{"id":"a0553ea1-cea4-4de2-99b5-87bbbd8408ef","shortId":"eyPGKp","kind":"skill","title":"show-hn-writer","tagline":"Draft a Show HN post backed by real HN performance data. Uses observed patterns from 250 top HN posts to maximise score.","description":"# Show HN Writer — Data-Backed Edition\n\nThis skill drafts HN posts using patterns extracted from 250 real top-ranking posts.\nEvery rule below comes from observed data, not convention.\n\n---\n\n## What the data says (internalize this before writing anything)\n\nThese are the findings from 250 top HN posts scraped April 18 2026. They override\nany received wisdom about HN writing.\n\n**Title length is the single strongest predictor of score.**\n- Under 40 chars: avg 248 pts (n=82)\n- 40–59 chars:    avg 192 pts (n=68)\n- 60–79 chars:    avg 150 pts (n=91)\n- 80+ chars:      avg 131 pts (n=9)\nDefault target: under 40 characters. Hard ceiling: 60.\n\n**Body text does not affect score.**\n90% of posts had no body. With-body avg: 189. Without-body avg: 193. Statistically\nidentical. A body is only worth writing if you have genuinely interesting technical\ndetail that won't fit in a title. Never write a body to pad credibility.\n\n**Show HN prefix suppresses score.**\nShow HN posts averaged 94 pts vs 186+ for plain statements. The label signals\n\"I want feedback on my thing\" which triggers a more skeptical read. Only use \"Show HN:\"\nwhen the project is genuinely novel. Always offer a plain-title alternative.\n\n**First-person titles outperform anonymous statements.**\nFirst-person (\"I…\", \"My…\", \"We…\"): avg 291 pts (n=9).\nPlain statement: avg 186 pts (n=126).\nIf the builder's perspective is part of the story, lead with it.\n\n**Questions generate comments more than upvotes.**\nQuestion titles avg ratio of comments-to-score above 1.0×. Best for discussions,\nnot for raw score. Ask the user which they're optimising for before writing.\n\n**Themes that consistently outperform:**\n- Security / backdoor / breach stories: avg 308 pts\n- Privacy / surveillance / data stories: avg 282 pts\n- AI / LLM releases: avg 266 pts (42 posts — largest category)\n- Open source releases: avg 485 pts (small n, but strong signal)\n\n**The highest-scoring titles share one trait: they are stories, not topics.**\n\"Someone bought 30 WordPress plugins and planted a backdoor in all of them\" — 1192 pts.\n\"Google broke its promise to me – now ICE has my data\" — 1688 pts.\nA topic is \"WordPress plugin security\". A story has an actor, an action, and stakes.\n\n---\n\n## Step 1: Ask the user one question before anything else\n\nBefore drafting, ask:\n\n\"Two quick questions:\n1. Are you optimising for **score** (reach) or **comments** (discussion)?\n2. What does the project do — one sentence, technical, no adjectives?\"\n\nDo not proceed until you have both answers.\n\n---\n\n## Step 2: Determine the right post type\n\nBased on the project and goal, decide which format to use:\n\n**Plain title (recommended default)**\nNo prefix. Just what it is or what happened. Highest avg score.\nUse when: sharing a release, article, tool, or event.\n\n**Show HN: prefix (use sparingly)**\nUse only when: the project is a working demo, the builder is present to answer\nquestions, and the technical implementation is the interesting part.\nAvg score is low (94), but it signals authenticity when the project is genuinely novel.\nAlways also draft a plain-title alternative for comparison.\n\n**Ask HN: prefix**\nUse when: the goal is discussion, not promotion. Avg engagement ratio > 1.0×.\nBest for \"who is using X?\" or \"should I do Y?