[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MMB10WB61CPWS29N9VSAAX":3,"skill-same:01M6MMB10WB61CPWS29N9VSAAX":75},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":25,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":13,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6MMB10WB61CPWS29N9VSAAX","pulse",null,"Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","JiayiV\u002Fquestion_mixing","JiayiV","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54466889?v=4","01M6JFJYYMRZ3QRGP9339JNQ2K","01M6JFJZ35KB0QVT50NAHPJA7G",0,"MIT","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-research-pulse-skills-pulse-skill-e7711097\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"time","window","hacker","briefing","people","trending","scope","trend","across","pattern","audit","me","info","conversation","default","conversion","open","analysis","Citations","2026-11-10 10:12:30",false,"---\nname: pulse\ndescription: \"Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.\"\nlicense: MIT\nmetadata:\n  source_spec: \"megaprompts\u002F01-pulse-megaprompt.md\"\n  build_pattern: \"Path B (direct conversion)\"\n  research_pack_convention: \"Agent Integrity Rules block preserved verbatim per PR #657 audit\"\n  version: 1.0.0\n---\n\n# Pulse — Multi-Source Recency Research\n\n> **Portability:** Works in both Claude Code CLI and Claude.ai. The optional X\u002FTwitter phase requires browser automation and is skipped automatically if unavailable.\n\nA recency-oriented research skill that synthesizes what people are saying about a topic across Reddit, Hacker News, the open web, and (optionally) X\u002FTwitter — within a configurable time window. Output is a single coherent briefing with citations, engagement signals, and cross-platform pattern analysis. The skill captures the **current conversation**, not the canonical reference.\n\n## Invocation\n\n**Explicit trigger phrases:**\n- \"pulse on [topic]\"\n- \"what's happening with [topic]\"\n- \"what are people saying about [topic]\"\n- \"current conversation about [topic]\"\n- \"take the pulse of [topic]\"\n- \"trending: [topic]\"\n- \"find me info on [topic]\"\n\nAlso covers: competitor research with recency flavor, trend discovery, tool comparisons, audience sentiment analysis.\n\n## Agent Integrity Rules (Research-Pack Convention)\n\nThe following rules apply throughout the run. They are inherited from the research-pack convention and locked down by PR #657's cross-skill consistency audit.\n\n- **Execution discipline.** Phases 1–3 run in parallel (Reddit + HN + Web are independent). Within each phase, sequential calls only. **1 q\u002Fsec rate limit per platform.** Confirm response received before next call within the same phase.\n- **Source discipline.** Cite only sources returned by **this session's tool calls.** Training knowledge is labeled `[Background — not from search]` and excluded from primary findings count.\n- **Three-count tracking.** Queries sent \u002F sources received (shown) \u002F sources cited. Surfaced in the audit log inline in the synthesis section. Use `scripts\u002Fcitation_tracker.py` for the deterministic count.\n- **Retry policy.** On failure → wait 3s → retry once → log. After **3 consecutive failures across all sources:** stop, alert user, share what was collected. Never deliver an empty file.\n- **Plan-tier detection.** Reddit + HN are unauthenticated public JSON APIs (rate-limited per IP, not per plan). Surface rate-limit signals from response headers when available; degrade gracefully otherwise.\n\nSee `references\u002Fresearch_pack_conventions.md` for the canon and `references\u002Fparallel_execution_discipline.md` for the rate-limit rationale.\n\n## Phase 0: Grill-Me Intake (2–4 forcing questions, one at a time)\n\nDependency-ordered. Each question carries explicit \"why I'm asking\". Stop condition: max 4.\n\n### Q1 (root) — Topic Specificity\n\n> **What's the topic? State it in 1–2 sentences — be specific. \"AI\" or \"tech\" will get you a vague survey; \"self-hosted LLM deployment for small teams\" or \"Claude Code adoption among enterprise engineering orgs\" will get you a useful answer.**\n>\n> *Why I'm asking:* Specificity dictates search quality. Vague topics produce vague briefings. If your topic is broad, I'd rather narrow it now than spend a search budget on noise.\n\n**Refuse mush.** If the user says \"AI\", push back once: \"What about AI — adoption, safety, capability, regulation, or comparison? Pick an angle.