「bec」の検索結果

Develop brand voice, tone matrices, messaging frameworks, and brand book documentation. Use when the user asks about brand voice, tone of voice, brand guidelines, messaging framework, or brand consistency.

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Install a per-turn canary signal (e.g. starting every reply with the user's name and a turn counter) so silent context degradation becomes visible the moment it happens, and run a recovery protocol when the canary trips. Use when the user mentions a "canary", "context canary", or "canary check", asks to detect context rot / compaction / drift, says "you stopped using my name" or "did you lose context", asks "how degraded is your context", or wants an early-warning system for long agent sessions.

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Audit whether a paper's baseline comparisons are COMPLETE, FAIR, and SIGNIFICANT: a required recent SOTA baseline is missing while 'best/SOTA' is claimed (HP-MISSING-BASELINE); a baseline is undertuned / given less compute-tuning-data, run at a mismatched config, or the equal-budget ablation-as-baseline is absent (HP-WEAK-BASELINE); 'outperforms' is asserted over overlapping error bars or with no variance/seeds (HP-SIG-OVERLAP); and a cross-row 'improves over baseline by X%' is arithmetically wrong (HP-DELTA-ERROR, cross-row form only). A versioned per-domain baseline profile + a live leaderboard/recency search are assembled by the EXECUTOR as structured facts; a fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per dimension) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Works at L0 (stated comparisons) and deepens at L2 (configs/result files). A completeness question it cannot settle internally becomes needs_external_check, never a guessed missing baseline. Emits baseline-comparison-audit.findings.json; computes NO verdict. Detect-only. Triggers: "baseline audit", "missing baselines", "is the comparison fair", "weak baseline", "baseline 误报", "SOTA earned?".

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Builds and revises a complete skill DIRECTORY — SKILL.md, scripts, references, assets — and packages it. Use for "create a skill for X", "turn this into a skill", "update/improve this skill", "why doesn't my skill trigger", "review this SKILL.md", "package this skill", or when a repeated procedure should become a reusable artifact. Enforces the structure that makes skills work — a concrete procedure rather than a goal statement, an explicit applicability boundary, failure modes with their signals, a runtime verification step, and a description checked against the existing catalogue for confusability. For choosing whether the instruction should be a skill at all rather than project instructions or a prompt, use crafting-instructions. For writing quality inside the prose, use writing-instructions.

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Decide which model, effort level, and cascade shape each subagent gets, and how to keep improvement loops safe (evaluator-as-selector, stop on regression). Routes on measured cost-per-completed-task rather than per-token price, because a tier's token count varies more by task shape than price varies across tiers. Covers per-model effort semantics, the concision lever, cascade preconditions, context handoff, and watching a subagent fan-out live. Use when choosing a subagent's model or effort, when choosing how to escalate a failed attempt, or when asked which model or effort a task should get. Grounded in a 2026-07 calibration, a 2026-08 coding-cost study, a 2026-09 seeded-bug repair battery, a 2026-10 Haiku 5.5 ladder priced per spawn, and a 2026-10 retry-versus-escalation ladder on 295 SWE-bench Verified tasks; Managed Agents API specifics are operational, not calibrated.

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The Socratic spec-refinement front of /feature, and the planning front of /sprint. Routed to BEFORE any code — it turns a one-line idea into a concrete spec with testable criteria, ready for initial combined sprint review or explicit sequential approval. Five gated phases — frame, shape, refine, write, review-and-approve. No implementation before an approved spec; the caller retains required planning, execution and delivery ordering. Each acceptance criterion becomes one tdd Phase 1 obligation.

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Live on tiny.place (the agent-to-agent social network) like a person lives on a social app — entirely through the `tinyplace` CLI. Onboard a @handle identity, get funded, become discoverable, then run a recurring check-in loop that pulls your messages, notifications, and feed and acts on them: reply to DMs, react on the feed, follow agents, join groups, and fund/win bounties (contest-style paid work) over Signal end-to-end encryption and x402 payments. Use whenever an autonomous agent or harness needs to onboard to, or keep operating on, tiny.place.

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Use when assessing the architectural health of a codebase — before a major refactor, when onboarding to an unfamiliar repo, after rapid growth, when planning a redesign, or to surface structural strengths and risks before they become expensive. Not for fixing what it finds, and not for reviewing a branch diff.

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Use when the user wants to add an in-site feedback loop to a website repo — a floating "提个建议" button where allowlisted visitors submit suggestions that become a GitHub Issue, which GitHub Actions turns into an automatic code change via Claude Code, auto-merges and deploys, and records on a /_feedback dashboard with one-click revert. Triggers — "给网站加个反馈对话框", "提一句话就自动改网站", "装上反馈闭环", "feedback loop", "/wjs-looping-feedback".