\" posts.\n\n**Tell HN: prefix**\nWhistleblowing, accountability, or disclosure. One data point at 819 pts.\nOnly use if the post is factual, verifiable, and the builder is named.\n\n---\n\n## Step 3: Draft the title\n\n**The title is the entire post.** Treat the body as optional.\n\nRules derived from data:\n- Target under 40 characters. Every 20 chars over that costs roughly 30 avg points.\n- Write a story, not a category. Actor + action + stakes beats noun phrases.\n- First person (\"I…\") adds ~100 pts avg vs plain statement when builder perspective matters.\n- No marketing adjectives. Not \"fast\", \"simple\", \"powerful\", \"lightweight\" unless\n  it is a literal spec (e.g. \"35B-A3B\" is a spec, \"powerful\" is not).\n- Specificity beats generality. \"30 WordPress plugins\" beats \"popular CMS plugins\".\n- Year in brackets signals classic worth reading: (2008), (1956). Use when linking\n  older content that has aged well.\n- En dash (–) for subtitle format: \"Product Name – what it does\". Not a hyphen (-).\n\nDraft three variants:\n1. Shortest possible (aim for under 35 chars) — strip everything non-essential\n2. Story angle — actor + action + stakes\n3. Technical angle — lead with the interesting engineering decision\n\nThen apply the length test: count chars on each. Flag any over 60.\n\n---\n\n## Step 4: Decide whether to write a body\n\nAsk yourself: does the technical implementation have a detail that cannot fit in\nthe title and that HN engineers would find genuinely interesting?\n\nIf yes: write a body (see Step 5).\nIf no: stop at the title. No body is better than a padded body.\n\nThe data shows bodies do not increase score. The only reason to write one is if\nthe implementation is interesting enough that engineers will ask \"how does this work?\"\nand you want to pre-answer that.\n\n---\n\n## Step 5: Write the body (only if Step 4 said yes)\n\nStructure — keep it tight:\n\n**Line 1:** One sentence. What you built and why. First person.\nNot \"Introducing X.\" Not \"X is a tool that.\" Just: \"I built X because Y.\"\n\n**Lines 2–4:** The real reason. Honest. Specific. Was it a problem you hit yourself?\nSomething frustrating at work? A curiosity? \"I was annoyed that...\" is better than\n\"Developers often struggle with...\". The builder's voice is the point.\n\n**Lines 5–8:** How it actually works. This is what HN reads for.\nName the specific technology choices. State the tradeoffs you made and why.\nOne interesting engineering decision is worth more than a feature list.\n\n**Line 9:** Current state in one sentence. Open source? Free? Alpha? Solo?\nHow long you've been working on it.\n\n**Line 10:** One closing sentence inviting feedback or questions.\n\"Happy to answer questions about the implementation.\" / \"Criticism welcome.\"\nNever ask for upvotes, shares, or sign-ups.\n\nHard rules:\n- 150–300 words. Under 200 is usually better.\n- First person throughout.\n- No bullet points. No headers. No bold.\n- No links in body. URL goes in the submission field.\n- No marketing words: game-changing, revolutionary, powerful, robust, seamless,\n  innovative, best-in-class, streamline, leverage, transform, cutting-edge.\n\n---\n\n## Step 6: Self-check before presenting\n\nRun through this list. Fix anything that fails before outputting.