'reflection' 검색 결과

Continuous self-reflection routine combining Schön (reflection-in-action / reflection-on-action) and Mezirow (content / process / premise) levels, anchored on KAMI values (val-002 глубина, val-007 честность перед собой, принцип 1 правда, принцип 2 нулевая толерантность к бреду). Triggers FIRE DURING THE TURN, not at end. Use when: (1) about to make a 2nd+ patch attempt at the same problem, (2) user pushes back with "стоп / нет / опять / так, ещё раз / не то / пиздёж", (3) noticing self drifting into "I think that..." without evidence, (4) about to report "готово", (5) copying a pattern from one place to another, (6) task took >2× expected time. Embeds scientific method (explicit hypotheses + falsifiability + prediction-first + alternatives) as substrate for premise-level reflection, not as ceremony.

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Find concrete LifeOS improvements in Anthropic updates, practitioner examples, and internal reflections. Check each candidate against the current setup, previous decisions, and compatibility, then present actionable recommendations tied to specific files or components.

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• Checking Anthropic updates for LifeOS improvements
• Finding improvements in internal reflections
• Researching a candidate feature in more detail

Improve router-facing pipeline and reflection narration to reduce noisy status churn and make Step 0/Reflection outcomes explicit. Use when updating Router output contract, reflection reminder wording, or post-pipeline notification batching.

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Turn reflection into decisions by scoring predictions against outcomes and tracking whether commitments held. Use when running a quarterly review, scoring forecast calibration, or reflection keeps producing notes instead of change.

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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.

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Review another agent’s recent work records to identify recurring problems and propose small changes to instructions, skills, or tool descriptions. Produce evidence-backed drafts and reviewable diffs, checking that proposed rules would preserve successful outcomes from past tasks.

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• Reviewing recurring mistakes in another agent’s work
• Drafting targeted improvements to agent instructions
• Turning a recurring issue into a reusable skill

Configure images and videos as surface textures in Three.js, and use environment images for backgrounds and reflections. Adjust placement, color settings, repetition, and filtering, then manage texture memory through compression, reuse, and disposal.

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• Adjust image placement and tiling on 3D surfaces
• Use HDR images for backgrounds and reflections
• Apply video or Canvas content to a 3D surface

Build asset loading for Three.js scenes, including 3D models, textures, and HDR images for backgrounds and reflections. Coordinate multiple files, track loading progress, reuse cached assets, and handle failures with retries or fallback files.

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• Load GLTF/GLB models into a Three.js scene
• Show loading progress across multiple assets
• Use HDR images for backgrounds and reflections

Style Three.js objects with realistic, cartoon, or custom materials. Choose material types, adjust textures and reflections, create glass or metal effects, and use shader code for custom appearance while considering rendering performance.

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• Give 3D objects metal or glass surfaces
• Create cartoon-style shading
• Adjust textures and environmental reflections

Design subject lessons that also teach students how to learn. Add recall practice, explanations in their own words, predictions, and reflection to help learners check their understanding and plan later review while studying the main concepts.

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• Adding recall practice to subject lessons
• Having students explain concepts in their own words
• Designing feedback and retries after mistakes

Design autonomous AI agents that break goals into tasks, use tools, and review their own work. Covers reasoning and action loops, separate planning and execution, and reliability measures such as cost limits, validation, and logging.

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• Choosing how an agent plans and executes tasks
• Breaking goals into constrained steps
• Adding self-review and correction to an agent

Build the effects of a water and crystal skill as one visual language in Three.js. Water that reads as clear water, never a teal fill or a textured ball. A ray-marched water orb whose surface swells, rings and deforms in place: it refracts the frame behind it with an inverted horizon, has a silver rim and sharp highlights, and shows a faint caustic web on its far wall. Streams wound up from tide pools as uneven beaded ropes with silver edges, drips and shed drops. A lash that unspools from the orb, whips and pours into a pool under a torn, lopsided splash crown of aerated white water with spikes breaking into drops. Analytic ripple rings and rain on the pools. Crystals traced against their own half-space planes (Snell, Fresnel, total internal reflection, exit dispersion, Beer–Lambert, fracture discs, a white-hot root glow and a resonance band that climbs the stone), growing out of the water in a draining veil, then shattering into Voronoi shards that fall, tumble and rest with real contact on a heightfield. Caustics on the pool floor, and a moonlit tidal rock flat: rock banks traced from target images onto a world grid, flat mirror pools, a moon-glitter column of micro-facet glints, and a moon disc with maria. Use for water or ice-crystal skills, water orbs or bubbles, water whips, jets or tentacles, splash impacts, tide pools, rain, crystal growth or eruption, crystal resonance, shattering glass or quartz, caustics, wet-stone night scenes, moon reflections on water, "water and crystal", "hydro", "clear water VFX", "crystal shatter" or "skill effects only". Includes a reusable module and a five-beat demo on a scanned CC0 low-tide floor with failure-mode switches.

