Results for “reads”

Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.

Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.

Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.

Reference for MariaDB information functions, covering session and server metadata such as LAST_INSERT_ID, ROW_COUNT, FOUND_ROWS, USER, and VERSION, with usage guidance for writing SQL that reads auto-increment IDs, row counts, and connection details.

Runs a six-phase research workflow that turns unfamiliar domains, source bundles, or collected material into publish-ready output. Use when users ask in any language to research, study, deep-dive, compile sources, synthesize unfamiliar material, or turn a source bundle into a coherent reference. Not for quick lookups or single-file reads.

Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.

Searches and reads X (Twitter): profiles, timelines, mentions, followers, tweet search, trends, lists, communities, and Spaces. Publishes posts, likes/unlikes tweets, and follows/unfollows users after the user completes OAuth in the browser. Use when the user asks for Twitter/X research, social listening, posting, or account interactions without sharing account passwords.

Searches and reads X (Twitter): profiles, timelines, mentions, followers, tweet search, trends, lists, communities, and Spaces. Publishes posts, likes/unlikes tweets, and follows/unfollows users after the user completes OAuth in the browser. Use when the user asks about Twitter/X data, social listening, posting, or interacting with tweets/users without sharing account passwords.

Discover what bundled skills exist in this mimir install and what each one does. Use when you're not sure if a capability is already covered by a skill ("can I X?", "is there a skill for Y?", "what skills do I have?"). Reads SKILL.md frontmatter from each skill directory and surfaces names + descriptions.

Research Twitter/X profiles, tweets, and trends, then take approved engagement and posting follow-through actions through the AIsa relay. Use when: the user needs Twitter/X research plus likes, follows, unfollows, posting, or post-action follow-through without sharing passwords. Supports relay-based reads, OAuth-approved writes, and media-capable posting flows.

Generates CVE request packages from pentest findings. Reads cve-candidates.json (auto-generated at pentest completion) or findings.json directly, then produces for each qualifying vulnerability: MITRE CVE form data, GitHub Security Advisory draft, full disclosure report, and vendor notification email. Invoke manually after a pentest engagement when you have true-positive findings that warrant CVE IDs.

Skeptical pushback on visual placement — both density and quality. Reads the annotated outline plus the visuals manifest and asks (a) whether the article hits the density target from editorial-principles-visuals.md, (b) whether each [VISUAL:...] earns its place, (c) whether sections without one would benefit. One revision pass on FAIL (BLOG_AGENT_VISUALS_REVISION_BUDGET, default 1).

Reads URLs and PDFs by fetching source content, defaulting to concise summaries for plain read requests and clean Markdown when asked to convert, save, quote, cite, or feed downstream work. Use when users ask in any language to read, fetch, check, summarize, quote, cite, convert, or save a URL or PDF. Not for local text files already in the repo.

Full ASVS 5.0 compliance assessment against a codebase and/or architecture diagrams. Reads all 346 controls from the companion CSV, performs targeted code analysis per control, and produces a complete matrix marked COMPLIANT / NON_COMPLIANT / NOT_RELEVANT — with per-control reasoning and evidence (code snippets, file:line references, diagram observations). Outputs a reviewed CSV matrix and a self-contained HTML evidence report.

Search Experience Optimization: reads Google SERPs backwards to detect page-type mismatches, derives user stories from search intent signals, and scores pages from multiple persona perspectives. Identifies why well-optimized pages fail to rank by analyzing what Google rewards for each keyword. Use when user says "SXO", "search experience", "page type mismatch", "SERP analysis", "user story", "persona scoring", "why isn't my page ranking", "intent mismatch", or "wireframe".

Use to turn a one-off support search into a standing monitor or alert that stays useful instead of being muted within a fortnight. Trigger for "alert me when a customer mentions X", "notify the team when complaints spike", "keep watching for this", "set up a daily monitor for", turning an ad-hoc sweep into something recurring, or an existing alert nobody reads any more.

This skill should be used when the user asks to "reflect on skills from memory", "audit marketplace skills against memory", "从记忆中检查 skills 的问题", "反思并修改 skill", "apply memory feedback to skills", or wants to turn accumulated memory feedback about this marketplace's skills into concrete skill fixes. Reads the project's persistent memory, re-verifies each known skill problem against current code, then fixes the skill or corrects the stale memory.

Turn "they bought it but nobody uses it" into a plan to fix it. Reads what is unused inside an account, picks the one habit to build next, and writes the enablement plan that gets there. Built for B2B customer success teams, customizable to your product and your onboarding. Trigger on "drive adoption", "which features are unused", "why aren't they using it", "seats sitting idle", "build an enablement plan", or any account-adoption diagnostic.

