Three research runs

As of 15 November 2026 there is no single best writer, because human voters, blind readers, LLM judges and slop measures rank the models differently. The pick that holds up across the most kinds of evidence is Claude Opus 5.5, on OpenRouter at $4 input / $20 output per million tokens: it is in a statistical tie for first with human voters, at or near the top of most LLM-judged boards, and the most common choice of reviewers. Gemini 4 Argon ties it with human voters and is the favourite of many fiction and roleplay users, at $2 / $10 until 30 November and $4 / $20 from 1 December, but it scores worst of the frontier models on every measure of AI-sounding prose and it invents details. Claude Fable 5.2 ($8 / $40) gets the best prose scores from LLM judges but loses blind preference tests, and reviewers do not think it is worth double the price for drafting. Every model named as a strong writer is live on OpenRouter except one small writing fine-tune, and the prices are reasonable for an individual writer: the output of a 100,000-word draft costs about $2.70 at Opus 5.5's list price and under $0.10 on the cheapest capable models, before hidden reasoning tokens (arithmetic, explained below).

Statements marked (inference) or (arithmetic) are a researcher's or this report's own reading or calculation, not something a source states. All prices are US dollars per million tokens, input / output, checked against OpenRouter's live endpoints API on 15 November 2026.

Short pick list by budget

Opus 5.5 is the only model near the top with both human voters and LLM judges

• Human votes. On Arena's creative-writing board of 11 November, Gemini 4 Argon is first at 1522.5 (4,928 votes), Claude Opus 5.5 second at 1517.1 (3,800 votes) and Gemini 4 Carbon third at 1516.1 (1,007 votes). Their 95% intervals overlap, and the lead has changed hands between Opus 5.5 and Argon four times since late September (Arena leaderboard dataset).
• LLM judges. Opus 5.5 is in the top group on Lech Mazur's story benchmark (3.555, where the top four overlap) (lechmazur/writing), first on ToneBench at 2,704 Elo with Fable 5.2 inside its interval (ToneBench data), first overall on Novelcrafter's NC Bench at 96.43% (NC Bench), third on EQ-Bench Longform at 84.6 (EQ-Bench longform data) and fifth on Caliper Bench at 85.96 (Caliper Bench).
• Blind readers plus judges. It is second overall on Fiction Bench (66.7), which combines blind reader votes with AI judges (Fiction Bench data).
• Reviewers. Chapter calls Claude "the best LLM for creative writing in November 2026" (Chapter); BenchLM calls it "a tie between Claude Opus 5.5 and Gemini 4 Argon" (BenchLM); Layer3 Labs and Ryan Doser both pick Opus 5.5 (Layer3 Labs; Ryan Doser). Five cost reviews agree that Opus 5.5 is the default Claude for writing and Fable the exception (Lindy; FullStack).
• Usage. Through OpenRouter, the novel-writing tool novelcrafter sent the most tokens this month to Gemini 4 Argon (5.34B) and Claude Opus 5.5 (3.95B), and Opus 5.5 has the largest share of content-writing spend at 12.8% (novelcrafter app page; OpenRouter rankings). OpenRouter says its rankings "reflect adoption, not model quality" (OpenRouter roleplay collection).

The praise is qualified. Every found "the most readable prose we've seen from an Anthropic or OpenAI model" but said "It still buries the point" (Every). Arize measured em dashes falling from 12.9 to 0.05 per 1,000 words against Opus 5 while new habits appeared, and concluded "better, but not fixed" (Arize). EQ-Bench's short-fiction board puts Opus 5.5 only eighth, behind the older Opus 5 (EQ-Bench data).

A sizeable minority prefers the older Claude Opus 4.6 for voice. An Anthropic fine-tuning engineer said he has not been as happy with a model's writing since Opus 4.6 (The Decoder), and one nine-test comparison scored Opus 4.6 ahead of Opus 5.5 by 6 to 4 (Why Try AI). Opus 4.6 is still on OpenRouter at $5 / $25 and Anthropic will not retire it before 5 February 2027 (Opus 4.6 endpoints; Anthropic deprecations).

Gemini 4 Argon wins human votes and loses every slop measure

"Slop" here means the filler, stock phrasing and templated structure that make text read as machine-written.

Argon leads Arena's human vote and the UGI leaderboard's Writing metric (79.7) (Arena dataset; UGI leaderboard), and blind readers on Fiction Bench prefer it 59.4% of the time, second only to GPT-6.1 Terra (Fiction Bench data). Fiction users are direct about it: "I'm a big fan of Opus 5.5 but Argon just flat out writes better" (Hacker News). The most upvoted roleplay answer says the choice between Opus 5.5 and Argon "comes down to mostly taste" (r/SillyTavernAI).

Measures that look for AI-sounding prose put it at or near the bottom of the frontier group:

Argon also embellishes. In a vendor test of cited research writing, 31% of its claims were fully supported by the sources against 75% for Opus 5.5 and 85% for GPT-6.1 Sol (Agent Bayes). In a small marketing-copy test it invented six product specifications in four briefs (Skryvo). The positive reports are about fiction and the negative ones about writing bound to a source or brief, so both can be true: inventive, and unfaithful to a brief (inference).

The position this supports: choose Argon for fiction when you like its voice and will edit, and Opus 5.5 for structure, long projects and anything that must stay faithful to source material. From 1 December the two cost the same.

Fable 5.2 has the best-judged prose, but it loses blind tests and costs double

Claude Fable 5.2 was released on 9 November, so only a few boards have it. Where it is tested by LLM judges it is first or tied first: Mazur 3.687 (lechmazur/writing), Caliper Bench 87.62 (Caliper Bench), and Fiction Bench's AI judges at 87.6, top in 19 of 20 genres (Fiction Bench data). On HumanBench its everyday writing passed an AI-text detector 23.1% of the time, about double any other model (HumanBench paper).