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Hunt gRPC vulnerabilities — server reflection enabled (enumerate all services/methods), missing authentication / metadata-stripping on internal endpoints, plaintext gRPC over HTTP/2, internal endpoint disclosure, proto file leakage, gRPC-Web/grpc-gateway transcoding injection, and HTTP/2 Rapid Reset DoS (CVE-2023-44487). Use when target exposes port 50051 / 443 / 8443 / 9090 with HTTP/2, when grpcurl/grpcui detects reflection, when an Envoy or grpc-gateway proxy is fronting a microservice, or when recon reveals a microservice architecture.

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Git提交与调试反思报告生成技能。用于分析开发过程中的错误、调试步骤和解决方案,生成结构化的中文反思报告,并创建包含报告引用的Git提交。显式请求词:反思提交、智能提交、生成调试报告、commit with reflection。

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Slow down interpretation from observation to action. Use when students or adults need to examine assumptions in conflict, dialogue, or inquiry.

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Generate structured reflection prompts for a specific teaching experience or professional learning challenge. Use when debriefing lessons, journaling, or preparing for coaching conversations.

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Structure a direct experience into a full learning cycle with concrete experience, reflection, and conceptual transfer. Use when planning field trips, simulations, or practical tasks.

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Provides expert guidance for building GraalVM Native Image executables from Java applications. Use when converting JVM applications to native binaries, optimizing cold start times, reducing memory footprint, configuring native build tools for Maven or Gradle, resolving reflection and resource issues in native builds, or implementing framework-specific native support for Spring Boot, Quarkus, and Micronaut. Triggers include "graalvm native image", "native executable java", "java cold start optimization", "native build tools", "ahead of time compilation java", "reflection config graalvm", "native image build failure".

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Expert knowledge for real-time and baked lighting in games - from cinematography fundamentals to engine-specific optimization, covering GI, time-of-day, volumetrics, and platform-aware lighting pipelinesUse when "lighting, light design, baked lighting, realtime lighting, lightmap, lightmapping, light probe, reflection probe, global illumination, GI, ambient occlusion, shadow, shadow cascade, time of day, day night cycle, volumetric fog, volumetric lighting, god rays, HDR, tonemapping, bloom, exposure, emissive, area light, Lumen, Enlighten, ray tracing, RTGI, light baking, lighting, global-illumination, lightmapping, shadows, GI, light-probes, reflection-probes, HDR, tonemapping, volumetric, fog, time-of-day, baked, realtime, mixed-lighting, lumen, enlighten, radiosity, ray-tracing, RTGI" mentioned.

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Generate structured reflection prompts for a specific teaching experience or professional learning challenge. Use when debriefing lessons, journaling, or preparing for coaching conversations.

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Structure a direct experience into a full learning cycle with concrete experience, reflection, and conceptual transfer. Use when planning field trips, simulations, or practical tasks.

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Slow down interpretation from observation to action. Use when students or adults need to examine assumptions in conflict, dialogue, or inquiry.

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Vehicle/automotive image editing for cars, trucks, SUVs, motorcycles — car scenes, reflections, tires refinement with snow/mud/grass, segment windshield/wheels/body/windows/hubcaps, atmospheric effects (dust, fog, snow, light leaks, lens flare), and lighting harmonization (hot-day, cold-day, hot-night, cold-night presets). Powered by Bria.ai's dedicated automotive pipeline. Use this skill whenever the user mentions a car, vehicle, truck, SUV, motorcycle, auto photo, car reflections, tire enhancement, muddy tires, vehicle background, automotive marketing, car dealership visual, vehicle configurator, or car scene generation. ALWAYS prefer this skill over general image tools when the subject is a vehicle — faster and more accurate for automotive workflows.

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Записать W-рефлексию (мировоззренческий слот RCS) в learning.w_reflections. Используется Диагностом R28 (MIM.R.009) или вызывается пользователем после рефлексивной сессии. Влияет на gate Д→П (FORM.089 §5.2).

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