Turn happy customers into references, case studies, and referrals, on purpose instead of by accident. Reads who is happy enough to ask, picks the right ask, sets the value exchange, and builds a pipeline of advocacy over time. Built for B2B customer success teams, customizable to your product and your program. Trigger on "build an advocacy program", "who can be a reference", "find case study candidates", "ask for a referral", "customer advocacy pipeline", or any reference-and-referral planning.

Humanize LLM output so it reads like a careful human wrote it. Subtracts AI-isms (sycophancy, tricolons, em-dash overuse, "delve"/"tapestry"/"testament", hedging stacks, tidy five-paragraph shapes), engineers burstiness and calibrated uncertainty, and preserves technical accuracy. Supports intensity levels: subtle, balanced (default), full, voice-match, anti-detector. Use when user says "humanize this", "make this sound human", "de-slop this", "rewrite without AI tone", "match my voice", "less robotic", or invokes /unslop. Also auto-triggers when text-quality is requested.

Strip AI-generated tells from a draft and rewrite it in your own voice. Fire on "humanize this", "de-AI this", "clean up this draft", "rewrite in my voice", "make this sound like me", "kill the AI tells", or "polish this draft". Applies a 30-pattern catalog of AI-writing tells, then re-injects a voice you define so the result reads like a person, not a politer machine. Pattern catalog based on Wikipedia's "Signs of AI writing." Forked from blader/humanizer (MIT).

Interactive project learning coach via interview-style Q&A. Reads codebase and docs, dynamically generates interview questions per knowledge domain and sub-topic, conducts up to 4 follow-up rounds, scores answers, provides learning guidance with code/doc references, and persists progress. 10 domains × 3-5 sub-topics = 45 knowledge points for comprehensive interview coverage. Use when user says '学习项目', '了解项目', '检验项目', '项目学习', '面试准备', 'learn project', 'study project', 'review project', 'interview prep', 'knowledge check', or wants to understand/master the project through guided Q&A.

The analyst that watches your other analysts. It reads the trail your reports and workflows leave behind, then surfaces scoring blind spots, CRM hygiene gaps, and workflow drift, and turns every miss into a training signal. Built for GTM teams running any stack of reports, customizable to your process. Trigger on "run QA", "weekly QA report", "system health check", "where are our scoring blind spots", "what should we coach on this week", "audit our workflow performance", or any system-level health or feedback-loop question.

Interactive project learning coach via interview-style Q&A. Reads codebase and docs, dynamically generates interview questions per knowledge domain and sub-topic, conducts up to 4 follow-up rounds, scores answers, provides learning guidance with code/doc references, and persists progress. 10 domains × 3-5 sub-topics = 45 knowledge points for comprehensive interview coverage. Use when user says '学习项目', '了解项目', '检验项目', '项目学习', '面试准备', 'learn project', 'study project', 'review project', 'interview prep', 'knowledge check', or wants to understand/master the project through guided Q&A.

Plan-driven implementation in Sage/Acorn — reads docs/plans/ directory, implements components from approved sub-plans, runs scaffold generators (lando acorn acf:block), commits incrementally, auto-invokes block-scaffolding per ACF block, runs lando flush and lando theme-build after changes; full PR workflow with sage-reviewer gate. Invoke for: "/building", "implement from the plan", "implement from the plan", "build from the plan", "execute the plan", "start building", "code this feature". Skip when: there is no approved plan yet — run /architecture-discovery then /plan-generator first.

Fully autonomous QA testing agent for Modular RAG MCP Server. Reads test cases from QA_TEST_PLAN.md, executes ALL test types automatically without human intervention — CLI commands, Dashboard UI via Streamlit AppTest headless rendering, MCP protocol via subprocess JSON-RPC, provider switches, and data lifecycle checks. Diagnoses failures, applies fixes with up to 3 retry rounds, and records results in QA_TEST_PROGRESS.md. Use when user says 'run QA', 'QA test', 'QA 测试', '执行测试', '跑测试', 'test and fix', or wants to execute QA test plan.

Fully autonomous QA testing agent for Modular RAG MCP Server. Reads test cases from QA_TEST_PLAN.md, executes ALL test types automatically without human intervention — CLI commands, Dashboard UI via Streamlit AppTest headless rendering, MCP protocol via subprocess JSON-RPC, provider switches, and data lifecycle checks. Diagnoses failures, applies fixes with up to 3 retry rounds, and records results in QA_TEST_PROGRESS.md. Use when user says 'run QA', 'QA test', 'QA 测试', '执行测试', '跑测试', 'test and fix', or wants to execute QA test plan.