\" If the user still won't narrow after one push-back, deliver with the explicit \"vague topic — survey level, not depth\" caveat.\n\n### Q2 (depends on Q1) — Angle\n\n> **What angle matters most? Pick one:**\n>\n> 1. **Trend** — what's accelerating or decelerating\n> 2. **Sentiment** — what people feel about it\n> 3. **Problems** — pain points and complaints\n> 4. **Opportunities** — gaps and unmet needs\n> 5. **Comparison** — how it stacks up against alternatives\n>\n> *Why I'm asking:* The angle dictates which sources weight more (Reddit for sentiment, HN for technical critique, Web for trend coverage) and how I rank the synthesis.\n\nForcing choice. **Recommended default:** trend, unless the topic obviously calls for a different angle.\n\n### Q3 (always) — Time Window\n\n> **Time window: 7 \u002F 14 \u002F 30 \u002F 60 \u002F 90 days? Default is 30.**\n>\n> *Why I'm asking:* 7 days catches breaking conversation; 90 days catches sustained narrative shift. Pick based on how recent the news matters.\n\nForcing choice with default.\n\n### Q4 (depends on Q1) — Platform Scope\n\n> **Any platform to skip? By default I'll cover Reddit + Hacker News + open web, plus X\u002FTwitter if browser automation is available. Skip any you don't care about.**\n>\n> *Why I'm asking:* Skipping a platform saves search budget. Reddit dominates sentiment; HN dominates technical critique; Web dominates breadth; X dominates breaking conversation. Skip what doesn't fit your angle.\n\nAsked only if Q1 + Q2 suggest some platforms are clearly off-target (e.g., consumer sentiment topic → HN less useful). Otherwise default to \"all platforms\".\n\n**Stop condition:** After Q4 (or earlier with dependency skips), commit and start Phase 1. Max 4 questions, never bundle.\n\n## Pre-flight\n\nBefore any phase fires:\n\n1. **Compute the time window** with `scripts\u002Ftime_window_calculator.py --window \u003CNd>`. Get back the Unix timestamp for `created_at_i>` (HN) and the `t=` parameter (`hour|day|week|month|year|all`) for Reddit.\n2. **Generate the output slug** with `scripts\u002Ftopic_slug_generator.py --topic \"\u003Ctopic>\" --date $(date +%Y-%m-%d)`. Detect if `${RESEARCH_DIR}\u002Fpulse\u002F\u003Cslug>-\u003Cdate>.md` already exists; if yes, append `-v2` suffix or warn user.\n3. **Start the three-count audit log** with `scripts\u002Fcitation_tracker.py --action start --session pulse-\u003Cdate>-\u003Cslug>`. This file at `~\u002F.pulse_sessions\u002F\u003Csession>.json` persists across the run.\n\n## Phase 1: Reddit (parallel with HN + Web)\n\n**API:** `reddit.com\u002Fsearch.json` (unauthenticated, public JSON).\n\n**Queries (sequential within Reddit, 1 q\u002Fsec):**\n1. `sort=top&t=\u003Cwindow>&q=\u003Ctopic>` — top posts in window\n2. `sort=new&t=\u003Cwindow>&q=\u003Ctopic>` — new posts in window (catches breaking signal)\n3. For each of the top 3–5 posts by score: fetch the comments JSON (`\u003Cpost-url>.json?limit=top`) for the top 10–20 comments.\n\n**Headers \u002F rate limits.** Reddit rate-limits by IP, not plan. Throttle to 1 q\u002Fsec. If response has `X-Ratelimit-Remaining: 0` or returns 429, wait 3s, retry once. If still failing, fall back to subreddit-restricted search (`r\u002F\u003Ctopic-subreddit>\u002Fsearch.json`) or `?raw_json=1`.\n\n**Record each query:** `citation_tracker.py --action record_sent --session NAME --query \"...\"`.\n**Record received counts:** `citation_tracker.py --action record_received --session NAME --count N`.\n\n## Phase 2: Hacker News (parallel with Reddit + Web)\n\n**API:** Algolia HN search (`hn.algolia.com\u002Fapi\u002Fv1\u002F`).\n\n**Queries (sequential within HN, 1 q\u002Fsec):**\n1. `search?query=\u003Ctopic>&numericFilters=created_at_i>\u003Ctimestamp>&tags=story` — stories in window\n2. `search?query=\u003Ctopic>&numericFilters=created_at_i>\u003Ctimestamp>&tags=comment` — comments in window (catches discussion signal)\n\n**Failure handling.** If HN returns empty: broaden the query (remove uncommon nouns); if still empty, drop the timestamp filter as last resort and label results \"outside window\".\n\n**HN bias note.** HN skews technical \u002F builder. Surface this in synthesis: \"HN's voice is implementation-oriented; consumer sentiment will be under-represented here.\"\n\n## Phase 3: Web Search (parallel with Reddit + HN)\n\n**Tools:** Available web search + fetch (e.g., `WebSearch` + `WebFetch`).