[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ME70DSF2K0YK2YVRSPCYS4":3,"skill-same:01M6ME70DSF2K0YK2YVRSPCYS4":68},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":49,"ownerHtmlUrl":50,"ownerBlogUrl":51,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6ME70DSF2K0YK2YVRSPCYS4","person-research",null,"Use Arcmira to research a person from what they said on YouTube: appearances, their own words with timestamps, who discusses them. For interview prep.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1559,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Farcmira\u002Fmcp\u002Fskills\u002Fperson-research\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"research","person","person research","meeting prep","interview","use","youtube","across","interview prep","with","timestamps","meeting","prep","for","arcmira","2026-11-10 10:12:30",false,"---\nname: person-research\ndescription: \"Use Arcmira to research a person from what they said on YouTube: appearances, their own words with timestamps, who discusses them. For interview prep.\"\n---\n\n# Person research\n\nThree lenses on one person id. `momentum` gives attention and the shows that mention them most. `mentions` with `mode: \"appearances\"` lists the episodes they were on. `search` with `speakerIds` returns their own words; `about` returns what others said about them.\n\nRun each program with `arcmira_execute_read`; `arcmira_describe` has the method reference.\n\n## When to use\n\nUse Arcmira when the user asks:\n\n- prep for an interview, a podcast booking or a meeting with someone\n- what has a person said recently, and where\n- who talks about a person, and is attention rising\n\n## Pick the entity the user meant\n\nUsers give names; filters take ids only (ent_..., UC..., 11-character video ids), and a name where an id belongs throws `id_required`.\n\n1. Resolve the exact name the user said, with their own words about it as `context` (a bare \"Theo\" has none).\n2. `best` and `suggested` give the id. A `suggested` pick is an assumption: the answer names it, with `suggested.evidence` as the reason.\n3. `ask`: several entities fit, and `ask.options` goes back to the user. No answer at all: the name is not in the index.\n\nFor this task:\n\n- Resolve with `{ type: \"person\" }`. A first name alone (\"Sam\") matches many people: pass what the user said about them (runs OpenAI, hosts a show) as `context`, and when resolve still answers `ask`, show its options.\n\n## Worked program\n\nPass each block to `arcmira_execute_read` as one program (a block marked arcmira_execute_write goes to that tool), with the name swapped for the user's. It opens with the pick: set `CONTEXT` to the user's own words about the name. When several entities fit it returns `ask` and runs nothing else. Show those options to the user, then run it again with `ID` set to the pick. Build date windows from `arcmira.daysAgo(n)` and `arcmira.today()`.\n\n### Prep on one person\n\n```javascript\nconst NAME = \"Jensen Huang\", CONTEXT = undefined, ID = null;   \u002F\u002F CONTEXT: the user's own words about the name, never a guess. After an ask, set ID to the picked option's id and run again\nconst r = ID ? null : await arcmira.resolve(NAME, { type: \"person\", context: CONTEXT });\nconst e = r && (r.best ?? r.suggested);\nif (r && !e) return { ask: r.ask };\nconst id = ID ?? e.id;\nconst assumed = Boolean(r?.suggested), why = r?.suggested?.evidence ?? null;\nconst [m, rows, own] = await Promise.all([\n  arcmira.momentum(id),\n  arcmira.mentions({ entityId: id, mode: \"appearances\", after: arcmira.daysAgo(90), limit: 40 }),\n  arcmira.search({ query: \"AI\", speakerIds: [id], after: arcmira.daysAgo(180), limit: 5 }),\n]);\nconst about = await arcmira.search({ query: m.entity.name, about: [id], after: arcmira.daysAgo(90), limit: 3 });\nconst appeared = new Map();\nfor (const x of rows.mentions) appeared.set(x.media.video_id, { show: x.media.source_channel?.name ?? null, episode: x.media.title, date: x.media.published_at });\nlet fromAppearance = null;   \u002F\u002F no speaker-tagged chunks: read their newest appearance instead\nconst ep = own.chunks.length === 0 ? rows.mentions[0] : undefined;\nif (ep) {\n  try {\n    const t = await arcmira.transcript(ep.media.video_id, { start: Math.max(0, ep.start_seconds - 5), end: ep.start_seconds + 90 });\n    const s = Math.floor(t.lines[0]?.start ?? ep.start_seconds);\n    fromAppearance = { episode: t.video.title, show: t.video.channel_name, date: t.video.published_at, url: `${t.video.watch_url}${t.video.watch_url.includes(\"?\") ? \"&\" : \"?