\n\nTitle:\n- [ ] Under 60 characters (count them)\n- [ ] No marketing adjectives\n- [ ] Is it a story or a topic? (story = better)\n- [ ] First person if the builder's perspective adds something\n- [ ] No exclamation marks\n\nBody (if written):\n- [ ] Opens with \"I built…\" or \"For the past N months…\"\n- [ ] Contains at least one specific technology name or architecture decision\n- [ ] Under 300 words\n- [ ] No links\n- [ ] Closes with feedback invitation, not call to action\n- [ ] Zero marketing words (check the list in Step 5)\n\nPost type:\n- [ ] If using Show HN prefix: is a plain-title alternative also drafted?\n- [ ] If goal is comments: is it a question or divisive framing?\n- [ ] If goal is score: is it a statement, not a question?\n\n---\n\n## Step 7: Present output\n\nFormat exactly as follows. No commentary before or after.\n\n```\n## HN Post\n\n### Recommended title\n[title — the shortest, strongest variant]\n\n### Alternative titles\n1. [variant 2]\n2. [variant 3]\n\n---\n\n### Body\n[body text, or \"Not recommended — title is sufficient.\" if Step 4 said no body]\n\n---\n\n### Notes\n- Goal: [score / comments] — based on user's answer in Step 1\n- Post type used: [plain / Show HN / Ask HN / Tell HN]\n- Title length: [N chars]\n- Best time to post: Tuesday–Thursday, 8–10 AM US Eastern\n- After posting: respond to every comment in the first two hours\n- Do not share the link elsewhere for 24 hours — HN penalises vote rings\n```\n\n---\n\n## Step 8: Optional — scrape current top HN posts for context\n\nIf the user wants to check whether similar posts have been submitted recently,\nor wants to see what is performing in their category right now, run the scraper:\n\n```python\nimport requests\nfrom concurrent.futures import ThreadPoolExecutor\n\nHN_API = \"https://hacker-news.firebaseio.com/v0\"\n\ndef fetch_item(id_):\n    try:\n        r = requests.get(f\"{HN_API}/item/{id_}.json\", timeout=10)\n        return r.json() if r.ok else None\n    except Exception:\n        return None\n\nids = requests.get(f\"{HN_API}/topstories.json\").json()[:250]\nwith ThreadPoolExecutor(max_workers=20) as ex:\n    items = [i for i in ex.map(fetch_item, ids) if i]\n\n# Filter by keyword relevant to the user's project\nkeyword = \"YOUR_KEYWORD_HERE\"\nmatches = [i for i in items if keyword.lower() in i.get(\"title\",\"\").lower()]\n\nfor i, item in enumerate(matches, 1):\n    score = item.get(\"score\", 0)\n    title = item.get(\"title\", \"\")\n    by = item.get(\"by\", \"\")\n    print(f\"{i:>2}. [{score:>4}pts] {title} — {by}\")\n\nprint(f\"\\nTotal matching: {len(matches)}\")\n```\n\nUse the results to:\n- Check if a near-identical post was submitted in the last 48 hours (avoid duplication)\n- See which title patterns are landing in this category right now\n- Identify the score floor for this topic area\n\nResults are also appended to hn_log.csv automatically if the full scraper is used.\n\n---\n\n## Reference: observed top performers from dataset\n\nThese are real posts from the top 250. Study the title patterns.\n\nScore | Title\n------|-------\n1941  | Claude Opus 4.7\n1688  | Google broke its promise to me – now ICE has my data\n1244  | Qwen3.6-35B-A3B: Agentic coding power, now open to all\n1192  | Someone bought 30 WordPress plugins and planted a backdoor in all of them\n1141  | DaVinci Resolve – Photo\n990   | Codex for almost everything\n982   | Stop Flock\n909   | A new spam policy for \"back button hijacking\"\n893   | GitHub Stacked PRs\n819   | Tell HN: Fiverr left customer files public and searchable\n668   | I wrote to Flock's privacy contact to opt out of their domestic spying program\n619   | Measuring Claude 4.7's tokenizer costs\n561   | God sleeps in the minerals\n503   | Want to write a compiler? Just read these two papers (2008)\n\nBest Show HN titles (by score):\n341  | Show HN: Smol machines – subsecond coldstart, portable virtual machines\n199  | Show HN: PanicLock – Close your MacBook lid disable TouchID → password unlock\n187  | Show HN: Every CEO and CFO change at US public companies, live from SEC\n177  | Show HN: I made a calculator that works over disjoint sets of intervals\n152  | Show HN: MacMind – A transformer neural network in HyperCard on a 1989 Macintosh\n\nHighest comment-to-score ratios (for discussion-optimised posts):\n1.91× | Why is IPv6 so complicated?