\n\n**Query strategy (sequential within Web, 1 q\u002Fsec):**\n1. **Trusted publishers** — `\"\u003Ctopic>\" site:nytimes.com OR site:wsj.com OR site:wired.com OR site:theverge.com OR site:techcrunch.com after:\u003Cdate>`\n2. **Recent reviews** — `\"\u003Ctopic>\" review \u003Cyear>` or `\"\u003Ctopic>\" \"honest review\" after:\u003Cdate>`\n3. **Honest-opinion sources** — `\"\u003Ctopic>\" problems OR complaints OR \"worth it\" after:\u003Cdate>`\n\nFetch the top 3–5 URLs per query. Truncate at the body, skip cookie\u002Fnav markup.\n\n**Citation discipline.** Every claim in the Web section must trace to a fetched URL. Do NOT cite from snippets alone; fetch first.\n\n## Phase 4: X\u002FTwitter (sequential, optional)\n\nRun last. Reasons:\n- Most likely to fail \u002F require browser automation\n- X content overlaps significantly with Reddit\u002FHN — so it adds delta, not primary signal\n\n**Interface (in priority order):**\n1. **Grok** if available in the harness\n2. **X API** if authenticated\n3. **Browser automation** if the harness supports it (Claude Code CLI with `playwright` or similar)\n4. **Skip with note** if none of the above available\n\n**Documented behavior:**\n> If Phase 4 is skipped: include the section header `## X\u002FTwitter` with body `Skipped — [reason: no browser automation \u002F no Grok \u002F no X API]`. Do NOT pretend to have data.\n\n## Synthesis (Cross-Platform Patterns)\n\nAfter Phases 1–4 complete (or Phase 4 skipped), produce the synthesis:\n\n1. **Consensus signals** — points where 3+ platforms agree (highest confidence). Tag each with cited source URLs.\n2. **Controversy signals** — points where platforms disagree. Note who says what.\n3. **Pain points** — recurring complaints across sources (esp. Reddit + Web).\n4. **Excitement signals** — recurring enthusiasm (esp. HN + X if available).\n5. **Emerging trends** — first-time mentions in newest posts but absent from older ones (compare `sort=new` vs `sort=top`).\n6. **Gaps** — what's notably absent that you'd expect to find.\n\nFor each pattern, **cite the source URLs** that support it. Use `citation_tracker.py --action record_cited --session NAME --url \"...\"` per citation.\n\nSee `references\u002Fcross_platform_synthesis.md` for detection heuristics.\n\n## Output\n\nSave to file AND paste in chat:\n\n**File:** `${RESEARCH_DIR}\u002Fpulse\u002F\u003Ctopic-slug>-\u003CYYYY-MM-DD>.md` (path from `topic_slug_generator.py`).\n\n**Format:**\n\n```markdown\n# [TOPIC] — Pulse (Last [N] Days)\n*Generated: [DATE] | Angle: [Q2 choice]*\n\n## TL;DR\n[2-3 sentences max]\n\n## Reddit\n### Top Posts\n- **[Title]** (r\u002Fsub) — [score, comments] — [summary] — [URL]\n### What Reddit Is Saying\n[Narrative paragraph]\n\n## Hacker News\n### Notable Stories\n- **[Title]** — [points, comments] — [summary] — [URL]\n### What HN Is Saying\n[Narrative paragraph; note HN's technical\u002Fbuilder bias]\n\n## Web\n### Key Sources\n- **[Title]** ([Publication]) — [takeaway] — [URL]\n### What the Web Is Saying\n[Narrative paragraph]\n\n## X\u002FTwitter (if available)\n[Cleaned response, with handles\u002Freferences preserved]\n[Or: \"Skipped — [reason]\"]\n\n## Cross-Platform Patterns\n[Highest-confidence signals across sources]\n\n## Key Takeaways\n- [3-5 bullets]\n\n## Content Angles (if applicable)\n[2-3 specific angles supported by the data]\n\n---\n*Audit:* Queries sent: N (Reddit: a, HN: b, Web: c, X: d|skipped).\nSources received: M. Sources cited: K. Training knowledge: 0 ([Background] excluded from count).\n```\n\n## Error Handling\n\n| Failure | Behavior |\n|---|---|\n| Topic is too vague (Q1) | Refuse to start. Re-ask Q1 once with examples. After 1 push-back, deliver with \"vague topic\" caveat. |\n| Reddit blocks \u002F rate-limits | Try `?raw_json=1` or fall back to subreddit-restricted search. Honor 3s-retry. |\n| HN returns empty | Broaden query, drop timestamp filter as last resort, label results \"outside window\". |\n| Web search returns nothing useful | Note in output; don't fabricate sources. |\n| Browser automation unavailable | Skip Phase 4 with documented note. |\n| WebFetch times out | Use what loaded, mark the source as \"truncated\". |\n| 3 consecutive failures across sources | Stop. Return what was collected with explicit \"stopped early\" note. Do NOT deliver empty file. |\n| All sources fail | Return error with diagnostic info. Do NOT deliver empty file. |\n\n## Tooling\n\n| Script | Role |\n|---|---|\n| `scripts\u002Ftime_window_calculator.py` | Compute Unix timestamps + Reddit `t=` parameter from window string (`30d`, `7d`, etc.). Deterministic from `datetime.now()`. |\n| `scripts\u002Fcitation_tracker.py` | JSON-backed three-count audit log (sent \u002F received \u002F cited) at `~\u002F.pulse_sessions\u002F\u003Csession>.json`. |\n| `scripts\u002Ftopic_slug_generator.py` | Filesystem-safe slug + duplicate-date detection for output paths. |\n\n## References\n\n- `references\u002Fresearch_pack_conventions.md` — Agent Integrity Rules canon (7+ sources: Google SRE, Reddit API docs, Algolia HN docs, exponential-backoff literature, citation discipline)\n- `references\u002Fcross_platform_synthesis.md` — consensus \u002F controversy \u002F pain detection across platforms (7+ sources)\n- `references\u002Fparallel_execution_discipline.md` — 1 q\u002Fsec rationale + plan-tier signals (7+ sources)\n\n## Anti-Patterns To Reject\n\n- Starting any search before the user commits to topic specificity (Q1)\n- Batching intake questions instead of one at a time\n- Hardcoded URLs that won't survive API changes (note format, explain may evolve)\n- Specific person \u002F brand references in the skill body\n- Tight coupling to one X\u002FTwitter interface\n- Missing fallback behavior on source failure\n- \"Just use [specific tool]\" without explaining what the tool does\n- Citing training knowledge in the cited count\n- Fabricating sources to fill out a section\n\n---\n\n**Version:** 1.0.0\n**Source spec:** `megaprompts\u002F01-pulse-megaprompt.md` (maintainer-local draft spec — gitignored, not present in the public repository)\n**Build pattern:** Path B (direct conversion). Re-grill with `\u002Fcs:grill-with-docs` if drift between spec and implementation surfaces.\n","# Pulse — Multi-Source Recency Research\n\n> **Portability:** Works in both Claude Code CLI and Claude.ai. The optional X\u002FTwitter phase requires browser automation and is skipped automatically if unavailable.\n\nA recency-oriented research skill that synthesizes what people are saying about a topic across Reddit, Hacker News, the open web, and (optionally) X\u002FTwitter — within a configurable time window. Output is a single coherent briefing with citations, engagement signals, and cross-platform pattern analysis. The skill captures the **current conversation**, not the canonical reference.\n\n## Invocation\n\n**Explicit trigger phrases:**\n- \"pulse on [topic]\"\n- \"what's happening with [topic]\"\n- \"what are people saying about [topic]\"\n- \"current conversation about [topic]\"\n- \"take the pulse of [topic]\"\n- \"trending: [topic]\"\n- \"find me info on [topic]\"\n\nAlso covers: competitor research with recency flavor, trend discovery, tool comparisons, audience sentiment analysis.\n\n## Agent Integrity Rules (Research-Pack Convention)\n\nThe following rules apply throughout the run. They are inherited from the research-pack convention and locked down by PR #657's cross-skill consistency audit.\n\n- **Execution discipline.** Phases 1–3 run in parallel (Reddit + HN + Web are independent). Within each phase, sequential calls only. **1 q\u002Fsec rate limit per platform.** Confirm response received before next call within the same phase.\n- **Source discipline.** Cite only sources returned by **this session's tool calls.** Training knowledge is labeled `[Background — not from search]` and excluded from primary findings count.\n- **Three-count tracking.** Queries sent \u002F sources received (shown) \u002F sources cited. Surfaced in the audit log inline in the synthesis section. Use `scripts\u002Fcitation_tracker.py` for the deterministic count.\n- **Retry policy.** On failure → wait 3s → retry once → log. After **3 consecutive failures across all sources:** stop, alert user, share what was collected. Never deliver an empty file.\n- **Plan-tier detection.** Reddit + HN are unauthenticated public JSON APIs (rate-limited per IP, not per plan). Surface rate-limit signals from response headers when available; degrade gracefully otherwise.\n\nSee `references\u002Fresearch_pack_conventions.md` for the canon and `references\u002Fparallel_execution_discipline.md` for the rate-limit rationale.\n\n## Phase 0: Grill-Me Intake (2–4 forcing questions, one at a time)\n\nDependency-ordered. Each question carries explicit \"why I'm asking\". Stop condition: max 4.