633 skills · sorted by relevance
Find a starting set of community legal skills through an interview about role, practice area, industry, and daily work. Review recommendations, install selected skills, and save preferences for trusted sources, updates, and available integrations.
Create or refine OpenClaw public websites and landing pages using existing site patterns. Improve page structure, navigation, imagery, and mobile layouts, then check themes, accessibility, and search-related presentation in a browser.
Build and update Convex backend code, including schemas, queries, authentication, storage, and scheduled jobs. Apply input and output validation, indexed data access, and deployment checks to verify the implementation.
Assess weaknesses in the software supply chain, from dependency packages to build pipelines and distributed artifacts. Covers authorized proof-of-concept testing, package confusion, container image risks, signing abuse, and defensive checks for tampering.
Plan and configure command and control (C2) systems for red team exercises. Covers major frameworks, communication methods, relay servers, certificate management, and the network or endpoint signals defenders use to detect these systems.
Find and run Refly skills that match a task, then track the workflow through completion. Retrieve generated files or text, or check whether an action finished, using results verified by the Refly command-line tool.
Design partner and affiliate programs around your product, customers, and revenue model. Define partner tiers, commissions, recruitment, onboarding, and revenue tracking, then identify ways to improve participation, resolve sales conflicts, and prioritize integration partnerships.
Build token swap features that move assets between blockchains using the NEAR Intents 1Click API. The skill guides React and Node.js integrations through quotes, deposit transactions, and status checks, with instructions for six blockchain ecosystems.
Upload Kleros evidence, policies, or metadata to IPFS through a paid gateway. Receive a CID, a content-based file identifier, and a viewing URL. Each request uploads one file and costs $0.01 USDC on Base mainnet.
Find and install agent skills for specific tasks, from testing and development to design and documentation. Translate needs into search terms, present relevant options with installation commands, and check or update installed skills.
Inspect public repositories, build artifacts, and container images for software supply chain risks. The skill identifies exposed internal package names, unsafe workflow configurations, and leaked secrets, while requiring build evidence before treating unclaimed package names as exploitable.
Redistricting analysis in R using the redistverse ecosystem. Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio. Covers the complete pipeline: Census and spatial data loading, adjacency graph construction, SMC/MCMC simulation, constraints (population balance, county splits, VRA compliance), convergence diagnostics, plan metrics (compactness, partisan fairness, splits), visualization, summary tables, and interactive plan drawing. Invoke whenever the user mentions redistricting, gerrymandering, district plans, simulation ensembles, or any redistverse package by name.
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Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".
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Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `WeightIt` / `gfoRmula` / `tmle` / `ltmle`, Mendelian randomization via `MendelianRandomization` / `TwoSampleMR` / `MRPRESSO`, KM / Cox / AFT / RMST survival via `survival` / `survminer` / `flexsurv`, E-value sensitivity via `EValue`, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `DoubleML`, S/T/X/R/DR meta-learners via `causalweight` / `grf`, causal forest via `grf::causal_forest`, BART/BCF via `bartCause` / `bcf`, matrix completion via `MCPanel`, CATE distribution + policy tree via `policytree`, off-policy evaluation, conformal causal via `conformalInference` / `cfcausal`, fairness audit via `fairmodels`, DAG learning via `pcalg` / `bnlearn` / LLM-assisted). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".
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React Three Fiber (R3F) and Poimandres ecosystem best practices. Use when writing, reviewing, or optimizing R3F code. Triggers on tasks involving @react-three/fiber, @react-three/drei, zustand, @react-three/postprocessing, @react-three/rapier, or leva.
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Multi-AI research using available external providers (Double Diamond Discover phase) - Questions about best practices, patterns, or ecosystem research PRIORITY TRIGGERS (always invoke): "octo research", "octo discover", "co-research", "co-discover" DO NOT use for: simple file searches (use Read/Grep), questions Claude can answer directly, debugging issues (use skill-debug), or "what are my options" for decision support.
