「aims」の検索結果

Write a product listing for an overseas marketplace or store, localised for the buyer rather than translated from Chinese: title, bullet points, description, search terms and the compliance claims that must or must not appear. Use when asked 帮我写亚马逊 listing, 跨境电商标题, write a product listing for Amazon, TikTok Shop, Shopee, Lazada, Etsy or Shopify, or localise my listing for the US, UK, Germany or Japan. Produces the listing in the target language within the platform's field limits, a keyword map, a compliance check of every claim, and a list of images the listing needs.

日本語の概要は準備中です。原文の説明を表示しています。

Handle a dispute with a contractor — unfinished work, poor quality, overcharging, or a no-show — with a path that protects your money and your options. Use when asked to deal with a contractor dispute, my contractor did bad work / won't finish / overcharged, or how to get a builder to fix their work. Produces a read on your position (contract, payments, evidence), a firm-but-professional communication and demand path, documentation and payment-leverage guidance, and escalation options (mediation, licensing board, chargeback, small claims) — flagging that it's not legal advice.

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Produce a CV shaped for one target company and role: its applicant tracking system, the country's conventions, the sector's norms, its published values and the job ad, with every choice explained. Use when asked to write my CV for a specific company, tailor my CV to Google, Amazon or any named employer, make my CV fit this company's format, or turn my experience into a CV for this job. Produces the tailored CV, a short format rationale citing public sources with dates, and a list of claims the person must be ready to defend. Never invents experience or insider knowledge.

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Triage an incoming insurance claim: check the coverage trigger against policy wording, band severity and complexity, screen for fraud indicators, set a first-pass reserve range, and route with an SLA. Use when asked to triage a claim, review a first notice of loss (FNOL), assess a new claim, decide fast-track vs adjuster routing, or screen a claim for SIU referral. Produces a structured triage note with coverage view, severity band, indicator screen, reserve range, and a routing recommendation.

日本語の概要は準備中です。原文の説明を表示しています。

Check AI output before you trust or use it — where it's likely wrong, what to verify, and how to catch confident-sounding errors. Use when asked can I trust this AI answer, how do I verify what AI told me, fact-check this AI output, or is this AI response reliable. Produces a risk read on the specific output (the claims most likely to be wrong or made up), the parts that need independent verification vs the parts that are low-risk, how to actually verify each, the tells of AI hallucination and overconfidence, and a habit for building verification into your AI use — because AI is confidently wrong often enough that unchecked trust is a real risk.

日本語の概要は準備中です。原文の説明を表示しています。

Records visual proof while testing UI behavior — the agent tests the app hands-on via computer use while a screen recording with structured test/assertion annotations captures the session — then posts the video and a results summary to the PR and tracker issue. Use whenever a change needs verifiable evidence that it works, instead of prose claims — including headless environments (scripted screenshots and probes) and non-UI changes (measured numbers, output pairs).

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Verifies truthfulness, accuracy, and link integrity of content before publishing. Catches fabricated statistics, dead URLs, misattributed sources, and company claims that contradict the brand DNA.

日本語の概要は準備中です。原文の説明を表示しています。

Audit claims that work is done or shipped against the diff or repo. Use when: asked whether something really got done, even if it looks obvious.

日本語の概要は準備中です。原文の説明を表示しています。

Freshly judge whether a finished change and its claims meet original acceptance: PASS, FAIL or NOT_PROVEN. Use when: asked for a go/no-go, sign-off or independent verdict.

日本語の概要は準備中です。原文の説明を表示しています。

Periodic trust sweep of persistent memory and durable knowledge notes - re-verifies environment-dependent claims against the live environment, stamps last_verified + confidence, and proposes archiving drifted entries

日本語の概要は準備中です。原文の説明を表示しています。

Review generated or changed documentation before it ships — READMEs, API references, docstrings, PHPDoc/JSDoc, changelogs, tutorials, and doc sites. Best used reactively after an agent writes or edits docs, after code changes documented behavior, or before publishing docs. Use when the user says 'review the docs', 'is this documentation accurate', 'update the docs', 'write a README', 'document this API', 'add a docstring', or 'add a changelog entry'. Core job: verify every referenced function, flag, endpoint, config key, and code sample against the source; catch docs-vs-code drift; strip filler and unverifiable claims. DO NOT USE for production code review (use clean-code-guard), test review (use test-guard), marketing copy or blog posts, prose style editing of non-technical writing, or documentation site theming.