Preference tests go the other way. Fiction Bench readers chose it 45% of the time, on only 42 votes (Fiction Bench data). In a blind essay test judged by GPT-6.1 Sol it "wrote the cleanest prose and almost never won" (1 of 12 rounds) (My Life in the Stack), though Peter Yang, judging by hand, picked it over Opus 5.5 in four of five essays (write-up). Ethan Mollick found its drafts could be "accurate and joyless" (One Useful Thing). A working author's advice is "Use Fable like a specialist, not a typist": Opus 5.5 or Sonnet 5.5 for drafting and line edits, Fable for structure and diagnosis (Writing Secrets). Fable 5.2 is not yet on Arena, EQ-Bench, NC Bench or Hemingway-bench (Arena dataset).

GPT-6 models write the cleanest, plainest prose and are the least liked for fiction

• LLM judges like GPT-6 Astra. It is first on EQ-Bench creative writing (2176.9) and second on Caliper Bench (87.42) (EQ-Bench data; Caliper Bench).
• Human voters do not. Astra is 40th on Arena creative writing and GPT-6.1 Sol 43rd; without Arena's style control they fall to 70th and 72nd (Arena dataset). Fiction communities describe GPT prose as staccato and same-voiced (r/WritingWithAI).
• Astra is not worth $10 / $50 for writing. GPT-6 Sol at $2 / $10 has the lowest slop on three boards and scores 82.5 on ToneBench at $0.056 per script against Opus 5.5's 85.8 at $1.00 (ToneBench data; AA-PersonalityBench). A vendor guide's answer on Astra for writers is "For most writers, no" (Inkfluence AI).
• For clear business and technical prose GPT leads. In one blind test OpenAI models "consistently produced writing that I found clearer and easier to read" (How I AI). On a German book-writing benchmark GPT-6.1 Sol rated 1382 on literary prose against Opus 5.5's 1325, with overlapping intervals and LLM judges (hermes3000 report).
• GPT-6.1 Terra ($1 / $5) is the open question. It is first on Fiction Bench with the best blind-reader rating (63.5% win rate), and OpenAI describes it as built for "clear, natural writing" (Fiction Bench data; OpenAI model page). But it is 23rd of 87 models on ToneBench (ToneBench data), Every says it "still needs a lot of human steering" (Every), and it is not on Arena. Its Fiction Bench lead overlaps about 30 other models' intervals (arithmetic).

What holds up among the cheaper models

Two points follow. Benchmark-only cases (Kimi K3.1 Pro, GLM-5.3) are weaker than they look, because users and blind readers do not confirm them. And the cheap tier is good enough for drafts a person will edit: on ToneBench "the last five points cost about 175× more" (Bouchard). Open-weight vendors concede that "Open weights lose at the top of the range" (Featherless).

For editing and proofreading, cheap models are close to the flagships

ErrataBench runs every model through OpenRouter on documents seeded with errors. Fix rate and the cost of the model per document, from the raw results file (ErrataBench results):

Argon at low effort is the cheapest route above 93%, and stays so after its price doubles (arithmetic). Revise, which runs the benchmark and sells a proofreading product, names GPT-6 Sol without reasoning as the best value (Revise). A Reddit user's test with 90 planted errors agrees on the order: Opus 5.5 98%, GPT-6.1 Sol 97% and "up to 4x cheaper", Argon 93% (r/WritingWithAI). These tests measure error-catching. For developmental and line editing there is no benchmark on current models, and reviewers split between Fable, Opus 5.5 and the GPT models (Zvi Mowshowitz; Every).

Availability and verified prices on OpenRouter

All 134 current model ids that the research checked are live; the only strong writers missing are the Hemmingway-1 / 1.5 fine-tune and a demo-only model (OpenRouter models API; Hemmingway model card). No leading writer has a free variant. Prices below are the lab's own endpoint on OpenRouter, from the endpoints API (https://openrouter.ai/api/v1/models/<id>/endpoints), which matches each lab's pricing page (Anthropic pricing; OpenAI pricing; Gemini pricing). "Draft" is output price times 135,000 tokens, the output of one 100,000-word draft before input and reasoning tokens (arithmetic).

Scheduled changes, in date order:

OpenRouter's pages show a 50% discount flag on these Gemini and Hy4 endpoints but no end date (Gemini 4 Argon endpoints). Claude Sonnet 4.5 matters because it still has the largest share of roleplay and fiction spend on OpenRouter (10.1%) (OpenRouter rankings). After 30 November it may stay callable through Bedrock or Vertex for a while, as Opus 4.1 has, and then disappear (inference from two earlier retirements) (Opus 4.1 endpoints).

What you pay per word differs from the list price

Hidden reasoning bills as output. At maximum effort, 70% to 86% of the output tokens of every model measured on Artificial Analysis's general test suite are reasoning that the writer never sees. For Opus 5.5 the share falls from 79% at maximum effort to 31% at low (Artificial Analysis: Opus 5.5). That is a general suite, not a writing test. On real short writing tasks run through OpenRouter at default settings, billed output tokens per visible word and the resulting output cost per 1,000 visible words were (researcher's arithmetic on the Slop Index output data) (Slop Index dataset):

So GLM-5.3, at under a quarter of Opus 5.5's list output price, cost the same per visible word in that data, and Kimi K3 cost more than GPT-6 Astra. Lowering reasoning effort would cut the GLM and Kimi figures. On these numbers a 100,000-word draft on Opus 5.5 costs about $5 in output, not $2.70 (arithmetic). Higher effort does buy some quality on ToneBench (median +231 Elo for 4.6 times the cost), but Opus 5.5 scores the same at "xhigh" for $1.00 a script as at maximum for $2.58 (ToneBench thinking page; ToneBench data).

