skills/skills/jfm-empirical-design .claude/skills/jfm-empirical-design && rm -rf skills-srcUse ~/.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/
Install the "jfm-empirical-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design into .claude/skills/jfm-empirical-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfm-empirical-design", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-designType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-empirical-design -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfm-empirical-design --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design .agents/skills/jfm-empirical-design && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "jfm-empirical-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design into .agents/skills/jfm-empirical-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfm-empirical-design", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-empirical-design -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfm-empirical-design --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design .cursor/skills/jfm-empirical-design && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "jfm-empirical-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design into .cursor/skills/jfm-empirical-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfm-empirical-design", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-empirical-design -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfm-empirical-design --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design .gemini/skills/jfm-empirical-design && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "jfm-empirical-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design into .gemini/skills/jfm-empirical-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfm-empirical-design", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfm-empirical-designInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-empirical-design -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design .github/skills/jfm-empirical-design && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "jfm-empirical-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design into .github/skills/jfm-empirical-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfm-empirical-design", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-empirical-design -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills jfm-empirical-design --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design .opencode/skills/jfm-empirical-design && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "jfm-empirical-design" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design into .opencode/skills/jfm-empirical-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jfm-empirical-design", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
jfm-empirical-design- GitHub stars
- 1.2k
- Token cost
- ~2.4k tokens
- SKILL.md length
- 1,154 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when the market-data design and microstructure measurement are the bottleneck for a Journal of Financial Markets (JFM) manuscript — TAQ/order-book cleaning, liquidity and…
- Works in 5 steps: State the universe and filters first.… → Match trades to quotes correctly. State… → Handle the diurnal pattern. Spreads,… → …
- Liquidity and price-impact construction
- SKILL.md covers When to trigger, Microstructure measurement…, Designing the sample and data… and Worked pipeline (illustrative,…, plus 8 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Jfm Empirical Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the market-data design and microstructure measurement are the bottleneck for a Journal of Financial Markets (JFM) manuscript — TAQ/order-book cleaning, liquidity and price-impact construction, sample filters. Hardens measurement; it does not invent evidence or citations.
Its SKILL.md is about 2.4k 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 Research & Science, covering Stock and market analysis and Citation management. 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
- Liquidity and price-impact construction
- Tasks that involve Stock and market analysis
- Tasks that involve Citation management
Example prompts
- “/jfm-empirical-design”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- State the universe and filters first. Asset class, venue(s), period, price/volume screens, and exactly which records are dropped (errors…
- Match trades to quotes correctly. State the timestamp alignment (no look-ahead), the quote-update convention, and the venue consolidation…
- Handle the diurnal pattern. Spreads, depth, and volume are U-shaped intraday; aggregate or control so time-of-day does not contaminate the…
- Validate the chosen measure. Show the headline survives an alternative construct (effective↔realized spread, Amihud↔intraday impact)…
- Lock the data lineage for replication: raw feed → cleaning → analysis file, with a codebook (see jfm-internet-appendix).
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
Jfm Empirical Design loads about 2.4k tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 1,154 words of instructions outside code blocks.
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). 1,154 words, ~2,383 tokens.
Download SKILL.mdSave it as
.claude/skills/jfm-empirical-design/SKILL.md (or your agent's skills folder).- name
- jfm-empirical-design
- description
- Use when the market-data design and microstructure measurement are the bottleneck for a Journal of Financial Markets (JFM) manuscript — TAQ/order-book cleaning, liquidity and price-impact construction, sample filters. Hardens measurement; it does not invent evidence or citations.
Empirical Design & Microstructure Measurement (jfm-empirical-design)
When to trigger
- Liquidity, spread, depth, or price-impact measures are being constructed and the choices are not pinned down
- TAQ / order-book data needs cleaning (trade-quote matching, outlier filters, cancellations) and the rules are ad hoc
- The sample (universe, period, asset class, venue) is chosen without a documented inclusion/exclusion rule
- A daily-frequency liquidity proxy (Amihud, Roll, CRSP-based) is standing in for an intraday claim
- A referee will ask whether the result is an artifact of the measure or the filter, not a real market effect
Microstructure measurement choices that JFM referees scrutinize
JFM is the journal where measurement is the contribution as often as identification is. Insiders know each liquidity construct embeds assumptions; the design must name them.
| Object | Common measures | The trap to disclose |
|---|---|---|
| Spread | quoted, effective, realized; %/cents | effective vs. quoted matters when trades execute inside the quote |
| Depth / quantity | quoted depth, order-book imbalance, Kyle's lambda | depth at touch vs. deeper levels; venue-fragmented depth |
| Price impact | permanent vs. temporary; 5-min realized | horizon choice drives the adverse-selection share |
| Trade direction | Lee-Ready, tick rule, BVC | sign-classification error biases PIN/impact |
| Informed trading | PIN, VPIN, adverse-selection component | PIN estimation is numerically fragile; report it honestly |
| Daily proxies | Amihud illiquidity, Roll, Corwin-Schultz | proxies for high-frequency claims must be validated |
Designing the sample and data pipeline
- State the universe and filters first. Asset class, venue(s), period, price/volume screens, and exactly which records are dropped (errors, openings/closings, halts, locked/crossed quotes). Microstructure results are notoriously filter-sensitive.