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Applies the philosophical frameworks of Friedrich Nietzsche (19th-century German philosopher, author of Thus Spoke Zarathustra). Use this skill whenever the user is grappling with existential crises, seeking meaning, facing severe adversity, questioning traditional moral frameworks, or feeling trapped by societal expectations (the "herd"). Trigger this when discussing self-overcoming, creating personal values, analyzing power dynamics in relationships or institutions, dealing with envy, or breaking down dogmatic beliefs. It helps reframe suffering as a catalyst for growth and pushes the user to "become what they are" rather than settling for comfort and mediocrity.

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Identify and challenge implicit assumptions in plans, proposals, and technical decisions. Use when strategic-cto-mentor needs to surface hidden assumptions and wishful thinking before they become costly mistakes.

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Detect common technical and organizational anti-patterns in proposals, architectures, and plans. Use when strategic-cto-mentor needs to identify red flags before they become problems.

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The methods a financial-ML result has to survive before it is evidence — purged cross-validation with an embargo, triple-barrier labelling, sample uniqueness under overlapping labels, fractional differentiation, meta-labelling, and multiple-testing correction. Written because the invariants were required of quant-researcher and nothing in the project explained how to satisfy them: a rule without a method produces either an invention or a block. Applied whenever a backtest, a feature or a label is being designed or judged.

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Review any client-facing message or material (emails, letters, newsletters, social posts, website copy, presentations, performance reports, proposals) for compliance with SEC rules that govern investment advisers — the Marketing Rule (206(4)-1), fiduciary duty / antifraud provisions (206), Books and Records (204-2), and Reg BI where broker-dealer activity applies. Produces a pass/flag/fail markup, required disclosures, a books-and-records scratch-pad entry, and an archiving handoff reminder. Triggers on "compliance check", "/compliance", "is this compliant", "review this before I send it", "can I say this to a client", "check this email/post/deck". Use it whenever an advisor asks to check, review, or look over anything before it goes to a client or prospect — "check this before I send it", "look this over before it goes out" — even a routine note, because deciding that a message needs no changes is part of the review, not a reason to skip it.

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Reshape source-locked mesh boundaries so the geometry fits texture-atlas or decal contours 1:1 before final UV/material work. Use when textures look off because the model behind them does not match the source texture region, or when the mesh must adapt to the texture instead of stretching the texture onto an approximate mesh.

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Present pathway options and orchestrate place-based curriculum design from a local place, curriculum requirement, or community issue. Use when place should become a primary text for learning.

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Generate an /insights-style HTML report across MANY of your agent coding sessions (Claude Code and OpenAI Codex): what you work on, how you use the agent, your wins, your friction points, and concrete fixes to become more effective — precise goal-setting, not trusting output blindly, scoping, context hygiene, right-sizing models. Reads local session history and writes one self-contained report.html. Use when the user asks to "разбери мои сессии", "оцени как я работаю с агентом", "мой insights", "session insights report", "review how I work", "на что обратить внимание", or runs "/mentor", "/mentor claude", "/mentor codex", "/mentor both". Do NOT use for reviewing the CODE produced (that's code-review) or building the agent.

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Use when writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on "write the ADR", "format as MADR", "check ADR quality", "mark gaps in ADR". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).

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Simultaneous Launch Button - Two-person rule for destructive commands. Use when coordinating dangerous operations between agents, requiring peer review for rm -rf, git push --force, kubectl delete, DROP TABLE, or terraform destroy.

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Handle blocked destructive commands. Use when dcg blocks rm -rf, git reset --hard, DROP DATABASE, kubectl delete, or when configuring agent safety guardrails.

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How nutrition science becomes dietary guidance, and how industry, agriculture, and politics shape that process. Covers the US Dietary Guidelines cycle, food labeling, WIC and SNAP, commodity subsidies, and the documented history of industry influence on research and recommendations. Use when a question asks why a guideline says what it says, how a recommendation got written, or whether an industry-funded study should be trusted.

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Triggers for automatically invoking the decision-framework skill before irreversible or high-blast-radius operations. Use as a checkpoint: when a proposed action matches a trigger pattern, pause and run the appropriate framework (first-principles, inversion, decision matrix, second-order thinking) before proceeding. Prevents ad-hoc calls from becoming regret.

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Use at write time to vector/embedding memory — Grove content-addressed insertion, chroma/pgvector upserts, memory-consolidation promoting session traces to MEMORY.md, or embedding externally-ingested content. Scores each candidate record against a fixed panel of sentinel queries and quarantines any record that would become the nearest neighbor of too many unrelated queries — a hub — whether from adversarial poisoning or accidental over-generality. This is the memory-record-side sibling of skill-injection-guardian (file-side) and the write-side complement of memory-use-warrant (read-side). Quarantine, never silently drop; a human reviews. Backed by the admission-time hubness gate (arxiv 2606.19692v1). Triggers on inserting into vector memory, consolidating memory, and embedding stranger content.

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Self-modifying AI agent configuration via ruler + MCP + DuckDB. All behavior mods become one-liners.

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。