Newer Claude models count more tokens. Anthropic says Claude 4.7 and later use a tokenizer that "produces approximately 30% more tokens for the same text" (Anthropic pricing). So moving from Opus 4.6 ($5 / $25) to Opus 5.5 ($4 / $20) is roughly cost-neutral per word, and moving from Sonnet 4.x ($3 / $15) to Sonnet 5.5 ($2 / $10) saves about 13%, not 33% (arithmetic). Token counts are not comparable across labs either (OpenRouter datasets doc).

The catalogue price of an open-weight model is not the price to budget. OpenRouter's headline figure is one cheap host: Kimi K3 shows $0.192 / $16 while Moonshot's own endpoint is $3 / $15 (Kimi K3 endpoints). The cheapest hosts are often compressed versions of the model, and on one model the same weights scored 90% at the lab's host and 75% at another (So you want to use OpenRouter?). Users advise pinning the lab's own host for open-weight models.

Other costs. OpenRouter says it adds no markup to provider prices (OpenRouter SillyTavern guide); a user guide reports a 5.5% fee on credit purchases (RoboRhythms). Prompt caching cuts the cost of resending a manuscript: 50 replies at a 32K prompt cost $8.84 on Opus 5.5 uncached and $1.32 cached in that guide. Writers on Google's forum report that responses blocked by Gemini's safety filter are still billed, including through OpenRouter (Google AI Developers Forum).

No model sounds human by default

• Every model that the Slop Index ran through the Pangram detector scored 100% AI (Slop Index). On HumanBench no output beat its human reference (HumanBench paper).
• Graphite finds that Opus 5.5 has the word distribution closest to human text (divergence 0.052 against 0.109 for GPT-6 Astra) and still has 2,548 measurable tells: "AI tells change between models and versions, but do not go away" (Graphite Opus 5.5 update).
• A study of nine frontier models found that all use more hard-to-process constructions than human writers, and that Opus 5.5 improved on Opus 5 on 19 of 21 measures (arXiv 2610.29162).
• Prompting moves the result more than the model choice within a tier. One page of style rules made all 21 models in one blind test sound more human, with Argon gaining most (Stop Slop benchmark).
• Claude output carries a statistical text watermark on every platform, which customers cannot turn off on current models (Anthropic watermark docs). OpenAI's watermark is opt-in for API users (The Decoder). Whether Gemini API text is watermarked was not checked.

Where sources disagree

The four kinds of evidence rank the same models in different orders.

None of the four is a clean measure:

• LLM judges changed their verdict in 43% of story pairs when the order was swapped (lechmazur/position_bias), and favoured their own lab's models by up to 7.5 points on ToneBench (ToneBench judges page).
• EQ-Bench is judged by a Claude model and, according to Surge, agreed with expert writers as little as 43% of the time (Surge).
• Arena voters reward length and formatting (Surge on LMArena), and new models' scores fall as votes arrive: GPT-6 Astra went from 22nd to 40th in two months (Arena dataset). Carbon, on about 1,000 votes, may also settle lower (inference).
• Academic work finds lay readers prefer AI prose while trained writers prefer human prose, which places crowd and LLM-judged boards closer to lay taste (arXiv 2510.13939).
• The one expert-writer board, Hemingway-bench, has no GPT-6, Opus 5.5, Fable 5.2 or Gemini 4 rows (Hemingway-bench).

Other open disagreements.

• Whether Claude 5.x writes better or worse than Claude 4.6: writers who want structure and obedience praise 5.5, and those who care about voice call 5.x "hollow" (r/SillyTavernAI).
• Whether Claude sounds human: Graphite says Opus 5.5 is closest to human word use, the Slop Index says it is among the most templated. They measure different things on different kinds of text.
• A widely repeated line that Every's Dan Shipper called Argon "by far better" at writing appears only in a second-hand report and could not be confirmed at source (BigGo). Every's own free text says Argon should be used as an editor, not a ghostwriter (Every).
• Fiction Bench's price column shows cheapest-host prices for Kimi K3.1 Pro, GLM-5.3 and DeepSeek V4.1 Flash, not the lab prices given above (Kimi K3.1 Pro endpoints).

Single-origin and unverified. Fable 5.2's standing rests on six days of data. GPT-6.1 Terra's first place rests on one board with about 120 votes per model. Stop Slop Studio does not say whether its "anonymous" judges are people or models (Stop Slop benchmark). Most "best AI for writing" articles are vendor or affiliate content, and one states that it ran no prose tests (Inkfluence AI).

Unknown. No study has expert human judges ranking the current models against each other on fiction. No benchmark covers developmental or line editing. No independent hands-on prose review of GLM-5.3 or Gemini 4 Carbon was found.

How the search was done

Scale. 15 researchers ran 710 probes and recorded 1,123 sources, of which 784 were opened and read and 339 were seen only in a listing. The run was one leg and stopped at its cap of 15 researchers.

Surfaces and moves. General web search (170 probes); OpenRouter's catalogue, endpoints API, rankings and blog (54); arXiv, Semantic Scholar and OpenReview (51 search probes and 16 citation walks); Reddit through an archive API and Hacker News (48); GitHub (34); Hugging Face datasets (21); YouTube and podcast listings (21); Chinese-language pages (14); and direct walks of benchmark sites. Later researchers switched from searching to opposition search, time slicing to October and November, raw dataset parsing, author pivots, and direct price verification against 148 OpenRouter ids and the labs' pricing pages.

What was not covered.