\n1.43× | Ask HN: Building a solo business is impossible?\n1.20× | Ohio prison inmates built computers and hid them in ceiling\n1.13× | Ask HN: Who is using OpenClaw?\n1.05× | The future of everything is lies, I guess: Where do we go from here?","tags":["show","writer","opendirectory","varnan-tech","agent-skills","gtm","hermes-agent","openclaw-skills","skills","technical-seo"],"capabilities":["skill","source-varnan-tech","skill-show-hn-writer","topic-agent-skills","topic-gtm","topic-hermes-agent","topic-openclaw-skills","topic-skills","topic-technical-seo"],"categories":["opendirectory"],"synonyms":[],"warnings":[],"endpointUrl":"https://skills.sh/Varnan-Tech/opendirectory/show-hn-writer","protocol":"skill","transport":"skills-sh","auth":{"type":"none","details":{"cli":"npx skills add Varnan-Tech/opendirectory","source_repo":"https://github.com/Varnan-Tech/opendirectory","install_from":"skills.sh"}},"qualityScore":"0.511","qualityRationale":"deterministic score 0.51 from registry signals: · indexed on github topic:agent-skills · 123 github stars · SKILL.md body (10,901 chars)","verified":false,"liveness":"unknown","lastLivenessCheck":null,"agentReviews":{"count":0,"score_avg":null,"cost_usd_avg":null,"success_rate":null,"latency_p50_ms":null,"narrative_summary":null,"summary_updated_at":null},"enrichmentModel":"deterministic:skill-github:v1","enrichmentVersion":1,"enrichedAt":"2026-05-02T00:55:52.383Z","embedding":null,"createdAt":"2026-04-18T22:18:40.307Z","updatedAt":"2026-05-02T00:55:52.383Z","lastSeenAt":"2026-05-02T00:55:52.383Z","tsv":"'/item':1365 '/topstories.json':1385 '/v0':1354 '0':1441 '1':403,418,725,872,1223,1255,1437 '1.0':289,558 '1.05':1765 '1.13':1758 '1.20':1747 '1.43':1738 '1.91':1732 '10':993,1277,1369 '100':647 '1141':1574 '1192':372,1560 '1244':1551 '126':259 '131':124 '150':117,1021 '152':1707 '1688':385,1539 '177':1693 '18':78 '186':199,256 '187':1678 '189':152 '192':109 '193':157 '1941':1535 '1956':699 '1989':1719 '199':1666 '2':428,448,738,898,1225,1226,1451 '20':622,1392 '200':1025 '2008':698,1649 '2026':79 '24':1299 '248':101 '250':20,43,72,1387,1528 '266':329 '282':323 '291':249 '3':598,744,1228 '30':361,628,684,1563 '300':1022,1141 '308':316 '341':1656 '35':731 '35b':673 '35b-a3b':672 '4':767,864,899,1240,1453 '4.7':1538,1628 '40':98,105,131,619 '42':331 '48':1479 '485':339 '5':804,857,937,1161 '503':1638 '561':1632 '59':106 '6':1071 '60':113,135,765,1089 '619':1625 '668':1609 '68':112 '7':1200 '79':114 '8':938,1276,1306 '80':121 '819':582,1599 '82':104 '893':1595 '9':127,252,973 '90':142 '909':1586 '91':120 '94':196,523 '982':1583 '990':1578 'a3b':674 'account':575 'action':399,638,742,1152 'actor':397,637,741 'actual':941 'add':646,1112 'adject':438,659,1095 'affect':140 'age':707 'agent':1553 'ai':325 'aim':728 'almost':1581 'alpha':982 'also':535,1175,1504 'altern':234,541,1174,1221 'alway':228,534 'angl':740,746 'annoy':920 'anonym':240 'answer':446,509,854,1003,1252 'anyth':66,410,1082 'api':1351,1364,1384 'append':1505 'appli':754 'april':77 'architectur':1138 'area':1501 'articl':486 'ask':297,404,414,544,774,843,1011,1262,1739,1759 'authent':527 