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dontbesilent 商业模式诊断。两种模式：问诊（消解你的问题）和体检（拆解你的商业模式）。 触发方式：/dbs-diagnosis、/问诊、「帮我看看商业模式」「诊断一下我的业务」「我有个商业问题」 Business model diagnosis using dontbesilent's ontological framework. Two modes: consultation (dissolve your question) and checkup (analyze your business model). Trigger: /dbs-diagnosis, "diagnose my business model", "I have a business question"

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Generate Mermaid architecture diagrams from your codebase. Produces C4 context and container diagrams, sequence diagrams for key flows, ER diagrams from database schemas, and module dependency graphs. Use when you need architecture diagrams, system diagrams, database ERD, sequence diagrams, dependency visualization, or C4 model diagrams.

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Breaks work into ordered tasks. Use when you have a spec or clear requirements and need to break work into implementable tasks. Use when a task feels too large to start, when you need to estimate scope, or when parallel work is possible.

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The only memory skill that watches on its own. No database. No vectors. No manual saves. Just an LLM observer that compresses your conversations into prioritised notes, consolidates when they grow, and recovers anything missed. Five layers of redundancy, zero maintenance. ~$0.00/month (using free-tier models). While other memory skills ask you to remember to remember, this one just pays attention.

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One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.

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Extract and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library.

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Think and work like an expert Cellular Neuroscientist. Use when a task calls for Cellular Neuroscientist judgment. Treat the neuron as a cable with active channels, not a point integrator unless you have shown it is one for your question. Separate voltage, calcium, and fluorescence observables. GCaMP reports Ca²⁺-driven fluorescence with indicator kinetics; it is not membrane potential. FRET sensors (ArcLight, ASAP) trade speed and dynamic range differently.

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Abusing Active Directory object ACLs (DACL/ownership) for privilege escalation and lateral movement (GenericAll, GenericWrite, WriteDACL, WriteOwner, AddMember, ForceChangePassword, and replication rights via DS-Replication-Get-Changes-All). Use when BloodHound CE shows an outbound control edge from a principal you own toward a higher-value object, and you want the exact bloodyAD/impacket command to weaponize that ACE, plus detection and remediation. DCSync is covered here strictly as a post-compromise technique, not a user entry path.

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Active Directory Certificate Services (AD CS) escalation techniques ESC1 through ESC17, driven by hand with Certipy (ly4k). Use when the target runs a Certificate Authority and you want to find vulnerable certificate templates or CA misconfigurations, request a certificate that impersonates a privileged user, and know the exact certipy command, what each ESC actually checks, the Windows Event IDs that fire, and the remediation. ESC1 and ESC8 are the two you hit most in the field.

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Authentication coercion (PetitPotam MS-EFSR, PrinterBug MS-RPRN, DFSCoerce MS-DFSNM) chained into NTLM relay (impacket ntlmrelayx) toward LDAP, AD CS web enrollment (ESC8), or SMB. Use when SMB signing is not enforced or LDAP channel binding is missing, and you want to force a privileged machine account to authenticate to your relay and turn that into RBCD, a DCSync-capable ACL grant, or a certificate. Includes the exact Coercer/ntlmrelayx commands, requirements, detection, and remediation.

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Local and self-hosted text-to-speech with Kokoro, the ~82M-parameter open-weight (Apache 2.0) model by hexgrad. Use when synthesizing speech offline, on-device, in the browser, or on your own server without per-character API cost — for high-volume narration, audiobooks, privacy-constrained pipelines, and prototyping. Covers running Kokoro via the Python `kokoro` package, ONNX / kokoro-js in the browser, and the OpenAI-compatible Kokoro-FastAPI wrapper; picking voices and language codes; blending voices; chunking long text; controlling pronunciation via misaki/espeak-ng; and judging when Kokoro is the right tool versus when its lack of voice cloning, narrow emotional range, and weaker non-English quality mean you should reach for a hosted or larger model instead. Not for voice cloning, expressive/emotional character performance, or high-fidelity multilingual work — say so and route elsewhere.