• The shared web-search budget ran out partway through. Researchers 10 and 12 to 15 worked almost entirely by direct fetch, so angles that depend on search are under-covered.
• Chinese-language sources: price pages and news were read, but no Chinese test of prose quality was found, and Zhihu, Bilibili, 少数派 and most mainland sites refused connections. The case for Kimi, GLM, DeepSeek and Qwen as writers rests on English-language benchmarks.
• Video transcripts: YouTube refused unauthenticated access, so every video (including the Nerdy Novelist's model reviews) was read by title, description and chapter list only.
• X posts were not fetchable and are known only through summaries.
• Also not reached: Every's paywalled reviews, novelist Discords, fan-fiction forums, and arena.ai's live pages (its official dataset was read instead).

Searched and found empty. An OpenRouter guide or category for writing quality; cost columns on Mazur, EQ-Bench, Hemingway-bench, Vulsar and Caliper; a post-launch critique of Argon's prose in the press; major general-press comparisons of current models for writing; current-model benchmarks for poetry, screenwriting or translation.

Saturation. The search is not saturated. The last five researchers still found 49% to 86% new sources among their results, and 224 recorded leads remain open. The most useful next steps are transcripts of the novelist videos, Chinese-language prose tests, a re-read of Arena and Fiction Bench once Fable 5.2, Carbon and Terra have more votes, and a price re-check after 1 December.

Full source catalogue: sources.md in this folder.

Add a small set of short, permissively licensed process skills to a personal skills repo: handoff, to-questionnaire and teach from mattpocock/skills, strategy-red-team and pre-mortem from phuryn/pm-skills, decide from makerskills, source-verification from the journalism set, two skill-maintenance skills (writing-for-agents, Intercom's skill-review), and, if you evaluate AI output or give agents mail and browser tools, the evals-skills set and 1Password's security-awareness. The evidence has clear limits. No third-party test exists for any meeting, spec, stakeholder-update, roadmap, sprint or tutoring skill. The best controlled studies show that skills written for a task help (+16.6 points) and that public skills taken off a leaderboard mostly do nothing (92 of 98). Install counts and stars are not usable as quality signals. Several attractive skills have no licence or forbid copying. Scanners cannot certify a third-party skill as safe. So the ranking below rests on usefulness, a read of the skill text, licence and portability, and it says where each of those is thin. All counts were read on 2026-11-15.

Ranked recommendation: what to add

Rank reflects usefulness, quality of the skill text as read, licence, and how little must change to use it. "Body read" means a researcher read the SKILL.md itself.

Two candidates are a different kind of addition, a plugin to install and not a file to copy:

• last30days (mvanhorn/last30days-skill, 68k stars, MIT, on the Trail of Bits list) searches what people said in the last 30 days. It adds recency beside deep research. Its folder is 178 files and 18 MB, it installs helper CLIs on first run, and it takes up to 15 optional API keys or browser cookies. Review credentials before use.
• marketingskills (coreyhaines31/marketingskills, 60.5k stars, MIT) is the only large non-engineering collection that is on 12 curated lists, is a marketplace Trail of Bits approves, and whose copywriting skill placed first of six (9.0) in a blind head-to-head (Happycapy docs arena). That arena has no no-skill baseline and one run. Add it only if you do marketing work.

Second tier: add when the need is concrete

What not to add, and why

• Not liftable. gstack skills are 6,000–8,500 words each and every one runs a setup binary and shared preamble. Compound Engineering skills share a config file and "non-optional" references; rewrite ideas from it (the seven sentence tests in ce-noslop are a good one). mattpocock's engineering skills need a configured issue tracker.
• Hooks you would have to audit. planning-with-files v3.28.2 registers five lifecycle hooks, one on every Read, Write, Edit, Bash, Glob and Grep call. Trail of Bits approved only a pinned 1.0.0 copy (manifest).
• Tested and not better than no skill. No data-visualisation skill beat the no-skill baseline in OpenSkillEval (visual-explainer, tufte-data-viz and visualize ranged from 0 to −0.28), and report-generator skills gained under 0.04 (paper). Three Obsidian skills gave no recall gain over plain file tools at 1.7 to 6.1 times the tokens (Agent Skills Hub test).
• Persona and framework restatements. The large "hundreds of skills" collections (alirezarezvani, OneWave-AI, borghei) cover every need by name; no body was judged. Practitioners who pruned their sets deleted exactly this kind first (Market Correct).
• Style skills with high stars. karpathy-guidelines is four coding rules that belong in a CLAUDE.md (repo). HN commenters report that i-have-adhd's style holds for "a few turns at most" (thread).
• Vendor skills are mostly manuals for the vendor's product. Of the 317 plugins in the official manifest, the largest sets (NVIDIA 452 SKILL.md files, Salesforce 437, AWS 204, Adobe 200) need the vendor's MCP server, CLI or key (marketplace.json). The Google Workspace skills have 3.5M installs and one that was read is a 40-line wrapper around one command (SKILL.md).

Benchmarks: task-written skills help, public skills mostly do not, and meeting, spec, planning and tutoring skills have no third-party test