\n\n### Q1 (root) — Topic Specificity\n\n> **What's the topic? State it in 1–2 sentences — be specific. \"AI\" or \"tech\" will get you a vague survey; \"self-hosted LLM deployment for small teams\" or \"Claude Code adoption among enterprise engineering orgs\" will get you a useful answer.**\n>\n> *Why I'm asking:* Specificity dictates search quality. Vague topics produce vague briefings. If your topic is broad, I'd rather narrow it now than spend a search budget on noise.\n\n**Refuse mush.** If the user says \"AI\", push back once: \"What about AI — adoption, safety, capability, regulation, or comparison? Pick an angle.\" If the user still won't narrow after one push-back, deliver with the explicit \"vague topic — survey level, not depth\" caveat.\n\n### Q2 (depends on Q1) — Angle\n\n> **What angle matters most? Pick one:**\n>\n> 1. **Trend** — what's accelerating or decelerating\n> 2. **Sentiment** — what people feel about it\n> 3. **Problems** — pain points and complaints\n> 4. **Opportunities** — gaps and unmet needs\n> 5. **Comparison** — how it stacks up against alternatives\n>\n> *Why I'm asking:* The angle dictates which sources weight more (Reddit for sentiment, HN for technical critique, Web for trend coverage) and how I rank the synthesis.\n\nForcing choice. **Recommended default:** trend, unless the topic obviously calls for a different angle.\n\n### Q3 (always) — Time Window\n\n> **Time window: 7 \u002F 14 \u002F 30 \u002F 60 \u002F 90 days? Default is 30.**\n>\n> *Why I'm asking:* 7 days catches breaking conversation; 90 days catches sustained narrative shift. Pick based on how recent the news matters.\n\nForcing choice with default.\n\n### Q4 (depends on Q1) — Platform Scope\n\n> **Any platform to skip? By default I'll cover Reddit + Hacker News + open web, plus X\u002FTwitter if browser automation is available. Skip any you don't care about.**\n>\n> *Why I'm asking:* Skipping a platform saves search budget. Reddit dominates sentiment; HN dominates technical critique; Web dominates breadth; X dominates breaking conversation. Skip what doesn't fit your angle.\n\nAsked only if Q1 + Q2 suggest some platforms are clearly off-target (e.g., consumer sentiment topic → HN less useful). Otherwise default to \"all platforms\".\n\n**Stop condition:** After Q4 (or earlier with dependency skips), commit and start Phase 1. Max 4 questions, never bundle.\n\n## Pre-flight\n\nBefore any phase fires:\n\n1. **Compute the time window** with `scripts\u002Ftime_window_calculator.py --window \u003CNd>`. Get back the Unix timestamp for `created_at_i>` (HN) and the `t=` parameter (`hour|day|week|month|year|all`) for Reddit.\n2. **Generate the output slug** with `scripts\u002Ftopic_slug_generator.py --topic \"\u003Ctopic>\" --date $(date +%Y-%m-%d)`. Detect if `${RESEARCH_DIR}\u002Fpulse\u002F\u003Cslug>-\u003Cdate>.md` already exists; if yes, append `-v2` suffix or warn user.\n3. **Start the three-count audit log** with `scripts\u002Fcitation_tracker.py --action start --session pulse-\u003Cdate>-\u003Cslug>`. This file at `~\u002F.pulse_sessions\u002F\u003Csession>.json` persists across the run.\n\n## Phase 1: Reddit (parallel with HN + Web)\n\n**API:** `reddit.com\u002Fsearch.json` (unauthenticated, public JSON).\n\n**Queries (sequential within Reddit, 1 q\u002Fsec):**\n1. `sort=top&t=\u003Cwindow>&q=\u003Ctopic>` — top posts in window\n2. `sort=new&t=\u003Cwindow>&q=\u003Ctopic>` — new posts in window (catches breaking signal)\n3. For each of the top 3–5 posts by score: fetch the comments JSON (`\u003Cpost-url>.json?limit=top`) for the top 10–20 comments.\n\n**Headers \u002F rate limits.** Reddit rate-limits by IP, not plan. Throttle to 1 q\u002Fsec. If response has `X-Ratelimit-Remaining: 0` or returns 429, wait 3s, retry once. If still failing, fall back to subreddit-restricted search (`r\u002F\u003Ctopic-subreddit>\u002Fsearch.json`) or `?raw_json=1`.\n\n**Record each query:** `citation_tracker.py --action record_sent --session NAME --query \"...\"`.\n**Record received counts:** `citation_tracker.py --action record_received --session NAME --count N`.\n\n## Phase 2: Hacker News (parallel with Reddit + Web)\n\n**API:** Algolia HN search (`hn.algolia.com\u002Fapi\u002Fv1\u002F`).\n\n**Queries (sequential within HN, 1 q\u002Fsec):**\n1. `search?query=\u003Ctopic>&numericFilters=created_at_i>\u003Ctimestamp>&tags=story` — stories in window\n2. `search?query=\u003Ctopic>&numericFilters=created_at_i>\u003Ctimestamp>&tags=comment` — comments in window (catches discussion signal)\n\n**Failure handling.