- Match trades to quotes correctly. State the timestamp alignment (no look-ahead), the quote-update convention, and the venue consolidation (single venue vs. consolidated tape; lit vs. dark).
- Handle the diurnal pattern. Spreads, depth, and volume are U-shaped intraday; aggregate or control so time-of-day does not contaminate the measure.
- Validate the chosen measure. Show the headline survives an alternative construct (effective↔realized spread, Amihud↔intraday impact) — this belongs partly here and partly in
jfm-robustness. - Lock the data lineage for replication: raw feed → cleaning → analysis file, with a codebook (see
jfm-internet-appendix).
Worked pipeline (illustrative, equity TAQ)
A study of intraday adverse selection on consolidated TAQ: (1) restrict to common shares on primary listings, drop ADRs and ETFs; (2) keep regular-hours trades, drop the opening/closing auctions and the first/last few minutes; (3) remove trades with corrected/cancelled condition codes and crossed/locked quotes; (4) sign trades with Lee-Ready against the prevailing NBBO with a defensible quote-staleness rule; (5) compute effective spread = 2·|p − m| and the 5-minute permanent price impact as the adverse-selection component; (6) aggregate to stock-day, controlling for the intraday U-shape. Every dropped record class and every parameter (staleness, horizon) is logged in the codebook. The headline is then shown to survive switching the impact horizon and the sign-classification rule — the two choices a referee will challenge first.
Asset-class measurement notes
- Equities: consolidated tape vs. single venue changes depth and fragmentation; state which. NBBO timing matters for effective-spread signing.
- Corporate bonds: TRACE is dealer-reported with reporting lags and caps on large-trade sizes; daily/transaction sparsity makes intraday measures impossible — use round-trip cost / imputed roundtrip (Feldhütter) instead of quoted spread.
- Treasuries / FX: decentralized; "the spread" depends on the platform (BrokerTec, EBS); name it.
- Crypto / AMMs: liquidity is the bonding-curve / pool depth, not a quoted spread; map the concept honestly rather than forcing an equity construct.
Execution bridge (StatsPAI / Stata MCP)
Run the asset-pricing battery, don't just specify it. Full map:
execution-with-mcp. JFM is market microstructure and asset pricing — liquidity, price discovery, and cross-sectional return tests where the factor-zoo multiple-testing haircut is salient.
- Factor regressions / time-series alphas:
feolswith the right SEs (Newey–West / clustered) — read the alpha and t off the return, not off a memory of the spec. - Factor-zoo haircut: after disclosing how many signals were screened, apply
romano_wolf/benjamini_hochbergand report the alpha that survives the threshold. - Fama–MacBeth + Shanken EIV correction are Stata-canonical: run them via
mcp__stata-mcp__stata_dowith the vendoredresources/code/(asreg/xtfmb). - Emit exhibits with
etable; hand formatting to the tables/figures skill.
Report the economic magnitude (bps/month alpha, Sharpe gain); the full factor grid and all screened signals go to the appendix. JF execution walkthrough.
Show full SKILL.md (477 more words)Show less
Checklist
- Universe, period, venue, asset class, and every inclusion/exclusion filter are documented
- Trade-quote matching, timestamp alignment, and venue consolidation are stated (no look-ahead)
- Each liquidity/impact measure is named with its construction and its embedded assumption
- The data granularity matches the claim (intraday for impact; not a daily proxy for a high-frequency story)
- The intraday diurnal pattern is handled
- The headline measure is shown robust to at least one alternative construct
- Data sources and any proprietary-feed access path are named for replication
The diurnal pattern is not optional
Intraday spreads, depth, and volume follow a pronounced U-shape — wide and active at the open, tightest midday, widening into the close — and this pattern is strong enough to swamp many effects if ignored. Any design comparing periods, events, or stocks at different times of day must neutralize it: include time-of-bin fixed effects, compare within the same intraday interval across treatment/control, or normalize each observation by its time-of-day average. The classic failure is an event that happens to cluster at the open or close, whose "effect" is really the diurnal level. State explicitly how the U-shape is handled; a microstructure referee will assume it contaminates the result until shown otherwise.