• Curated skills help. SkillsBench (87 tasks, 18 model-harness configurations) reports that curated skills raise the pass rate from 33.9% to 50.5%, with Office and White Collar tasks at +12.6 points. Skills the agent writes for itself from its own knowledge score below no skill (−8.1 to −11.5 points). "Comprehensive" skills gain +0.7 points against +19.0 for compact ones (arXiv 2602.12670).
• Public skills mostly do nothing. SWE-Skills-Bench took 98 public software skills from a marketplace leaderboard: 92 of 98 had no significant effect, the mean gain was +0.34 points, and two were harmful (arXiv 2603.15401). A web-development benchmark found a net loss of 1.3 to 4.2 points and 72% to 394% more tokens, and advises caution with popularity rankings (arXiv 2608.23067).
• The gap is explained by provenance. SkillsBench skills were written by the task authors for their tasks; the other two used public skills. When the agent must find skills in a pool of 34k real ones, Claude Opus 4.6 scores 38.4 against 35.4 with no skills and 55.4 with curated skills forced (arXiv 2604.04323).
• What helps is knowledge the model lacks. A paired test with an independent grader found skills carrying current or house-specific knowledge gained up to +90 points, and skills restating general knowledge gained nothing or lost (BuidlGuidl). One talk reports the model already met the general idea of 70% of a knowledge skill's content without the skill (AI Engineer talk).
• Skills often do not fire. Unforced activation measured 41% in Tessl's sample (Tessl), about 48% in OpenSkillEval (arXiv 2605.23657), and in Vercel's evals "In 56% of eval cases, the skill was never invoked" (Vercel).
• More skills cost something. Pass rate fell by up to 21% going from a small helpful set to a 202-skill library (arXiv 2605.24050). A same-job skill installed beside another lowers use of the intended one by 19.9 points (arXiv 2610.11647). This matters for two tutoring skills, or two humanizers, in the same repo.
• Results do not carry across models. The same skill helps some model-harness pairs and hurts others on 36.78% of SkillsBench tasks (arXiv 2610.08875).
• What is missing. No third-party paired test was found for any meeting-summary, spec, stakeholder-update, roadmap, sprint-planning or tutoring skill. The nearest independent test is Happycapy's head-to-head arena, which scored doc-coauthoring 8.0 and internal-comms 7.3 with no baseline (arena).

One small result supports the kind of skill ranked highest here. An A/B test of nine skill ideas found grill gave the largest gain (+75 and +58 on two models) while four did nothing and loaded in 0 of 6 natural-prompt runs; the nine were the tester's own rewrites, not the upstream files (dev.to).

Install counts and stars do not identify good skills

• skills.sh installs are repo-level. Whole repos install as a block: every mattpocock skill sits between 0.2M and 1.6M (skills.sh). The inference that some counts are automated comes from the researcher, not from a source: six near-identical "superpowers" look-alike repos each show 2–4M installs for the same image and video skills (skills.sh).
• Installs and stars contradict each other. design-mobile-apps has 1.4M installs and tops trending while its GitHub repo has 35 stars (skills.sh, repo). One publisher's sales skills show 17k–34k installs each from repos with 2–3 stars (listing, repo). In the other direction, intercom/2x-skills has 279 stars and 63 installs, because vendors that ship through their own marketplace are not counted.
• Other registries show the parent repo's stars against every skill (MCP Market, agentskill.sh).
• Stars can be gamed or stale. About ten identical repos with roughly 380 stars each from throwaway accounts appeared under the agent-skills topic on one day (GitHub search). Lists print star snapshots months old.
• Where popularity was tested against quality, it lost. In a humanizer comparison blader/humanizer (67.2k stars) scored 3.0 of 5 and sloptrim (421 stars) scored 4.5 (Agent Skills Hub); that test is one run, model-judged, by a directory operator. A study of 2.19 million skill adoptions found that stars do not identify the repos others copy from (arXiv 2610.11169).
• Cross-listing is weak too. A CSV skill with 404 stars sits on 12 curated lists because early entries are copied from list to list (repo). Practitioners use commit count as a better signal: "About a third of all those skills only have one commit" (Hamel Husain).

Licences rule out several attractive skills

• No licence, so no right to copy: aiwithremy/claude-skills-llm-council (3.1k stars, one push), backnotprop/bro (its clean-room second-opinion skill is 349 words and can be rewritten), coleam00/excalidraw-diagram-skill, readwiseio/readwise-skills, obra/the-elements-of-style, vercel-labs/agent-skills.
• Restrictive: dbskill forbids redistribution since 2026-11-02. deanpeters/Product-Manager-Skills is CC BY-NC-SA 4.0. Trail of Bits' own skills, including skill-extractor, are CC-BY-SA-4.0, which is share-alike.
• Mixed signals: jwynia presentation-design says MIT in frontmatter but the repo has no licence file.
• The skills in the ranked table are MIT, CC0 or Apache-2.0 except evals-skills, whose licence this run did not record.

Security: read every file and pin the version; a scan is not a clearance

• Flawed and malicious skills are common in open registries. Snyk found 36.82% of 3,984 skills from ClawHub and skills.sh had a flaw and 76 carried confirmed malicious payloads (Snyk). A security vendor reports taking down 100 malicious skills from skills.sh with 1.7 million total downloads (AI Engineer talk).
• Scanners miss attacks. Trail of Bits got four malicious skills past ClawHub's scan, Cisco's scanner and all three skills.sh scanners, and concludes "No amount of scanning or LLM analysis can reliably detect malicious content in agent skills" (Trail of Bits). A skill that fetched its payload at run time passed Anthropic's admin scanning (PromptArmor).
• Scanners also over-flag. Marketplace scanners label up to 46.8% of skills malicious; 0.52% of 238,180 stay suspicious after repository context is checked (arXiv 2603.16572).
• Ordinary skills loosen oversight. 5.8% of 1.53 million skills tell the agent to proceed without approval or pass flags such as --yes (arXiv 2610.37734).
• What this does not show. Nearly all prevalence data is for ClawHub, not for Claude Code plugin marketplaces. No malicious behaviour was reported for any skill recommended here.
• Practical rule. Follow the OWASP checklist: verify the publisher, read the full package including scripts, pin versions and hashes, rescan after updates (OWASP). The Trail of Bits list is the only human review found; it checks safety and structure, not effect (CLAUDE.md). Most of the top-ranked skills are plain Markdown with no scripts or hooks, which is why reading them in full is cheap. One caveat on a trusted name: superpowers' brainstorming companion loads a remote logo as version telemetry unless disabled (repo).