** If HN returns empty: broaden the query (remove uncommon nouns); if still empty, drop the timestamp filter as last resort and label results \"outside window\".\n\n**HN bias note.** HN skews technical \u002F builder. Surface this in synthesis: \"HN's voice is implementation-oriented; consumer sentiment will be under-represented here.\"\n\n## Phase 3: Web Search (parallel with Reddit + HN)\n\n**Tools:** Available web search + fetch (e.g., `WebSearch` + `WebFetch`).\n\n**Query strategy (sequential within Web, 1 q\u002Fsec):**\n1. **Trusted publishers** — `\"\u003Ctopic>\" site:nytimes.com OR site:wsj.com OR site:wired.com OR site:theverge.com OR site:techcrunch.com after:\u003Cdate>`\n2. **Recent reviews** — `\"\u003Ctopic>\" review \u003Cyear>` or `\"\u003Ctopic>\" \"honest review\" after:\u003Cdate>`\n3. **Honest-opinion sources** — `\"\u003Ctopic>\" problems OR complaints OR \"worth it\" after:\u003Cdate>`\n\nFetch the top 3–5 URLs per query. Truncate at the body, skip cookie\u002Fnav markup.\n\n**Citation discipline.** Every claim in the Web section must trace to a fetched URL. Do NOT cite from snippets alone; fetch first.\n\n## Phase 4: X\u002FTwitter (sequential, optional)\n\nRun last. Reasons:\n- Most likely to fail \u002F require browser automation\n- X content overlaps significantly with Reddit\u002FHN — so it adds delta, not primary signal\n\n**Interface (in priority order):**\n1. **Grok** if available in the harness\n2. **X API** if authenticated\n3. **Browser automation** if the harness supports it (Claude Code CLI with `playwright` or similar)\n4. **Skip with note** if none of the above available\n\n**Documented behavior:**\n> If Phase 4 is skipped: include the section header `## X\u002FTwitter` with body `Skipped — [reason: no browser automation \u002F no Grok \u002F no X API]`. Do NOT pretend to have data.\n\n## Synthesis (Cross-Platform Patterns)\n\nAfter Phases 1–4 complete (or Phase 4 skipped), produce the synthesis:\n\n1. **Consensus signals** — points where 3+ platforms agree (highest confidence). Tag each with cited source URLs.\n2. **Controversy signals** — points where platforms disagree. Note who says what.\n3. **Pain points** — recurring complaints across sources (esp. Reddit + Web).\n4. **Excitement signals** — recurring enthusiasm (esp. HN + X if available).\n5. **Emerging trends** — first-time mentions in newest posts but absent from older ones (compare `sort=new` vs `sort=top`).\n6. **Gaps** — what's notably absent that you'd expect to find.\n\nFor each pattern, **cite the source URLs** that support it. Use `citation_tracker.py --action record_cited --session NAME --url \"...\"` per citation.\n\nSee `references\u002Fcross_platform_synthesis.md` for detection heuristics.\n\n## Output\n\nSave to file AND paste in chat:\n\n**File:** `${RESEARCH_DIR}\u002Fpulse\u002F\u003Ctopic-slug>-\u003CYYYY-MM-DD>.md` (path from `topic_slug_generator.py`).\n\n**Format:**\n\n```markdown\n# [TOPIC] — Pulse (Last [N] Days)\n*Generated: [DATE] | Angle: [Q2 choice]*\n\n## TL;DR\n[2-3 sentences max]\n\n## Reddit\n### Top Posts\n- **[Title]** (r\u002Fsub) — [score, comments] — [summary] — [URL]\n### What Reddit Is Saying\n[Narrative paragraph]\n\n## Hacker News\n### Notable Stories\n- **[Title]** — [points, comments] — [summary] — [URL]\n### What HN Is Saying\n[Narrative paragraph; note HN's technical\u002Fbuilder bias]\n\n## Web\n### Key Sources\n- **[Title]** ([Publication]) — [takeaway] — [URL]\n### What the Web Is Saying\n[Narrative paragraph]\n\n## X\u002FTwitter (if available)\n[Cleaned response, with handles\u002Freferences preserved]\n[Or: \"Skipped — [reason]\"]\n\n## Cross-Platform Patterns\n[Highest-confidence signals across sources]\n\n## Key Takeaways\n- [3-5 bullets]\n\n## Content Angles (if applicable)\n[2-3 specific angles supported by the data]\n\n---\n*Audit:* Queries sent: N (Reddit: a, HN: b, Web: c, X: d|skipped).\nSources received: M. Sources cited: K. Training knowledge: 0 ([Background] excluded from count).