Fully autonomous QA testing agent for Modular RAG MCP Server. Reads test cases from QA_TEST_PLAN.md, executes ALL test types automatically without human intervention — CLI commands, Dashboard UI via Streamlit AppTest headless rendering, MCP protocol via subprocess JSON-RPC, provider switches, and data lifecycle checks. Diagnoses failures, applies fixes with up to 3 retry rounds, and records results in QA_TEST_PROGRESS.md. Use when user says 'run QA', 'QA test', 'QA 测试', '执行测试', '跑测试', 'test and fix', or wants to execute QA test plan.

Cost-benefit analysis. Produces economic NPV and financial NPV side by side, with BCR, optimism bias (with mitigation), Marginal Excess Tax Burden, real-terms rebasing, WELLBY / QALY / VPF wellbeing valuation, sensitivity, switching values, EANC for unequal-life options, validation gate, and a one-line headline verdict (socially worthwhile vs financially self-sustaining). Backed by the greenbook R package (HM Treasury Green Book primitives) when available, with graceful fallback. Supports HMT Green Book, EU Better Regulation, World Bank, ADB, and Victorian HVHR. Reads a longlist markdown file directly via --from.

Turn revenue reporting from a rear-view mirror into an operating rhythm. It narrates where you are against target, decomposes the variance by channel, reads your forecast for where you land, then adds an action layer of hot leads and deals to work now. Built for GTM teams reporting on ARR and bookings, customizable to your sheet and your metrics. Trigger on "weekly revenue wrap-up", "monthly revenue report", "how are we pacing", "revenue position", "forecast confidence", "run the weekly report", or any ARR, bookings, pipeline, or channel-performance review.

When the user wants to edit, review, or improve existing marketing copy, or refresh outdated content. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' 'copy sweep,' 'tighten this up,' 'this reads awkwardly,' 'clean up this text,' 'too wordy,' 'sharpen the messaging,' 'refresh this content,' 'update this page,' 'this content is outdated,' or 'content audit.' Use this when the user already has copy and wants it improved or refreshed rather than rewritten from scratch. For writing new copy, see copywriting.

Re-onboard a churned customer with the same care they got the first time. Reads why they left and whether they left the door open, picks the ones worth winning back, and builds a warm re-onboarding sequence, not a "we miss you" discount blast. Built for any post-sale or growth team, customizable to your product and win-back window. Trigger on "win back churned customers", "re-engage a lost account", "build a win-back sequence", "which churned customers should we chase", "boomerang play", "re-onboard a customer who left", or any win-back question.

Turn a messy quarter into a GTM board update a director reads in two minutes. Leads with the number versus plan, surfaces the two or three things that actually matter, names the risks honestly instead of burying them, and ends with clear asks. Verdict first, short, no victory lap. Built for B2B GTM and revenue leaders, customizable to your metrics and your stack. Trigger on "write the board update", "GTM update for the board", "exec summary for the QBR", "what do I tell the board", "draft my leadership update", or any board or exec GTM write-up.

Bluesky + X social loop. The bundled notifications poller runs `social-cli sync` on cron (default `*/15`), parses the per-platform `inbox-<platform>.yaml` files, and wakes the agent in batches of up to 3 never-seen notifications per turn. The optional feed poller runs `social-cli feed` every 2h for timeline scanning. Agent reads inbox, writes `outbox-<platform>.yaml`, runs `social-cli dispatch`. Also supports one-shot commands (post/reply/thread/like). Opt-in: install the skill, drop `.env` credentials into `<home>/state/pollers/social-cli-notifications/`. Companion to the `pollers` framework skill and the `world-scanning` skill.

Auto-review pipeline — reads the full CEO, design, eng, and DX review skills from disk and runs them sequentially with auto-decisions using 6 decision principles. Surfaces taste decisions (close approaches, borderline scope, codex disagreements) at a final approval gate. One command, fully reviewed plan out. Use when asked to "auto review", "autoplan", "run all reviews", "review this plan automatically", or "make the decisions for me". Proactively suggest when the user has a plan file and wants to run the full review gauntlet without answering 15-30 intermediate questions. (gstack) Voice triggers (speech-to-text aliases): "auto plan", "automatic review".

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one/apis/v1/twitter. Native-first ClawHub plugin for `twitter`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Searches and reads X (Twitter): profiles, timelines, mentions, followers, tweet search, trends, lists, communities, and Spaces. Publishes posts, likes/unlikes tweets, and follows/unfollows users after the user completes OAuth in the browser. Use when the user asks about Twitter/X data, social listening, posting, or interacting with tweets/users without sharing account passwords.