Many skills only work inside their author's setup

A full read split the candidates three ways. Standalone: mattpocock's productivity skills, phuryn/pm-skills, single-skill repos. Needs a path or name edit: makerskills, superpowers brainstorming (hard-wired to sibling skills, and its description says "You MUST use this before any creative work", which would over-trigger in a mixed repo) (SKILL.md). Not liftable: gstack, Compound Engineering, last30days. Personal-productivity skills are the worst case: Chris Blattman's pre-meeting-brief hard-codes MCP tool names and a local WhatsApp database. The most-wanted personal skills (daily planning, morning brief, "what's up" catch-up) exist mainly as private company or creator skills with no public repo, for example Figma's internal set of 31 skills used by 60–70 PMs (How I AI). One practitioner puts the general point this way: "Most of mine are throwaway, full of paths and assets specific to my machine" (Tim Hopper).

Skills also go stale. One well-starred set was archived in September 2026 because its author called the skills obsolete for newer models (rjs/shaping-skills).

Where sources disagree

• Size of the benefit. +16.6 points (SkillsBench) against +0.34 (SWE-Skills-Bench) and a net loss (web development). A review argues the SkillsBench figure "may be an upper bound" (Evaluating Evals). A hobby eval reports 92% of 123 skills beat the bare model, using 2–3 prompts and a rubric that rewards format (jensen-srp).
• "40 of 47 skills made output worse" is repeated on many pages and traces to one informal Substack test with undescribed metrics (Shcheglov). Do not rely on it.
• Slides. OpenSkillEval puts frontend-slides below no skill (3.96 against 4.04); Happycapy ranks it first (8.2), with no baseline and the operator listed as publisher of the winner (slides arena).
• Superpowers. Praised by named practitioners (Simon Willison) and criticised as slow, with 8 to 12 approval gates per feature (contextfirst). Agreement only that brainstorming is the part people keep.
• Use published skills or write your own. Several practitioners avoid published skills ("More and more I'm avoiding skills that other people are publishing", Chris Fonnesbeck) while still recommending grill-me. The split follows type: process skills (interview, plan, review, hand off) are endorsed; taste and reference skills are the ones people drop.
• Humanizers. One author's test found every rewrite of an AI draft still read as AI to a commercial detector unless built from the writer's own notes (work-humanizer). Single-author claim, not checked.
• Counts differ everywhere. Self-reported skill counts do not match repo trees, and the same repo's stars differ by tens of thousands between sources. Figures here are from GitHub or the registry page on 2026-11-15 where the notes say so.
• Unknown. Whether any recommended skill improves your output. Whether install counts are inflated by automation. What the bodies of most second-tier skills say; many were read as frontmatter only.

How the search was done

Fifteen researchers ran 896 probes in one leg and recorded 3,497 sources, of which 1,151 were opened and read; the other 2,346 were seen only in a listing or search result (figures from sources.md). The run ended because it reached its cap of 15 researchers, not because the search ran dry.

Surfaces and moves: Anthropic's own repos, docs and blog; GitHub repository and code search by stars, topic, vendor organisation and creation date; 46 curated lists with a cross-list frequency count; registries (skills.sh, ClawHub, claudemarketplaces, Tessl and others); Hacker News and practitioner blogs; arXiv and Semantic Scholar for benchmarks and security audits; Chinese, Japanese, Korean and other non-English sources; vendor-published skills from the official manifest; full-body reads of the short list; two domain sweeps over about 50 knowledge-work terms; talks, podcasts and newsletters; reviewed and eval-scored sets; personal repos of named practitioners; and skills created September to November 2026.

The search is not saturated. The share of each researcher's reported results that were new stayed high to the end: 92%, 88% and 70% for the last three, and 55% at the lowest (the depth-read researcher). The vendor researcher and the recent-skills researcher both state their veins were still producing when they stopped. 487 leads remain open.

What could not be reached: Reddit and X (no thread was read; sentiment from them is second-hand), skillsmp.com (403 and rate limits), Gitee, lobste.rs, and paywalled "I installed N and kept 3" posts. The shared web-search budget ran out part-way, and the GitHub API quota was exhausted more than once, which cut short the vendor, talks, authors and recent-skills researchers.

Searched and found empty: public skill repos from Anthropic staff; any third-party paired test of a meeting, spec, stakeholder, roadmap, sprint or tutoring skill; an approved-skills list from a second security firm; official skills from meeting-notes vendors (Granola, Fireflies, Otter, Gong); adopted tool-neutral skills for speechwriting, copyediting, calendar scheduling, newsletter writing, personal journalling or new-hire onboarding; an attorney endorsement of any community legal skill; German, Portuguese or Italian community skill repos of note.

Open leads most likely to change the ranking: full-body reads of the second-tier skills (asb-skills, the remaining writer/skills and microsoft/cat-agent-skills entries, cursor's pstack, openai's data-analytics); the per-skill results behind the Agent Skills Hub humanizer and slide tests; individual skill pages on skills.every.to (editorial checks, Slack catch-up); and more vendor organisations by code search.

No exact equivalent was found. The video is Lydia Hallie's 17:31 talk from the AI Engineer CODE Summit, published Nov 11 2026: Anthropic's own Claude Code team telling experienced engineers which habits to drop now that the models are stronger, and what replaced each one (talk page). Anthropic has published nothing like it for product managers or people managers, and neither has any other tool maker. The nearest talk for PMs is Dan Cary's "Designing with Claude" (an Anthropic PM, 28 min). The nearest for managers is Fiona Fung's "Running an AI-native engineering org" (Anthropic, 29 min). One talk is closer in shape than either but nobody on this run read its content: Amandeep Khurana's "When AI Joins the Team" (Anthropic, 19 min). The researchers watched no video. The evidence level in each row below says what was read.

Evidence levels:

• Transcript: a full transcript was read.
• Write-up: the organiser's or publisher's article about the talk was read, not the talk.
• Summary: a third-party machine summary was read. These can contain errors.
• Description only: only the uploader's description and chapter titles were read.