Documenting the sample-construction funnel
Report the sample as a funnel a referee can audit: start from the raw universe, then show each screen and the count it removes (e.g., 9,800 stocks → drop ADRs/ETFs → 6,200 → require ≥250 trading days → 5,400 → drop penny stocks → 5,100). This single table answers the perennial "are the results driven by sample selection?" before it is asked. Pair it with the period, venue, and frequency. Hidden or undocumented filters are the most common silent driver of a fragile microstructure result and the easiest reject to avoid.
Anti-patterns
- Reporting a spread or impact number without saying quoted vs. effective vs. realized, or the horizon
- Undocumented filters that quietly drive the result (the classic JFM reject)
- A daily Amihud/Roll proxy used to make a claim that requires order-book resolution
- Lee-Ready sign classification applied without acknowledging its error rate
- Ignoring venue fragmentation / dark trading when the claim is about market-wide liquidity
- PIN/VPIN reported as if numerically clean when estimation is fragile
Where measurement becomes the contribution
In many JFM papers the contribution is a better measure: a sharper decomposition of the spread into adverse-selection vs. inventory vs. order-processing components, a price-impact estimator robust to microstructure noise, an order-book-based liquidity index, or a way to sign trades when the NBBO is stale. If that is your paper, hold the new measure to a higher standard: show it recovers the right answer in a setting with a known benchmark, show it correlates sensibly with established measures while capturing something they miss, and show the downstream result is not mechanically induced by the construction. A new measure that is only validated by "it gives the result we wanted" will not survive review.
Output format
【Journal】Journal of Financial Markets (JFM)
【Skill】jfm-empirical-design
【Sample】universe / period / venue / asset class / key filters
【Liquidity measure】<spread/depth/impact + construction + assumption>
【Granularity check】matches the claim? <quote/trade/order-book vs. daily>
【Pipeline】trade-quote match + diurnal handling + lineage documented? [Y/N]
【Measure validation】alternative construct agrees? [Y/N → jfm-robustness]
【Source status】verified URL / 待核实 / not asserted
【Next skill】jfm-robustness© 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-Financial-Markets-Skills/skills/jfm-empirical-design of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Jfm Empirical Design 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Jfm Empirical Design this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~2.4k | Automated safety check: Pass | MIT | |
| Trade Hypothesis Ideatortradermonty/claude-trading-skills | 3k | 1 repos | ~693 | Automated safety check: Pass | MIT | |
| Consistency Checkerfranklee16/academic-research-skills | 223 | — | ~2.6k | Automated safety check: Pass | None | |
| Content Research Writerweapp-tailwindcss/weapp-tailwindcss | 1.9k | 25 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Systematic Review ScreenerImbad0202/academic-research-skills | 51k | — | ~8.4k | Automated safety check: Pass | Custom licence | |
| NetworkxzLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~3.2k | Automated safety check: Pass | BSD-3-Clause |
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Questions about Jfm Empirical Design
What does Jfm Empirical Design do?
A skill your agent uses when the market-data design and microstructure measurement are the bottleneck for a Journal of Financial Markets (JFM) manuscript — TAQ/order-book cleaning, liquidity and…. Jfm Empirical Design is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when the market-data design and microstructure measurement are the bottleneck for a Journal of Financial Markets (JFM) manuscript — TAQ/order-book cleaning, liquidity and price-impact construction, sample filters.
When should I use Jfm Empirical Design?
Jfm Empirical Design fits situations like: liquidity and price-impact construction; tasks that involve Stock and market analysis; tasks that involve Citation management.
How do I install Jfm Empirical Design in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-empirical-design -a claude-code`. Or copy the skill folder (Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/jfm-empirical-design in your project. Claude Code loads it when a task matches its description.
How do I install Jfm Empirical Design in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jfm-empirical-design -a codex`. Or copy the skill folder (Journal-of-Financial-Markets-Skills/skills/jfm-empirical-design in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/jfm-empirical-design in your project. Codex loads it when a task matches its description.
Can I use Jfm Empirical Design 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 jfm-empirical-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jfm-empirical-design, .gemini/skills/jfm-empirical-design, .github/skills/jfm-empirical-design and .opencode/skills/jfm-empirical-design in your project.
What does Jfm Empirical Design need to run?
SKILL.md names no scripts, command-line tools or credentials: Jfm Empirical Design is instructions for the agent only.
Does Jfm Empirical Design 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 Jfm Empirical Design 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 Jfm Empirical Design use?
Jfm Empirical Design 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 Jfm Empirical Design use?
About 2.4k tokens (SKILL.md is roughly 9.5k 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 Jfm Empirical Design?
Skills that share tags, products or a category with Jfm Empirical Design: Trade Hypothesis Ideator (tradermonty/claude-trading-skills, 3k stars), Consistency Checker (franklee16/academic-research-skills, 223 stars), Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars) and Systematic Review Screener (Imbad0202/academic-research-skills, 51k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Jfm Empirical Design?
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.