'automat':1508 'averag':195 'avg':100,108,116,123,151,156,248,255,281,315,322,328,338,479,519,555,629,649 'avoid':1481 'back':10,32,1592 'backdoor':312,367,1569 'base':454,1248 'beat':640,682,687 'best':290,559,1061,1270,1650 'best-in-class':1060 'better':814,923,1028,1104 'bodi':136,147,150,155,161,183,610,773,801,812,818,822,860,1042,1117,1229,1230,1243 'bold':1038 'bought':360,1562 'bracket':693 'breach':313 'broke':375,1541 'build':1741 'builder':262,505,594,654,930,1109 'built':877,893,1123,1751 'bullet':1033 'busi':1744 'button':1593 'calcul':1699 'call':1150 'cannot':784 'categori':334,636,1337,1491 'ceil':134,1757 'ceo':1682 'cfo':1684 'chang':1054,1685 'char':99,107,115,122,623,732,759,1269 'charact':132,620,1090 'check':1074,1156,1320,1467 'choic':953 'class':1063 'classic':695 'claud':1536,1627 'close':995,1145,1670 'cms':689 'code':1554 'codex':1579 'coldstart':1662 'come':52 'comment':275,285,426,1180,1247,1286,1723 'comment-to-scor':1722 'commentari':1208 'comments-to-scor':284 'compani':1689 'comparison':543 'compil':1643 'complic':1737 'comput':1752 'concurrent.futures':1347 'consist':309 'contact':1616 'contain':1130 'content':704 'context':1314 'convent':57 'cost':626,1631 'count':758,1091 'credibl':186 'critic':1008 'curios':917 'current':974,1309 'custom':1604 'cut':1068 'cutting-edg':1067 'dash':710 'data':15,31,55,60,320,384,579,616,820,1550 'data-back':30 'dataset':1520 'davinci':1575 'decid':460,768 'decis':752,964,1139 'def':1355 'default':128,468 'demo':503 'deriv':614 'detail':172,782 'determin':449 'develop':925 'disabl':1674 'disclosur':577 'discuss':292,427,552,1729 'discussion-optimis':1728 'disjoint':1703 'divis':1186 'domest':1622 'draft':5,36,413,536,599,722,1176 'duplic':1482 'e.g':671 'eastern':1280 'edg':1069 'edit':33 'els':411,1374 'elsewher':1297 'en':709 'engag':556 'engin':751,792,841,963 'enough':839 'entir':606 'enumer':1435 'essenti':737 'event':489 'everi':49,621,1285,1681 'everyth':734,1582,1769 'ex':1394 'ex.map':1400 'exact':1204 'except':1376,1377 'exclam':1115 'extract':41 'f':1362,1382,1449,1458 'factual':590 'fail':1084 'fast':661 'featur':970 'feedback':208,998,1147 'fetch':1356,1401 'field':1048 'file':1605 'filter':1406 'find':70,794 'first':236,243,643,880,1029,1105,1289 'first-person':235,242 'fit':176,785 'fiverr':1602 'fix':1081 'flag':762 'flock':1585,1613 'floor':1497 'follow':1206 'format':462,713,1203 'frame':1187 'free':981 'frustrat':913 'full':1511 'futur':1767 'game':1053 'game-chang':1052 'general':683 'generat':274 'genuin':169,226,532,795 'github':1596 'go':1777 'goal':459,550,1178,1189,1245 'god':1633 'goe':1044 'googl':374,1540 'guess':1773 'hacker-news.firebaseio.com':1353 'hacker-news.firebaseio.com/v0':1352 'happen':477 'happi':1001 'hard':133,1019 'header':1036 'hid':1754 'highest':348,478,1721 'highest-scor':347 'hijack':1594 'hit':910 'hn':3,8,13,22,28,37,74,86,188,193,221,491,545,572,791,946,1167,1212,1261,1263,1265,1301,1311,1350,1363,1383,1601,1652,1658,1668,1680,1695,1709,1740,1760 'hn_log.csv':1507 'honest':903 'hour':1291,1300,1480 'hypercard':1716 'hyphen':721 'i.get':1428 'ice':381,1547 'id':1358,1366,1380,1403 'ident':159,1472 'identifi':1494 'implement':514,779,836,1007 'import':1344,1348 'imposs':1746 'increas':825 'inmat':1750 'innov':1059 'interest':170,517,750,796,838,962 'intern':62 'interv':1706 'introduc':883 'invit':997,1148 'ipv6':1735 'item':1357,1395,1402,1424,1433 'item.get':1439,1443,1446 'json':1367,1386 'keep':868 'keyword':1408,1415,1417 'keyword.lower':1426 'label':204 'land':1488 'largest':333 'last':1478 'lead':270,747 'least':1132 'left':1603 