What to watch if you are a product manager

What to watch if you manage people or teams

The source talk is Anthropic telling experienced engineers what to stop doing

The video is "Rethinking Claude Code Best Practices — Lydia Hallie, Anthropic" on the AI Engineer channel. It had 615,663 views four days after upload (YouTube). Hallie is on Anthropic's Claude Code team (speaker page). The event describes itself as "Built for practitioners" and has no session for PMs or managers (AIE CODE 2026).

Her thesis is that most of what people added to their Claude Code setups over the past year now holds the model back. Each duty the developer used to perform has a new owner: steering goes to the model, command approval to auto mode, hard limits to deny rules, and output checks to tests, hooks and CI (event write-up). The full transcript is public (transcript). Its only mention of managers is "we've now essentially become managers for dozens of sessions". It does not mention product managers at all.

Five things make it valuable, and a true equivalent would need all five:

1. It is the tool maker's own current guidance.
2. It is a reset for experienced users, not an introduction.
3. It gives one mental model and a few decision rules, not a tips list.
4. It is short and built on running examples.
5. It recasts the user's job as managing delegated work.

Nothing found for PMs or managers has all five.

Anthropic has not made this talk for PMs or managers

One researcher listed every 2026 video on the Claude channel (203), every video on the Anthropic channel (145), all 147 Code with Claude 2026 session pages, about 125 webinar pages and all 18 developer-blog posts. None is aimed at product managers or people managers, apart from one three-minute video (Claude channel; site map). That video, "How Anthropic uses Claude in Product Management", is a testimonial with two use cases (video). Anthropic's role-based Cowork webinars cover seven functions and product is not one of them (webinar hub).

Anthropic's own product chief says the answer is not settled. At the Lenny and Friends Summit, Ami Vora said: "We don't know the answer. We don't think anyone knows the answer." Stripe's Katie Dill said at the same event: "The best practices have not been written yet." (summit recap). That 29-minute panel, "Why Claude can't be your PM (yet)", is a discussion of why the PM role persists and has no how-to (video; summary).

Nobody has responded to the source talk for a non-engineer audience either. 197 of its 216 comments were scanned and none asks for a PM or manager version (YouTube).

For PMs, the Dan Cary talk is the closest thing that was actually read

Cary says in the talk: "I am a product manager. I lead a product within Anthropic Labs." His stop-doing list is explicit: "we did not write a PRD in advance. We had zero vision docs. We had zero KR meetings." He says prototypes have "effectively replaced PRDs for me as a product manager that's been writing them for almost two decades now". He closes with three things to try, starting with "next time you're going to write a PRD, skip it" (transcript).

Its limit is that it is a product-process talk about a three-person team building Claude Design. It does not cover how to set up or supervise Claude Code or Cowork, and it is six months old (session page).

The other ranked PM talks each match two or three of the five properties:

• Mechaber shows a shared repo of 31 skills at Figma. The pruning advice is real but brief: "As models improve, he has been removing instructions that no longer earn their place." He now works mostly in Codex (write-up). A 17:42 conference version exists, read at description level only (Config 2026).
• Charles argues that "speed is a systems problem" and tells PMs to "be as lazy as engineers". All the tools he shows are Ramp's own, and he gives no stop list (summary).
• Vo says to stop feature-and-date roadmaps and to stop treating backlog clearance as progress (summary). It has no agent guidance.
• Brouwer says to stop fixing a bad output by hand and to fix the workflow that produced it. Claude is named once (write-up).
• Simons says to stop writing specs and tickets and to put a built thing in front of customers. About a third of the write-up is Bolt customer stories (write-up).

For managers, the Fiona Fung talk is the best-evidenced match

Fung manages Anthropic's Claude Code and Cowork team. Her talk has the source talk's structure applied to management. Processes existed to protect scarce engineering time, and the bottleneck has moved to verification and review. She covers planning, context gathering, code review and team makeup, and gives one test: "always ask yourself is it still serving its purpose". Her homework is to pick your noisiest workflow and ask that question (transcript; written version).

Its limits: it addresses engineering managers, it is six months old, and PMs appear only in passing. Prefer the talk to her 98-minute Lenny's Podcast interview, whose top comment calls it "super surface-level and generic" (interview).

Clauson's talk is the nearest structural twin of the source talk from any other vendor. Each increase in sessions per developer came from removing one reason an agent waited for a person. On review he says: "we've stopped reviewing code a long time ago... What we're responsible for is the review record." About 3 of its 18 minutes are product pitch, and its numbers are self-reported (transcript page). It has no separate video yet.

Agrawal describes a leader with 15 recurring meetings who assembles context for 20 minutes, hands off around 5 pm and reviews in the morning (talk page). Carson caps review at three rounds and says "The decision to ship belongs to a person" (talk page).

The Khurana talk is the one to check first, and its content is unread

It is an Anthropic speaker on a product-management conference stage at the source talk's length. Its description opens with a reset: he "thought he was an advanced AI user", then joined Anthropic and kept hearing "we use Claude differently here". It offers one model, four stages of handing work to Claude (YouTube).

Three things count against ranking it. No transcript, summary or attendee write-up could be found by any route. The description names no Claude Code or Cowork practice and no list of things to stop. It had about 1,700 views after 26 days, so there is no reception evidence. The speaker leads Forward Deployed Engineering and had been at Anthropic three months (YouTube). It costs 19 minutes to find out whether it is the answer.

The nearest thing to the source talk's content for non-engineers is written

Anthropic's most direct habit reset for non-engineers is an article, not a video. "Using Claude Opus 5.5 as your daily driver for knowledge work" (Nov 12 2026) says each new model is a reason "to drop prompting habits built for older models". It lists habits to drop, such as "Delete 'think hard' and 'think carefully'", and habits to adopt, such as "Say what done looks like in your first message" (article). It never names PMs.