\n```\n\n## Error Handling\n\n| Failure | Behavior |\n|---|---|\n| Topic is too vague (Q1) | Refuse to start. Re-ask Q1 once with examples. After 1 push-back, deliver with \"vague topic\" caveat. |\n| Reddit blocks \u002F rate-limits | Try `?raw_json=1` or fall back to subreddit-restricted search. Honor 3s-retry. |\n| HN returns empty | Broaden query, drop timestamp filter as last resort, label results \"outside window\". |\n| Web search returns nothing useful | Note in output; don't fabricate sources. |\n| Browser automation unavailable | Skip Phase 4 with documented note. |\n| WebFetch times out | Use what loaded, mark the source as \"truncated\". |\n| 3 consecutive failures across sources | Stop. Return what was collected with explicit \"stopped early\" note. Do NOT deliver empty file. |\n| All sources fail | Return error with diagnostic info. Do NOT deliver empty file. |\n\n## Tooling\n\n| Script | Role |\n|---|---|\n| `scripts\u002Ftime_window_calculator.py` | Compute Unix timestamps + Reddit `t=` parameter from window string (`30d`, `7d`, etc.). Deterministic from `datetime.now()`. |\n| `scripts\u002Fcitation_tracker.py` | JSON-backed three-count audit log (sent \u002F received \u002F cited) at `~\u002F.pulse_sessions\u002F\u003Csession>.json`. |\n| `scripts\u002Ftopic_slug_generator.py` | Filesystem-safe slug + duplicate-date detection for output paths. |\n\n## References\n\n- `references\u002Fresearch_pack_conventions.md` — Agent Integrity Rules canon (7+ sources: Google SRE, Reddit API docs, Algolia HN docs, exponential-backoff literature, citation discipline)\n- `references\u002Fcross_platform_synthesis.md` — consensus \u002F controversy \u002F pain detection across platforms (7+ sources)\n- `references\u002Fparallel_execution_discipline.md` — 1 q\u002Fsec rationale + plan-tier signals (7+ sources)\n\n## Anti-Patterns To Reject\n\n- Starting any search before the user commits to topic specificity (Q1)\n- Batching intake questions instead of one at a time\n- Hardcoded URLs that won't survive API changes (note format, explain may evolve)\n- Specific person \u002F brand references in the skill body\n- Tight coupling to one X\u002FTwitter interface\n- Missing fallback behavior on source failure\n- \"Just use [specific tool]\" without explaining what the tool does\n- Citing training knowledge in the cited count\n- Fabricating sources to fill out a section\n\n---\n\n**Version:** 1.0.0\n**Source spec:** `megaprompts\u002F01-pulse-megaprompt.md` (maintainer-local draft spec — gitignored, not present in the public repository)\n**Build pattern:** Path B (direct conversion). Re-grill with `\u002Fcs:grill-with-docs` if drift between spec and implementation surfaces.\n",{"name":5,"license":14,"metadata":50,"description":7},{"version":51,"source_spec":52,"build_pattern":53,"research_pack_convention":54},"1.0.0","megaprompts\u002F01-pulse-megaprompt.md","Path B (direct conversion)","Agent Integrity Rules block preserved verbatim per PR #657 audit","main","068c2486ccc0f8d200a3db4d21ec137064eda3b4","skills\u002Fpublic-02-claude-skills-main-research-pulse-skills-pulse-skill-e7711097","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing","https:\u002F\u002Fgithub.com\u002FJiayiV","2026-08-25 16:30:52",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add JiayiV\u002Fquestion_mixing --skill pulse",{"tool":67,"command":68},"bunx","bunx skills add JiayiV\u002Fquestion_mixing --skill pulse",{"tool":70,"command":71},"pnpm","pnpm dlx skills add JiayiV\u002Fquestion_mixing --skill pulse","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-research-pulse-skills-pulse-skill-e7711097\u002FSKILL.md",true,[],[76,101,127,156,184,219],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":45},"01M6NN9PJS9NHWPDG4QP93BP8F","webmcpify","Make a web app agent-ready — propose a WebMCP tool manifest, integrate, verify in a real browser, heal; unrelated code stays untouched. Use for \"webmcpify\", \"add WebMCP\", or \"expose app actions to AI agents\".","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fwebmcpify\u002FSKILL.md",[],[78,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,32,99,100],"github","ai agents","web","notes","code","browser","https","actions","manifest","inventory","verify","tool","use","ready","unrelated","app","for","webmcp",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":105,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":45},"01M6NN6KZE5RBN6NGHN2WXV1RE","vardoger-analyze","Use when the user asks to personalize the GitHub Copilot CLI assistant, adapt Copilot to their style, use vardoger, or analyze their Copilot CLI conversation history. Reads the local session directory at `~\u002F.copilot\u002Fsession-state\u002F`, extracts recurring preferences and conventions, and writes a fenced personalization block into `~\u002F.copilot\u002Fcopilot-instructions.md`. Runs entirely on the user's machine via the local `vardoger` CLI (`pipx install vardoger`); no network calls and no uploads. Triggers: 'personalize my copilot', 'analyze my copilot history', 'tailor copilot to me', 'run vardoger', 'update my copilot instructions from history', 'make copilot learn my style'.