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Repository-Ansicht von 19 gesammelten Skills in 3 GitHub-Repositories.

gesammelte Skills
19
Repositories
3
aktualisiert
2026-07-20
Repository-Explorer

Repositories und repräsentative Skills

learn
Sonstige Computerberufe

Takes any external artifact you link (a repo, a paper, a tool, a blog post, a thread) and decides what to lift from it into your own stack. Resolves the artifact, maps it against what you already have (your notes, skills, rules, memory), extracts only the genuine deltas, red-teams each delta against your real setup, and returns a per-delta decision: borrow, build, install, or skip. ALWAYS invoke when the user shares a link and asks "should we adopt this", "what do we take from this", "what do we lift from X", or "is this worth stealing". NOT a review of the artifact on its own merits (that is a different, grade-it-in-isolation skill); this one grades the artifact against YOUR systems and outputs a lift plan.

2026-07-20
commit
Softwareentwickler

Create a well-formatted git commit using conventional-commit format. ALWAYS invoke when the user says "commit", "commit this", "make a commit", or asks to save work to git. NOT for pushing (that has its own gitleaks gate) and NOT for staging review.

2026-06-12
council
Projektmanagementspezialisten

Convenes a council of orthogonal thinker-lenses (Munger, Taleb, Thiel, plus idea-lenses base-rates and second-order-effects, plus a functional skeptic seat) to pressure-test a decision from multiple perspectives, force disagreement, then synthesize. Stateless three-phase engine: blind parallel reads, a dissent round against the consensus, then a synthesis (where they agree, sharpest live dissent, the lens you ignored, decision-shaped close). Invoke when the user says 'convene the council', 'council this', 'pressure-test this decision', or wants multiple mental-model perspectives on a decision before committing. Seats fan out in parallel; the main session synthesizes.

2026-06-12
designing-frontend-uis
Web- und digitale Schnittstellendesigner

Build or improve any frontend UI (component, page, app, dashboard, landing, marketing site) so it does not look like generic AI output. Enforces a brief-read, three dials, and a countable pre-flight gate instead of vague 'be distinctive' advice. Invoke when the user asks to build/create/redesign/improve a web UI, a React/Vue/Svelte component, a page, a dashboard, or an app, or says it 'looks generic / boring / AI-slop / templated'. Pulls a NAMED design style only when the user names one; otherwise reads the brief and picks a fitting direction. Does NOT auto-clone any template and does NOT default to any one font.

2026-06-12
dream
Softwareentwickler

Consolidates memory across all layers (the durable memory files, the learnings log, project continuity) by pruning stale entries, merging duplicates, and resolving contradictions. Invoke when the user says 'dream', 'consolidate memory', 'clean up memory', 'merge duplicates', or after a heavy session that produced many similar entries. Different from per-correction capture (memory and the learning loop write new entries; dream cleans the corpus). Run periodically when memory feels noisy or after a multi-day push.

2026-06-12
grill-me
Projektmanagementspezialisten

Interrogate the user about a plan or design one question at a time, walking the decision tree branch by branch and recommending an answer for each, until every decision is resolved and understanding is shared. User-invoked only via explicit phrases like 'grill me', 'interrogate this plan', 'stress-test this design', or '/grill-me'. Never auto-fires. Distinct from plan mode (explores and proposes) and a premortem (runs after a rough decision); grill-me runs before a plan solidifies, to extract and resolve the open decisions.

2026-06-12
post-update
Softwareentwickler

Dispatch an already-approved status update to a channel (chat, social, a webhook) via a CLI. ALWAYS invoke when the user says "post the update", "send the update", "broadcast this", after they have approved the text. This is a DISPATCH point, not an author — it never writes the content itself.

2026-06-12
research-sweep
Marktforschungsanalysten und Marketingspezialisten

Run a multi-source research sweep on a topic and synthesize a cited summary. ALWAYS invoke when the user says "research X", "sweep X", "what's being said about X", or asks for a grounded summary across sources before drafting something. NOT for a single-source lookup (just fetch that source).

2026-06-12
Zeigt die Top 8 von 9 gesammelten Skills in diesem Repository.
book-platform-sweep
Technische Redakteure

Full three-tier research sweep for one chapter of The AI Exoskeleton book. Tier 1 raw platform sweep (Perplexity / HN with comment bodies / Reddit with post bodies / Grok x_search / Gemini grounded / shared Substack pool). Tier 2 open-ended discovery sweep ("surprise me") + firecrawl URL body extraction + per-URL sub-summaries. Tier 3 hybrid two-pass synthesis: Pass A (Sonnet, mechanical rollup) + Pass B (Opus, judgment synthesis) → raw-summary + discovery-summary + 1000+-line merged findings.md. Run before drafting any chapter. Floor: six Tier-1 files + three Tier-2 files + ≥15 URL bodies.