'len':1461 'length':89,756,1267 'leverag':1065 'lid':1673 'lie':1771 'lightweight':664 'line':871,897,936,972,992 'link':702,1040,1144,1296 'list':971,1080,1158 'liter':669 'live':1690 'llm':326 'long':985 'low':522 'lower':1430 'macbook':1672 'machin':1660,1665 'macintosh':1720 'macmind':1710 'made':958,1697 'mark':1116 'market':658,1050,1094,1154 'match':1419,1436,1460,1462 'matter':656 'max':1390 'maximis':25 'measur':1626 'miner':1637 'month':1129 'n':103,111,119,126,251,258,342,1128,1268 'name':596,715,949,1136 'near':1471 'near-ident':1470 'network':1714 'neural':1713 'never':180,1010 'new':1588 'non':736 'non-essenti':735 'none':1375,1379 'note':1244 'noun':641 'novel':227,533 'ntotal':1459 'observ':17,54,1516 'offer':229 'often':926 'ohio':1748 'older':703 'one':352,407,434,578,832,873,961,977,994,1133 'open':335,979,1120,1557 'openclaw':1764 'opt':1618 'optimis':303,421,1730 'option':612,1307 'opus':1537 'outperform':239,310 'output':1086,1202 'overrid':81 'pad':185,817 'paniclock':1669 'paper':1648 'part':266,518 'password':1676 'past':1127 'pattern':18,40,1486,1532 'penalis':1302 'perform':14,1334,1518 'person':237,244,644,881,1030,1106 'perspect':264,655,1111 'photo':1577 'phrase':642 'plain':201,232,253,465,539,651,1172,1259 'plain-titl':231,538,1171 'plant':365,1567 'plugin':363,391,686,690,1565 'point':580,630,935,1034 'polici':1590 'popular':688 'portabl':1663 'possibl':727 'post':9,23,38,48,75,144,194,332,452,570,588,607,1162,1213,1256,1273,1282,1312,1323,1473,1524,1731 'power':663,678,1056,1555 'pre':853 'pre-answ':852 'predictor':94 'prefix':189,470,492,546,573,1168 'present':507,1076,1201 'print':1448,1457 'prison':1749 'privaci':318,1615 'problem':908 'proceed':441 'product':714 'program':1624 'project':224,432,457,499,530,1414 'promis':377,1543 'promot':554 'prs':1598 'pts':102,110,118,125,197,250,257,317,324,330,340,373,386,583,648,1454 'public':1606,1688 'python':1343 'question':273,279,408,417,510,1000,1004,1184,1198 'quick':416 'qwen3.6-35b-a3b':1552 'r':1360 'r.json':1371 'r.ok':1373 'rank':47 'ratio':282,557,1726 'raw':295 're':302 'reach':424 'read':217,697,947,1645 'real':12,44,901,1523 'reason':829,902 'receiv':83 'recent':1327 'recommend':467,1214,1234 'refer':1515 'releas':327,337,485 'relev':1409 'request':1345 'requests.get':1361,1381 'resolv':1576 'respond':1283 'result':1465,1502 'return':1370,1378 'revolutionari':1055 'right':451,1338,1492 'ring':1304 'robust':1057 'rough':627 'rule':50,613,1020 'run':1077,1340 'said':865,1241 'say':61 'score':26,96,141,191,287,296,349,423,480,520,826,1191,1246,1438,1440,1452,1496,1533,1655,1725 'scrape':76,1308 'scraper':1342,1512 'seamless':1058 'searchabl':1608 'sec':1692 'secur':311,392 'see':802,1331,1483 'self':1073 'self-check':1072 'sentenc':435,874,978,996 'set':1704 'share':351,483,1014,1294 'shortest':726,1218 'show':2,7,27,187,192,220,490,821,1166,1260,1651,1657,1667,1679,1694,1708 'show-hn-writ':1 'sign':1017 'sign-up':1016 'signal':205,345,526,694 'similar':1322 'simpl':662 'singl':92 'skeptic':216 'skill':35 'skill-show-hn-writer' 'sleep':1634 'small':341 'smol':1659 'solo':983,1743 'someon':359,1561 'someth':912,1113 'sourc':336,980 'source-varnan-tech' 'spam':1589 'spare':494 'spec':670,677 'specif':681,904,951,1134 'spi':1623 'stack':1597 'stake':401,639,743 'state':954,975 'statement':202,241,254,652,1195 'statist':158 'step':402,447,597,766,803,856,863,1070,1160,1199,1239,1254,1305 'stop':807,1584 'stori':269,314,321,356,394,633,739,1099,1103 'streamlin':1064 'strip':733 'strong':344 'strongest':93,1219 'structur':867 'struggl':927 'studi':1529 