Universal guardrails loaded by every agent. Defines Verify Before Reporting (VBR), Write-Ahead Log (WAL), and the security baseline. Always-on, role-independent.
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。
Shared foundation for all human-voice writing skills. Load before any profession-specific skill. Contains: burstiness principle, perplexity principle, soul injection techniques, community-discovered AI patterns with sources, additional core patterns, banned AI vocabulary, and scoring rubric.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Load this skill before any profession-specific writing skill (job-seeker, consultant, etc.). It defines the universal writing quality baseline all human-voice skills share.
AI detectors measure "burstiness": sentence length variance. Human writing has HIGH burstiness. AI has LOW.
Target these sentence length patterns:
AI detectors also measure "perplexity": how predictable each word is. AI text has LOW perplexity. Human text has HIGHER — more surprising word choices.
Increase perplexity naturally by:
Removal alone does not produce human writing. These make the difference between "AI-clean" and "human":
Surfaced from HackerNews, Substack, Wikipedia's editorial guidelines, and writing practitioner blogs. Sources cited inline.
P38: Paragraph-Reshuffling Immunity. LLMs generate parallel blocks rather than an unfolding argument. Test: can you swap paragraph 2 and paragraph 4 without breaking the piece? If yes, it's AI. Fix: make paragraph N+1 depend on something concrete in paragraph N — references, callbacks, causal linkage. If two paragraphs are interchangeable, merge or cut one. Source: HackerNews, May 2025
AI: Remote work improves balance. Many workers prefer it. Studies show productivity rises. Additionally, commuting costs drop. Human: Remote work's flexibility is the obvious sell. The harder question is what you lose — the hallway conversation that turns into your best idea.
P39: Paragraph-Closing "Whether" Summary Sentences. LLMs treat paragraph endings as local summaries, mimicking SEO blog structure where each section self-explains. Humans rarely end flowing prose this way. Fix: cut the closing "whether" sentence. End on the strongest specific point, not a hedge that gestures at the range covered. Source: Gone Travelling Productions, Aug 2025
AI: Tokyo offers everything from Michelin-starred restaurants to humble ramen stalls. Whether you prefer fine dining or street food, Tokyo has something for every palate. Human: Tokyo's best ramen counter doesn't take reservations and hasn't changed the broth recipe since 1987.
P40: Symbolic Gloss / Meaning-Telling. LLMs narrate the meaning of things rather than trusting description to carry it. Triggers: "represents", "symbolises", "speaks to", "embodies", "reflects broader" applied to mundane things. Fix: cut the symbol sentence. State the fact and let the reader interpret. Source: Writewithai Substack, 2025
AI: The closed factory represents the decline of manufacturing and speaks to broader anxieties about post-industrial identity. Human: The factory closed in 2009. Three hundred jobs. The town's high school dropped football the following year.
P41: Infomercial Engagement Hooks. Fake dramatic pauses imported from social-media-optimised AI writing. Triggers: single-sentence paragraphs that mimic viral LinkedIn cadence — "The catch?", "The kicker?", "Here's the thing:", "The brutal truth?", "Want to know the best part?" Fix: delete the hook line. Let the next paragraph make its point directly. Source: Writewithai Substack, corroborated on HackerNews
AI: Most people abandon goals in week three.\n\nThe brutal truth?\n\nThey lack a clear failure threshold. Human: Most people abandon goals in week three. The ones who don't usually make the failure threshold explicit before they start.
P42: Erratic Inline Bolding. Patternless bolding — the model decided certain words felt important, with no consistent rule. Triggers: bold spans of 1–4 words appearing mid-paragraph, not labelling a defined term, multiple bold spans per paragraph with no shared category. Fix: strip all inline bold except glossary terms and UI labels. If something deserves emphasis, the sentence structure should provide it. Source: Gone Travelling, 2025, Wikipedia: Signs of AI writing
P43: The Treadmill Effect (Low Information Density). A 500-word AI section may contain 100 words of new information and 400 words of restatement. Humans advance; AI circles. Triggers: marker phrases inside the paragraph — "In other words,", "Put simply,", "To put it another way,", "Essentially,". Fix: apply the "what's actually new here?" test on each sentence. Delete any that just rephrases what came before. Source: aidetectors.io, HackerNews
P3: Superficial -ing Phrases. Tacking present participle phrases onto sentences to fake depth. Triggers: sentences ending with "...ensuring reliability.", "...fostering growth.", "...highlighting the importance of.", "...contributing to.", "...showcasing." Fix: delete the -ing clause. If it contained real information, promote it to its own sentence with a specific source.
AI: The platform processes payments globally, ensuring reliability and fostering growth. Human: The platform processes payments in 47 countries. Uptime is 99.97% over the last 12 months.
P24: Generic Positive Conclusions. Default closing sentences that say nothing. Triggers: "The future looks bright", "exciting times lie ahead", "continues its journey toward excellence", "a step in the right direction", "poised for growth", "we look forward to what comes next." Fix: cut the closing entirely, or end on the last specific fact.
Single words that mark AI-generated writing — replace with plain language:
delve, leverage, robust, seamless, pivotal, nuanced, tapestry, realm, underscore, elevate, foster, navigate (metaphorical), landscape (metaphorical), synergy, cutting-edge, game-changer, impactful, utilize (use "use"), spearhead, orchestrate, bolster, crucial, garner, multifaceted, interplay, vibrant, showcase, testament
Score on five dimensions (0–10 each, 50 max). Revise if total < 35.
| Dimension | 0–4 | 5–7 | 8–10 |
|---|---|---|---|
| Directness | Long warm-up, buried claim | Main point findable | Claim in first sentence, no hedging |
| Rhythm | Uniform sentence length, monotone | Some variation | Deliberate mix, reads naturally aloud |
| Trust | Constant hedging and softening | Occasional hedging | States directly, no apology for existing |
| Authenticity | Generic, could be anyone | Some specific detail | Sounds like a specific person with a specific history |
| Density | Padded with filler | Some waste | Every sentence earns its place |
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概要と使いどころ
Universal guardrails loaded by every agent. Defines Verify Before Reporting (VBR), Write-Ahead Log (WAL), and the security baseline. Always-on, role-independent.
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: architecture mode, architect this, ADR, C4, PlantUML, system boundary, design decision, PRD gap, high ambiguity, multiple technical approaches, security architecture, data architecture, integration risk, implementability gap, story ticket, or epic ticket.
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: assumptions audit, audit assumptions, check for hidden assumptions, plan/ticket has ambiguous scope edges, pre-flight before finalizing a ticket, unstated assumptions, or 'what am I assuming'. Surfaces unstated assumptions, ambiguous scope edges, and untested preconditions in a finished plan/ticket/ADR before it is handed to Builder. Optional pass — Architect judges when to apply it; not a mandatory gate on every ticket.
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: Tester needs UI state evidence for VBR, or Builder needs to verify an integration wire is observable end-to-end. Powered by agent-browser MCP — token-efficient browser automation (200–400 tokens/page).
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。
Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch.
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。
Writing skill for freelancers and independent consultants. Covers project proposals, bids, client emails, and scope summaries. Leads with the client's problem, not the consultant's background.
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。