2026-06-29
book-voice-polish
Technische Redakteure

Run a Stage 5 voice-polish pass on a chapter of The AI Exoskeleton. Mechanical scan (em-dashes, banned words, sentence-length monotony, paragraph-rhythm uniformity, banned constructions, heavy-use content-word frequency) plus Opus judgment to fix violations in place at clause and sentence level. Produces review-work/NN-slug/06_voice_polish.md capturing every fix made and every flagged hit ruled context-justified. Strictly clause-and-sentence-level, never restructures the chapter. Grounded in voice.md and book-voice.md. Use after Stage 4 adversarial-reader convergence and before Stage 6 second red team. Runs inline on main-context Opus.

2026-04-30
book-adversarial-reader
Technische Redakteure

Run a multi-persona reader simulation on a drafted chapter of The AI Exoskeleton. Produces review-work/NN-slug/05_adversarial_reader.md capturing what each of five real audience personas thinks while reading — what convinces them, what loses them, what they would do on Monday morning. Different from book-red-team (which critiques argument and structure from editorial lenses); this skill simulates the actual readers the book is written for, including the hostile augmented-leadership reviewer the book argues against. Use after Stage 3 fact-check and before Stage 5 voice polish. Runs inline on main-context Opus.

2026-04-29
book-draft-chapter
Technische Redakteure

Draft or revise a chapter of The AI Exoskeleton, grounded in canon files and source essays. Use when the user asks to draft, write, or revise a specific chapter by number or slug. Runs inline on main-context Opus (voice-sensitive work).

2026-04-29
book-export
Softwareentwickler

Assemble the full manuscript from chapters/ into a typeset PDF (and optionally EPUB). Use when the user asks to build the book, export the book, compile a draft, produce a readable copy, or ship the manuscript. Defaults to a DRAFT-watermarked preview build; will refuse to produce a ship build if any chapter is not ship-ready.

2026-04-29
book-fact-check
Technische Redakteure

Fact-check a drafted chapter of The AI Exoskeleton against the chapter's research findings. Extracts every factual claim from the chapter, matches each to a source in research/<chapter-slug>/findings.md or research/source-index.md, and flags any claim that cannot be matched. Produces an assertion registry + an open-claims list per chapter. Use when a chapter draft exists and needs verification before it can be considered ready.

2026-04-29
book-red-team
Redakteure

Run a structural and adversarial critique of a drafted chapter of The AI Exoskeleton. Two stages. Stage 2 (default) reviews early/mid drafts and produces review-work/NN-slug/04_red_team.md with weak arguments, unsupported load-bearing claims, structural issues, and counter-positions not yet engaged. Stage 6 reviews post-Stage-5 final-quality drafts as the last rigor gate before export, produces review-work/NN-slug/07_red_team_v2.md, and looks specifically for what slipped through six prior passes. Both stages run inline on main-context Opus.

2026-04-29
grill-me
Projektmanagementspezialisten

Interrogate the user about a plan or design one question at a time, walking the decision tree branch by branch and recommending an answer for each, until every decision is resolved and understanding is shared. User-invoked only via explicit phrases like 'grill me', 'interrogate this plan', 'stress-test this design', or '/grill-me'. Never auto-fires. Distinct from EnterPlanMode (explores and proposes) and the premortem rule (runs after a rough decision); grill-me runs before a plan solidifies, to extract and resolve the open decisions.

2026-05-26
dossier
Marktforschungsanalysten und Marketingspezialisten

Decodes an AI company past the marketing site — what they actually do, where the substance vs slideware divide is, and how they compare against 3 contextual alternatives + 1 contrarian build-internal option, ending with a take-the-meeting verdict and 2-3 prep questions sharp enough to expose the gap in 90 seconds. ALWAYS invoke when the user says '/dossier <company>', 'have you seen <company>', 'decode <company>', 'what does <company> actually do', 'should I take a meeting with <company>', 'is <company> any good', or pastes a company website and asks for a read. Always comparative — never produces a single-company writeup without 3 alternatives + 1 contrarian framed against the user's specific use-case lens. V1 SCOPE: AI/data/ML companies only — if the company isn't AI flavored, surface that and ask before proceeding. Refreshes existing dossiers in place (preserves prior verdict in 'Previous decode' section). Skeptical-analyst tone. Output to a per-company directory at ~/vault/Knowledge/AI-Research/Companies/

2026-05-18
artificial-analysis
Softwareentwickler

Compare LLM and media-model performance using Artificial Analysis (artificialanalysis.ai). ALWAYS invoke when the user asks to compare models, asks 'which model is best/fastest/cheapest', asks about model intelligence/coding/math benchmarks, asks about tokens-per-second / TTFT / latency, asks about model pricing, asks about Elo ratings for image/video/TTS models, or names two+ models and wants a head-to-head. Covers all major LLMs (OpenAI, Anthropic, Google, Meta, DeepSeek, etc.) plus text-to-image / image-editing / text-to-speech / text-to-video / image-to-video Elo leaderboards. Cached 1h, no rate limit concerns.

2026-05-08
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