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Use this skill to run a four-panel adversarial advisory review on any pull request that touches the OpenAPM specification artifact (docs/src/content/docs/specs/openapm-*.md), its inline / sidecar JSON Schemas (docs/src/content/docs/specs/schemas/*.schema.json), or the conformance fixture seed (tests/fixtures/spec-conformance/**). The panel fans out to four spec-ecosystem reviewers (swagger-openapi-editor, oci-distribution-editor, pkgmgr-registry-contract-editor, w3c-tag-architect), each running in its own agent thread, and a spec-editor synthesizer that produces a fold-now / defer-v0.1.1 / defer-v0.2 / reject list plus a ship decision keyed off a 1..10 shocked_meter scale. The orchestrator is the sole writer to the PR: ONE consolidated comment, no verdict labels, no merge gating. The panel is advisory -- it surfaces findings, prioritizes folds, and renders a ship recommendation that the maintainer weighs.
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Golang ecosystem watch list — official sources (go.dev/blog, pkg.go.dev, tour.golang.org, golang-nuts), newsletters (Golang Weekly, Awesome Go Newsletter), communities (r/golang, gophers.slack.com, Go Forum, go.dev/wiki), blogs (Dave Cheney, Ardan Labs, Rob Pike), YouTube channels (Gopher Academy, GopherCon EU/UK), conferences, and Go contributors to follow on GitHub, X and Bluesky. Use when seeking Golang learning resources, discovering new libraries or tools, finding community channels or meetups, picking Go people to follow, or keeping up with Go language changes and releases. Not for querying a specific module's versions, docs, or vulnerabilities from the CLI (→ See `samber/cc-skills-golang@golang-pkg-go-dev` skill).
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Golang semantic code intelligence via `gopls`, the official Go language server — go-to-definition, find references, call/implementation hierarchy, workspace symbol search, package API discovery, diagnostics, safe rename, refactors (extract/inline/fill/rewrite code actions), formatting, and generated tests. Reaches an agent via gopls's own MCP server (`go_*` tools), Claude Code's native `LSP` tool, or the `gopls` CLI. Use when navigating or refactoring Go code — jumping to a definition, finding call sites before a rename, understanding a file's or package's dependencies, running diagnostics after an edit, or extracting/inlining/renaming. Not for the published ecosystem — packages not in your `go.mod`, versions, licenses, importers — → See `samber/cc-skills-golang@golang-pkg-go-dev` skill (`godig`). Not for a whole-tree vulnerability audit → See `samber/cc-skills-golang@golang-security` skill (`govulncheck`).
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Idiomatic Golang error handling — creation, wrapping with %w, errors.Is/As, errors.Join, custom error types, sentinel errors, panic/recover, the single handling rule, structured logging with slog, HTTP request logging middleware, and samber/oops for production errors. Built to make logs usable at scale with log aggregation 3rd-party tools. Apply when creating, wrapping, inspecting, or logging errors in Go code. For samber/oops specifics → See `samber/cc-skills-golang@golang-samber-oops` skill; for slog handler ecosystem → See `samber/cc-skills-golang@golang-samber-slog` skill.
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QIIME2 command-line workflow for 16S/ITS amplicon analysis. Alternative to DADA2/phyloseq R workflow with built-in provenance tracking. Use when preferring CLI over R, needing reproducible provenance, or working within QIIME2 ecosystem.
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Map the full ecosystem of touchpoints, channels, and relationships across a service. Use when the experience spans more than one product. For one persona's linear journey use `journey-map` (design-research); for backstage operations use `service-blueprint`.
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Map one persona's end-to-end experience with stages, touchpoints, emotions, and pain points. Use when improving an existing experience. For the multi-channel ecosystem use `experience-map` (ux-strategy); for screen-level paths use `user-flow-diagram` (prototyping-testing).
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Semantic topic cluster planning and automated execution engine for claude-blog. Performs SERP-based keyword research, groups keywords by search intent and SERP overlap, builds a hub-and-spoke cluster architecture, generates an interactive SVG cluster map, and executes the full cluster by orchestrating blog-write calls with shared cluster context and automatic internal-link injection. Fills the strategy-to-execution gap: blog-strategy plans the blueprint, blog-cluster builds the house. Use when user says "blog cluster", "topic cluster", "content cluster", "cluster plan", "cluster execute", "pillar content", "hub and spoke", "content ecosystem", "cluster map".
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