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fvardoger-analyze\u002FSKILL.md",[],[109,110,111,112,113,114,115,116,117,118,119,120,39,37,121,122,123,124,125,126],"vardoger analyze","analyze","directory","cli","github copilot","copilot","run","update","learn","network","my style","install","local","github copilot cli","pipx","my","at","copilot instructions",{"skillNo":128,"name":129,"nameZh":6,"description":130,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":131,"categories":132,"keywords":137,"publishedAt":45},"01M6NN3CZGQP3XAJ04BM1NNYGT","transloadit-media-processing","Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS\u002FMP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftransloadit-media-processing\u002FSKILL.md",[133,136],{"categoryNo":134,"categoryName":135,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},[138,139,140,141,142,143,144,145,146,147,148,149,150,95,151,152,153,154,99,155],"transloadit media processing","Media","media processing","processing","MCP","mcp server","documents","subtitles","pipeline","ocr","Video","Audio","mp4","images","files","transformation","free","hls",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":165,"publishedAt":45},"01M6NN28E92F9GH4WFYQ2H1Y15","terraform-azurerm-set-diff-analyzer","Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes. Use when reviewing terraform plan output for Azure resources like Application Gateway, Load Balancer, Firewall, Front Door, NSG, and other resources with Set-type attributes that cause spurious diffs due to internal ordering changes.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fterraform-azurerm-set-diff-analyzer\u002FSKILL.md",[162],{"categoryNo":163,"categoryName":164,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",[166,167,168,169,170,171,172,173,174,95,175,176,177,178,179,180,181,182,183,99],"terraform","SET","terraform azurerm set diff analyzer","diff","analyzer","diffs","azure","gateway","firewall","only","resource changes","provider","resources","load balancer","attributes","front","load","balancer",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":201,"publishedAt":45},"01M6NN1QMH2TDYWTH3XY3N5XPV","technical-job-search","Use this skill when a software engineer asks for help with job search tasks: parsing or analyzing a job description, tailoring a CV\u002Fresume, writing a cover letter, evaluating a job offer, or drafting a post-interview follow-up email. Do not activate for general career advice unrelated to an active job search action.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftechnical-job-search\u002FSKILL.md",[190,193,198],{"categoryNo":191,"categoryName":192,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":194,"categoryName":195,"taxonomyType":196,"taxonomyTypeDesc":197},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",{"categoryNo":199,"categoryName":200,"taxonomyType":196,"taxonomyTypeDesc":197},"01JBSEEDCAT000000000000038","人力资源",[202,203,204,205,206,207,208,209,110,210,95,211,212,213,214,215,216,217,99,218],"search","Job Search","technical job search","technical","writing","tasks","email","parsing","interview","up","engineer","general","activate","do","task","cv","letter",{"skillNo":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":223,"categories":224,"keywords":225,"publishedAt":45},"01M6NMZFA67XYS1GS09WT42235","steno-mode","Shorthand-first response compression that cuts ~40% of response tokens while preserving technical precision and exact literals. Use when the user says \"steno mode\", \"shorthand mode\", \"compressed responses\", \"token reduction\", \"brief structured output\", or invokes \u002Fsteno. Supports four compression levels: lite, brief, court, machine. Do not trigger for requests needing polished prose such as onboarding\u002Ftutorial content, stakeholder or customer-facing copy, or teaching-focused explanations.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fsteno-mode\u002FSKILL.md",[],[226,227,228,229,230,205,231,95,215,232,233,234,235,236,237,238,239,240,99,241],"steno mode","mode","customer","brief","first","tutorial","teaching","tokens","as","trigger","structured output","machine","output","court","requests","response compression"]