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Plan, produce, adapt, finish, deliver, and improve platform-ready short vertical social videos from a brief, script, transcript, or source media. Use for TikTok videos, Instagram or Facebook Reels, YouTube Shorts, LinkedIn vertical clips, and cross-platform social cutdowns when the work requires audience and objective definition, an honest hook and promise, short-form beat or shot planning, UI-safe layout, captions and accessibility, speech/music/SFX mixing, platform-specific exports, rights and AI or sponsorship disclosure, analytics-led retention iteration, or final QA and repair. Do not use as a provider-specific image, video, voice, or music generation guide.

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Load project-specific context -- tech stack, architecture overview, directory layout, trusted sources, and conventions -- so that all skills operate with awareness of what this project actually is. Use when a knowledge base document exists, or when the user asks about the project's tech stack, architecture, conventions, framework, directory layout, or says 'tell me about this project', 'what are we using?', 'what's our stack?', or 'what framework is this?'. Use the knowledge-priming-refiner to create a knowledge base document.

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Create a new Lattice skill — atom, molecule, or refiner — following all framework conventions. Writing skill files manually almost always produces convention violations: wrong section order, missing confirmation gates, defaults.md without the right structure. This skill knows all of that and guides you through it. Use whenever adding any new atom, molecule, or refiner to Lattice, or when the user says 'create a new skill', 'add an atom', 'add a molecule', 'add a refiner', 'build X for Lattice', 'new lattice skill', or 'skill forge'. Does not validate, align docs, or deploy — those are separate skills you run after.

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Extract full transcripts and captions from YouTube videos for content analysis, summarization, note-taking, and repurposing. Supports multiple languages and auto-generated captions. Ideal for research, content creation, and accessibility workflows.

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Generate content ideas backed by real audience demand data from Reddit questions, YouTube search gaps, Twitter discussions, and web search trends. Identifies proven topics, underserved questions, and content formats that outperform in your niche.

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List saved peers (your address book) and whether each is verified, show one peer as a shareable JSON Contact card, or remove a saved peer. Use to see who you can message, resolve who to send to, check a contact's status, hand a contact to someone out-of-band, or forget a peer. `<user>` is this session's user directory (absolute path; from init).

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Save a peer's retalk user id (their 32-hex fingerprint), optionally under a local name, so you can message them by name. Use when you have a peer's fingerprint to record. Add --verify to fetch and pin their keys now. If the fingerprint is missing, ask with AskUserQuestion.

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Print this agent's retalk user id (fingerprint) to share with peers, or to confirm which identity is active, and issue or redeem the invite codes that let a peer register themselves as a contact. Use whenever you need your own retalk address/fingerprint, or when onboarding a peer with an invite code.

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Save a contact someone shared with you (a Contact card) as a local peer. agent-talk does NOT auto-import — review staged cards and import only ones from a peer you trust. `<user>` is this session's user directory (absolute path; from init).

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Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.

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Use AI to write NEW test code from specs, PRDs, user stories, code diffs, bug reports, or OpenAPI specs. Staged pipeline: requirements extraction → risk analysis → coverage matrix → scenario generation → oracle design → test code → human review, with guardrails against hallucinated APIs and weak assertions. Use when: "generate tests from spec," "tests from PRD," "tests from user story," "auto-generate test cases," "AI write tests for me." Not for: testing AI/LLM features in your product — use ai-system-testing. Not for: auditing a pre-existing test suite you did not just generate — use ai-qa-review (Step 7 here only reviews tests THIS pipeline produced). Related: playwright-automation, unit-testing, api-testing, qa-project-context.

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Resolve the currently-unresolved review threads on the current branch's PR in one sweep — assess each, fix it (test-first, verified) or reply-only, then reply and resolve — and return a summary. Use standalone to address PR review feedback, or as the thread-handling step invoked by /watch-pr. Repo is adamayoung/TMDb.

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Cut a new TMDb release — work out the next SemVer version from the evidence, do the pre-tag housekeeping a tag would otherwise freeze in place, draft release notes, then tag and publish the GitHub release. Presents the version (with its reasoning) and the full notes for approval and STOPS; nothing is tagged or published until you say go.

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