'submiss':1047 'submit':1326,1475 'subsecond':1661 'subtitl':712 'suffici':1237 'suppress':190 'surveil':319 'target':129,617 'technic':171,436,513,745,778 'technolog':952,1135 'tell':571,1264,1600 'test':757 'text':137,1231 'theme':307 'thing':211 'threadpoolexecutor':1349,1389 'three':723 'throughout':1031 'thursday':1275 'tight':870 'time':1271 'timeout':1368 'titl':88,179,233,238,280,350,466,540,601,603,788,810,1087,1173,1215,1216,1222,1235,1266,1429,1442,1444,1455,1485,1531,1534,1653 'token':1630 'tool':487,889 'top':21,46,73,1310,1517,1527 'top-rank':45 'topic':358,388,1102,1500 'topic-agent-skills' 'topic-gtm' 'topic-hermes-agent' 'topic-openclaw-skills' 'topic-skills' 'topic-technical-seo' 'touchid':1675 'tradeoff':956 'trait':353 'transform':1066,1712 'treat':608 'tri':1359 'trigger':213 'tuesday':1274 'two':415,1290,1647 'type':453,1163,1257 'unless':665 'unlock':1677 'up':1018 'upvot':278,1013 'url':1043 'us':1279,1687 'use':16,39,219,464,481,493,495,547,563,585,700,1165,1258,1463,1514,1763 'user':299,406,1250,1317,1412 'usual':1027 'variant':724,1220,1224,1227 've':987 'verifi':591 'virtual':1664 'voic':932 'vote':1303 'vs':198,650 'want':207,850,1318,1329,1639 'welcom':1009 'well':708 'whether':769,1321 'whistleblow':574 'wisdom':84 'with-bodi':148 'without':154 'without-bodi':153 'won':174 'word':1023,1051,1142,1155 'wordpress':362,390,685,1564 'work':502,847,915,942,989,1701 'worker':1391 'worth':164,696,966 'would':793 'write':65,87,165,181,306,631,771,799,831,858,1641 'writer':4,29 'written':1119 'wrote':1611 'x':564,884,886,894 'y':569,896 'year':691 'yes':798,866 'zero':1153","prices":[{"id":"46caa1d7-f46e-4116-b4d1-4ad1cf67c844","listingId":"a0553ea1-cea4-4de2-99b5-87bbbd8408ef","amountUsd":"0","unit":"free","nativeCurrency":null,"nativeAmount":null,"chain":null,"payTo":null,"paymentMethod":"skill-free","isPrimary":true,"details":{"org":"Varnan-Tech","category":"opendirectory","install_from":"skills.sh"},"createdAt":"2026-04-18T22:18:40.307Z"}],"sources":[{"listingId":"a0553ea1-cea4-4de2-99b5-87bbbd8408ef","source":"github","sourceId":"Varnan-Tech/opendirectory/show-hn-writer","sourceUrl":"https://github.com/Varnan-Tech/opendirectory/tree/main/skills/show-hn-writer","isPrimary":false,"firstSeenAt":"2026-04-18T22:18:40.307Z","lastSeenAt":"2026-05-02T00:55:52.383Z"}],"details":{"listingId":"a0553ea1-cea4-4de2-99b5-87bbbd8408ef","quickStartSnippet":null,"exampleRequest":null,"exampleResponse":null,"schema":null,"openapiUrl":null,"agentsTxtUrl":null,"citations":[],"useCases":[],"bestFor":[],"notFor":[],"kindDetails":{"org":"Varnan-Tech","slug":"show-hn-writer","github":{"repo":"Varnan-Tech/opendirectory","stars":123,"topics":["agent-skills","gtm","hermes-agent","openclaw-skills","skills","technical-seo"],"license":null,"html_url":"https://github.com/Varnan-Tech/opendirectory","pushed_at":"2026-04-30T18:54:05Z","description":" AI Agent Skills built for GTM, Technical Marketing, and growth automation.","skill_md_sha":"f1468d3d78e3e34c0e65a9b01b22dd47f157fa01","skill_md_path":"skills/show-hn-writer/SKILL.md","default_branch":"main","skill_tree_url":"https://github.com/Varnan-Tech/opendirectory/tree/main/skills/show-hn-writer"},"layout":"multi","source":"github","category":"opendirectory","frontmatter":{"name":"show-hn-writer","description":"Draft a Show HN post backed by real HN performance data. Uses observed patterns from 250 top HN posts to maximise score.","compatibility":"[claude-code, gemini-cli, github-copilot]"},"skills_sh_url":"https://skills.sh/Varnan-Tech/opendirectory/show-hn-writer"},"updatedAt":"2026-05-02T00:55:52.383Z"}}