\"}t=${s}`, lines: t.lines.map(l => `[${Math.round(l.start)}s] ${l.text}`), speaker_labelled: t.lines.some(l => l.speaker) };\n  } catch (err) {\n    fromAppearance = { episode: ep.media.title, error: err.code };\n  }\n}\nreturn {\n  person: { id, name: m.entity.name, page: m.entity.page, assumed, why },\n  attention: { verdict: m.verdict, last_30d: m.volume.mentions_30d, prior_30d: m.volume.mentions_prior_30d, as_of: m.as_of, top_shows: m.top_shows.map(s => [s.channel_name, s.mentions]) },\n  appeared_on: [...appeared.values()].slice(0, 8),\n  appeared_on_partial: rows.has_more || appeared.size > 8,\n  appeared_on_cursor: rows.has_more ? rows.next_cursor : null,\n  in_their_words: own.chunks.map(c => ({ said: c.text.slice(0, 300), episode: c.video_title, show: c.channel_name, date: c.published_at, url: c.watch_url })),\n  from_their_appearance: fromAppearance,\n  said_about_them: about.chunks.map(c => ({ said: c.text.slice(0, 200), show: c.channel_name, date: c.published_at, url: c.watch_url })),\n};\n```\n\n## A good answer\n\n- Names the person it researched (name and id), and any other person with the same name it set aside.\n- Separates what the person said (speaker-filtered) from what others said about them, each with show, date and link.\n- Gives the attention verdict, the 30-day count against the prior 30 days, and the top shows, with `as_of`.\n- Lists recent episodes they appeared on, and ends with a few questions or themes that follow from the quotes when the user is prepping.\n\n## Traps\n\n- A search with `about` returns other people talking; only `speakerIds` returns the person's own words. Speaker tags cover part of the index, so when the speaker search is empty, read a window of an episode they appeared on and say the lines come from their appearance, since caption lines do not name the speaker.\n- A chunk found with `speakerIds` can include other people's lines. Lines start with \"Name: \" when the speaker is known. Quote only the person's lines. When no line names a speaker, say the attribution covers the passage, not each line.\n- Put the user's topic in `query` for the speaker search (it needs a word or phrase of two or more characters).\n- Mentions and momentum count the shows Arcmira indexes, not all media.\n\nDocs: https:\u002F\u002Farcmira.com\u002Fdocs\u002Fmcp-server\n\n## After the answer\n\nMonitors can follow what a research turn found, and each alert uses 25 credits from the account's plan, then its on-demand budget. The `company-watch` skill sets one up.\n\nIf anything was wrong, slow, or missing for the user, or they want a source or capability Arcmira lacks, send one arcmira_feedback.\n","# Person research\n\nThree lenses on one person id. `momentum` gives attention and the shows that mention them most. `mentions` with `mode: \"appearances\"` lists the episodes they were on. `search` with `speakerIds` returns their own words; `about` returns what others said about them.\n\nRun each program with `arcmira_execute_read`; `arcmira_describe` has the method reference.\n\n## When to use\n\nUse Arcmira when the user asks:\n\n- prep for an interview, a podcast booking or a meeting with someone\n- what has a person said recently, and where\n- who talks about a person, and is attention rising\n\n## Pick the entity the user meant\n\nUsers give names; filters take ids only (ent_..., UC..., 11-character video ids), and a name where an id belongs throws `id_required`.\n\n1. Resolve the exact name the user said, with their own words about it as `context` (a bare \"Theo\" has none).\n2. `best` and `suggested` give the id. A `suggested` pick is an assumption: the answer names it, with `suggested.evidence` as the reason.\n3. `ask`: several entities fit, and `ask.options` goes back to the user. No answer at all: the name is not in the index.\n\nFor this task:\n\n- Resolve with `{ type: \"person\" }`. A first name alone (\"Sam\") matches many people: pass what the user said about them (runs OpenAI, hosts a show) as `context`, and when resolve still answers `ask`, show its options.\n\n## Worked program\n\nPass each block to `arcmira_execute_read` as one program (a block marked arcmira_execute_write goes to that tool), with the name swapped for the user's. It opens with the pick: set `CONTEXT` to the user's own words about the name. When several entities fit it returns `ask` and runs nothing else. Show those options to the user, then run it again with `ID` set to the pick. Build date windows from `arcmira.daysAgo(n)` and `arcmira.today()`.\n\n### Prep on one person\n\n```javascript\nconst NAME = \"Jensen Huang\", CONTEXT = undefined, ID = null;   \u002F\u002F CONTEXT: the user's own words about the name, never a guess. After an ask, set ID to the picked option's id and run again\nconst r = ID ? null : await arcmira.resolve(NAME, { type: \"person\", context: CONTEXT });\nconst e = r && (r.best ?? r.suggested);\nif (r && !e) return { ask: r.ask };\nconst id = ID ?? e.id;\nconst assumed = Boolean(r?.suggested), why = r?.suggested?.evidence ?? null;\nconst [m, rows, own] = await Promise.all([\n  arcmira.momentum(id),\n  arcmira.mentions({ entityId: id, mode: \"appearances\", after: arcmira.daysAgo(90), limit: 40 }),\n  arcmira.search({ query: \"AI\", speakerIds: [id], after: arcmira.daysAgo(180), limit: 5 }),\n]);\nconst about = await arcmira.search({ query: m.entity.name, about: [id], after: arcmira.daysAgo(90), limit: 3 });\nconst appeared = new Map();\nfor (const x of rows.mentions) appeared.set(x.media.video_id, { show: x.media.source_channel?.name ?? null, episode: x.media.title, date: x.media.published_at });\nlet fromAppearance = null;   \u002F\u002F no speaker-tagged chunks: read their newest appearance instead\nconst ep = own.chunks.length === 0 ? rows.mentions[0] : undefined;\nif (ep) {\n  try {\n    const t = await arcmira.transcript(ep.media.video_id, { start: Math.max(0, ep.start_seconds - 5), end: ep.start_seconds + 90 });\n    const s = Math.floor(t.lines[0]?.start ?? ep.start_seconds);\n    fromAppearance = { episode: t.video.title, show: t.video.channel_name, date: t.video.published_at, url: `${t.video.watch_url}${t.video.watch_url.includes(\"?\") ? \"&\" : \"?\"}t=${s}`, lines: t.lines.map(l => `[${Math.round(l.start)}s] ${l.text}`), speaker_labelled: t.lines.some(l => l.speaker) };\n  } catch (err) {\n    fromAppearance = { episode: ep.media.title, error: err.code };\n  }\n}\nreturn {\n  person: { id, name: m.entity.name, page: m.entity.page, assumed, why },\n  attention: { verdict: m.verdict, last_30d: m.volume.mentions_30d, prior_30d: m.volume.mentions_prior_30d, as_of: m.as_of, top_shows: m.top_shows.map(s => [s.channel_name, s.mentions]) },\n  appeared_on: [...appeared.values()].slice(0, 8),\n  appeared_on_partial: rows.has_more || appeared.size > 8,\n  appeared_on_cursor: rows.has_more ? rows.next_cursor : null,\n  in_their_words: own.chunks.map(c => ({ said: c.text.slice(0, 300), episode: c.video_title, show: c.channel_name, date: c.published_at, url: c.watch_url })),\n  from_their_appearance: fromAppearance,\n  said_about_them: about.chunks.map(c => ({ said: c.text.slice(0, 200), show: c.channel_name, date: c.published_at, url: c.watch_url })),\n};\n```\n\n## A good answer\n\n- Names the person it researched (name and id), and any other person with the same name it set aside.\n- Separates what the person said (speaker-filtered) from what others said about them, each with show, date and link.\n- Gives the attention verdict, the 30-day count against the prior 30 days, and the top shows, with `as_of`.\n- Lists recent episodes they appeared on, and ends with a few questions or themes that follow from the quotes when the user is prepping.\n\n## Traps\n\n- A search with `about` returns other people talking; only `speakerIds` returns the person's own words. Speaker tags cover part of the index, so when the speaker search is empty, read a window of an episode they appeared on and say the lines come from their appearance, since caption lines do not name the speaker.\n- A chunk found with `speakerIds` can include other people's lines. Lines start with \"Name: \" when the speaker is known. Quote only the person's lines. When no line names a speaker, say the attribution covers the passage, not each line.\n- Put the user's topic in `query` for the speaker search (it needs a word or phrase of two or more characters).\n- Mentions and momentum count the shows Arcmira indexes, not all media.\n\nDocs: https:\u002F\u002Farcmira.com\u002Fdocs\u002Fmcp-server\n\n## After the answer\n\nMonitors can follow what a research turn found, and each alert uses 25 credits from the account's plan, then its on-demand budget. The `company-watch` skill sets one up.\n\nIf anything was wrong, slow, or missing for the user, or they want a source or capability Arcmira lacks, send one arcmira_feedback.\n",{"name":5,"description":7},"main","f620abaff09061f8c8b1f1559dca3fdd031a32e5","plugins\u002Farcmira\u002Fmcp\u002Fskills\u002Fperson-research",474,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-11 14:40:14",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill person-research",{"tool":60,"command":61},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill person-research",{"tool":63,"command":64},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill person-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Farcmira\u002Fmcp\u002Fskills\u002Fperson-research\u002FSKILL.md",true,[],[69,77,84,91,118,142],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":76},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":76},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":76},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":39},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":39},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[125,126,127,113,33,128,129,130,131,132,133,134,135,136,103,137,138,139,140,141],"deja reflect","reflect","review","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":157,"publishedAt":39},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[148,151,154],{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":155,"categoryName":156,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[144,158,159,160,161,162,163,138,164,104,165,166,167,168,109,169,170,171,172,173],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]