Three other written pieces carry the argument:

• Paweł Huryn makes the reset case for PMs most directly: "If your CLAUDE.md keeps growing, you're writing for a weaker model than the one you're running" (post).
• Hilary Gridley's "is AI giving you spaghetti brain?" has the source talk's shape for managers: one model, a few rules, named habits to stop (essay). She has since joined Anthropic "leading AI Fluency" (post). The essay predates that role.
• Felix Rieseberg of Anthropic wrote "How to be more ambitious" for office workers (article). A 49-minute interview version has about 496,000 views, read at description level only (video).

The best-known PM tutorials teach habits the source talk retires

Most PM video on Claude Code is build-up: operating systems, dozens of skills, many agents. Carl Vellotti's widely shared episode teaches a status line to watch context, subagents to save context, and keyword hooks to trigger skills (summary). Teresa Torres's written safety guide tells non-technical users to read every diff and treat every permission prompt as a checkpoint (guide). These are the practices the source talk says have moved to the model and the environment. Watch them as introductions, not as current best practice.

Where sources disagree

Later readings that overturned earlier notes. Where notes conflicted, this report follows the later content-level reading.

• An earlier shortlist ranked the Khurana talk first for PMs on its description alone. The later content check found no way to read it and moved it out of the ranking. Cary's transcript-read talk takes first place.
• Camille Fournier's talk was first recorded as login-gated and unrankable. It is public on YouTube (video).
• The Simons recording was first recorded as gated. It is public on YouTube (video).
• A second Hilary Gridley episode of How I AI, dated Sep 21 2026, does not exist on the show's archive (archive).
• The source talk's transcript was first recorded as not obtained. It is public, and it confirms the talk's figures.
• "Product management on the AI exponential" was written by Cat Wu in March 2026. Its URL now redirects to a different article by Jess Yan (URL). Wu has left Anthropic for OpenAI (The Next Web).
• Geoff Charles's talk is 19:31 on the playlist, not 18:33 as a summary page had it (playlist).

Disagreements between the speakers. The practitioner talks disagree on the questions a PM most needs answered, which supports the finding that no settled guidance exists.

• Say yes or say no. Carlos Gonzalez de Villaumbrosia says the PM's job is now "to make yes cheap" (video). Clauson says: "The problem with saying yes all the time is that you might lose your product shape" (transcript page).
• Build the factory or avoid it. Charles tells PMs to build the software factory. Karri Saarinen of Linear warned at the same event that "output is not the product" (summary).
• How many agents. The source talk ends at one person managing dozens of sessions. Gridley tells managers never to work on more than two things at once (essay).
• Fewer instructions or more. Anthropic staff say the newest models need "fewer hard constraints, more context, and fewer instructions overall" (Willison write-up). Huryn's own August post says Opus 5 needed more explicit rules (post).
• Whether the PM role survives. Quinn Slack of Amp says "We have no PMs" (talk page). Marty Cagan says the delivery-coordinating PM shrinks while discovery remains (article).

A current of opposition. Torres and Petra Wille argue that "Building with agents needs more coordination, not less" (episode). Pavel Samsonov rejects the manager-of-agents frame outright: "It is a SaaS product" (post).

Unknown. Whether any ranked talk is as clear on video as its write-up suggests. Whether the Khurana talk says what to stop doing.

How the search was done

Fifteen researchers ran 752 probes and recorded 1,496 sources. 710 were opened and read. The other 786 were seen only in a listing. The full catalogue is in sources.md beside this report.

The moves were:

• identifying the video;
• walking Anthropic's own channels, webinars and event pages in full;
• PM podcasts and shows;
• conference recordings;
• manager and course material;
• community lists and comment threads;
• the talk's thesis in PM writing;
• podcast and video indexes queried directly;
• other vendors and the opposition;
• seven non-English languages;
• a final pass on open leads.

Four limits matter:

1. No video was watched. YouTube's player and captions required sign-in from the research environment. Every content judgement rests on transcripts, write-ups, summaries or descriptions, as labelled.
2. Web search ran out early. The shared budget of 200 searches was spent partway through the fifth researcher. Later researchers worked by walking sites and querying indexes directly, which cannot find a talk on a site nobody thought to walk.
3. Reddit, LinkedIn and X could not be read. Mainland Chinese sites refused connections. These are gaps, not evidence of absence.
4. Reception is thin. Outside Lenny's channel, the product-conference recordings have 300 to 600 views, and Hacker News has no discussion of any of them.

Searched and found empty:

• a PM or manager session at any AI Engineer event;
• a product-team edition of Anthropic's role webinars;
• a talk by Lydia Hallie for non-developers;
• any PM-facing response to the source talk in its first four days;
• a matching talk from OpenAI, Cursor, GitHub, Google, Figma, Notion, Linear or Atlassian;
• a matching talk in Japanese, Korean, Chinese, French, German, Spanish or Portuguese.

The search is saturated on the central question and not on the edges. Anthropic's own surfaces were listed in full, and the answer did not change across the last eight researchers. The run stopped at its cap of 15 researchers, not because results dried up. Three of the last four researchers still reported over half their results as new (83%, 86% and 54%; the fourth, a content check, 27%). The Japanese search was still returning new slide decks when it was cut off.

Open leads, in order of value:

1. Read the Khurana talk's captions in a signed-in browser.
2. Watch the short ranked talks whose content was read only as write-ups or summaries.
3. Check for a recording of Hendrik Hemken's "Claude Code for Product Managers" session, scheduled for Nov 17 2026 in Cologne (session page).
4. Check for a recording of Anthropic's code-review webinar by a Claude Code PM, live Nov 18 2026 (page).
5. Watch for anything Hilary Gridley publishes for managers in her Anthropic role.