日本語の概要は準備中です。原文の説明を表示しています。

Use when revising an AISTATS paper for concise AI-statistics framing, theorem-and-experiment clarity, 8-page two-column compression, double-blind wording, reproducibility clarity, statistically careful claims, and assumption-labeling discipline that survives statistician reviewers.

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Use when revising an ACM CCS paper for a precise threat model, calibrated attack/defense claims, 12-page ACM sigconf compression, double-blind wording, adaptive-evaluation honesty, and the security house style that survives an adversarial SIGSAC program committee.

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Use when revising an ACL paper for computational-linguistics house style, covering task-first framing, linguistic examples tied to quantitative error analysis, scoping language claims to tested languages, LLM-era claim discipline, anonymous self-reference, Limitations prose, and compressing into the 8-page or 4-page ACL format.

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Use when revising an AAMAS paper so the interaction contribution leads the first page, the solution concept or incentive property is named, the game and agents are legible, claims are paired with proofs or multiagent experiments, the argument fits eight two-column pages plus references, and the wording stays double-blind.

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Use when packaging AAMAS multiagent code, environments, opponent and population sets, random seeds, game definitions, and logs as anonymous supplementary evidence or a public post-acceptance release, even without a separate artifact badge, so that game-theory and MARL reviewers can inspect and re-run the interaction claims.

日本語の概要は準備中です。原文の説明を表示しています。

Turn TikTok, Instagram, YouTube, Facebook, X, LinkedIn, Reddit, or Rumble transcripts into timestamped hooks, claims, objections, proof, calls to action, sponsorship signals, and reusable content atoms. Use directly for transcript analysis or as support for creator, competitor, trend, demand, and repurposing work.

日本語の概要は準備中です。原文の説明を表示しています。

Write a short-form video ad from an evidence-backed angle and the selected template's actual recipe. Reuses or fetches ad-angle-miner, preserves the chosen angle, plans words and visuals together, checks product claims and production fit, and delivers the strongest script with useful alternatives. Use before video production and for script rewrites. Customer language is one input alongside audience, promise, product proof, offer and format.

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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.

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Render a designed 'value prop' video from a config — 3-5 noun-phrase benefit claims (<=4 words each) revealed sequentially over per-SKU product visuals, one crisp editorial frame per claim (hook sticker -> N claim beats -> brand end card). Deterministic PIL/HTML beat renderer frame-stepped via Playwright and encoded with FFmpeg, sound-off legible, hard cuts, uniform pacing. FREE (no paid calls); music is added separately (create-music-elevenlabs). Use for the value-prop format.

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Research competitors, analyze their messaging, and generate a positioning document with category definition, differentiation claims, value propositions, and proof points. Chains web research, competitor site analysis, and review mining to produce a positioning doc ready for website copy and sales deck use. Use when a product marketing team needs to define or refresh positioning ahead of a launch, rebrand, or competitive shift.

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Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.

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Performs subgroup and heterogeneous treatment effect (HTE) analyses for clinical trials. Covers Mantel-Haenszel pooling, Breslow-Day, interaction tests in regression, RERI for additive interaction, modern data-adaptive HTE methods (STEPP, SIDES, causal forests, X/R-learners), Bayesian shrinkage (Dixon-Simon, MAP, EXNEX), graphical multiplicity (Bretz-Maurer), and credibility frameworks (Sun BMJ, EMA 2019). Use when analyzing treatment effects across patient subgroups for regulatory submissions or precision-medicine claims.

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Prove that a command-line tool actually did what it said, instead of trusting its exit code and its output. Snapshots the filesystem before and after, names the expected consequence in advance, and returns one of four verdicts with what could not be seen. Use after running a build, a migration, a scaffolder, a formatter, a codegen step, or an AI coding CLI; when a command printed success and you are about to report "done"; when you are about to write "exit code 0, so it worked"; or when a tool claims it edited files. Needs nothing installed.

日本語の概要は準備中です。原文の説明を表示しています。