'compound' 검색 결과

Run a portable sequential plan-work-verify-review-compound lifecycle. Use optional generic workers only when Amp makes them available.

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Searchable Elixir/Phoenix/Ecto solution documentation system with YAML frontmatter. Builds institutional knowledge from solved problems. Use when consulting past solutions before investigating new issues.

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Capture solved problems as searchable solution docs. Use after fixing bugs, when "that worked", or after successful /phx:review or /phx:investigate.

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Autonomous skill lifecycle manager. Reviews skill-compounder drafts, promotes high-confidence patterns, merges duplicates, archives low-quality drafts. Zero manual review required - runs on every session start.

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Capture solved problems as categorized documentation with YAML frontmatter for fast lookup

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Query ChEMBL's bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.

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Final craft pass applying the compound details from the polish checklist — micro-typography, spacing rhythm, hover states, and a signature detail — directly to code. Use when the surface is functionally complete but feels unfinished, or when the user says "polish this" / "it looks generic" / "add the final touches". Invoke when the user asks for polish on their UI, or mentions 'polish' alongside design / UI / frontend work.

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React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.

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KEGG REST API (academic only). Pathways, genes, compounds, enzymes, diseases, drugs via 7 ops (info/list/find/get/conv/link/ddi). ID conversion (NCBI/UniProt/PubChem). Use bioservices for multi-DB Python.

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Medicinal chemistry filters for compound triage. Drug-likeness rules (Lipinski Ro5, Veber, Oprea, CNS, leadlike, REOS, Golden Triangle, Ro3), structural alerts (PAINS, NIBR, Lilly Demerits), chemical group detectors, complexity metrics, and filter composition query language. Built on RDKit/datamol. For hit-to-lead filtering, library design, ADMET pre-screening. For molecular I/O use rdkit-cheminformatics or datamol.

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Parse HMDB (Human Metabolome Database) local XML for metabolite info, chemical properties, biological context, disease links, spectra, and cross-DB mapping. No REST API — uses ~6 GB XML download. Use drugbank-database-access for drugs; pubchem-compound-search for live lookups.

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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface: 6 tools with --full (adds check_action); 13 tools with AR_EXTRAS=1 --full. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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Cost discipline for recurring /loop runs. A loop multiplies per-iteration cost by iteration count, so waste compounds. Encodes cache-aware pacing (avoid the 300s dead zone), event-driven-over-polling, a no-change short-circuit, a compact state handle carried between ticks, and a stop condition. Use when a /loop is running or being set up — especially long-lived or frequently-firing ones.

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Sprint Quality Gate 通过后触发。提炼本 Sprint 学到的为结构化条目追加 lessons.md。

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Apply the bioengineering, translational systems biology, and tissue modeling frameworks of Linda Griffith, Professor of Biological Engineering at MIT and tissue engineering pioneer. Use this skill whenever facing decisions, designs, or evaluations in bioengineering, tissue modeling, microphysiological systems (organs-on-chips), synthetic matrix design, disease subtyping, or drug target validation. Reach for this skill when evaluating preclinical models (human vs. animal), reframing overlooked or neglected pathologies into rigorous engineering challenges, selecting biomaterials, designing cell signal processing assays, or diagnosing operational flaws in experimental platforms (e.g., PDMS compound absorption or Matrigel lot variability).

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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.

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React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture.

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Predicts ADMET properties using ADMETlab 3.0 (119 endpoints with uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES gold-standard endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.

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World-class marketing expertise combining Seth Godin's permission marketing philosophy, Neil Patel's data-driven growth tactics, and the strategic frameworks from brands like Apple, Nike, and Dollar Shave Club. Marketing is the art and science of creating customers. Great marketing isn't about shouting louder - it's about being more relevant. The best marketers understand that attention is earned, not bought. They build systems that compound, create word-of-mouth, and turn customers into advocates. Marketing is the bridge between what you've built and the people who need it. Use when "marketing, campaign, go-to-market, gtm, launch, promotion, advertising, ads, acquisition, demand gen, lead gen, channel, funnel, conversion, cac, customer acquisition, reach, awareness, consideration, marketing, advertising, acquisition, campaigns, gtm, demand-generation, analytics, channels" mentioned.

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Expert in designing and optimizing growth loops - self-reinforcing systems where the output of one cycle becomes the input for the next. Covers viral loops, content loops, paid loops, sales loops, and product loops. Knows how to identify, measure, and accelerate the specific loop mechanics that drive sustainable growth. Use when "growth loop, viral loop, content loop, flywheel, compounding growth, acquisition loop, referral loop, " mentioned.

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World-class content strategy expertise combining the frameworks of Andy Crestodina's data-driven approach, Ann Handley's "everybody writes" philosophy, and the content engines that power companies like HubSpot, Ahrefs, and Intercom. Content strategy isn't about creating content - it's about creating the right content for the right audience at the right time in the right format. The best content strategists think in systems, not pieces. They build content engines that compound over time and turn strangers into customers and customers into advocates. Use when "content strategy, content plan, editorial calendar, blog strategy, content marketing, topic clusters, pillar content, content audit, content calendar, SEO content, content distribution, content funnel, thought leadership, content engine, content topics, what to write, content, strategy, editorial, SEO, blog, planning, distribution, measurement" mentioned.

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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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Captures reusable knowledge after work is done — learnings from feedback or failures, resolved bugs (symptom, root cause, prevention), and recurring code patterns — into the project's persistent memory file. Use after feedback, a non-trivial bug fix, a failed attempt, or the third repetition of a pattern.

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Suede-owned content-strategy discipline for audience questions, content pillars, topic clusters, editorial priorities, cadence, distribution, refreshes, and stop-doing decisions. Use when deciding what to publish, why it deserves resources, and how the portfolio compounds. NOT FOR: writing an individual asset (use suede-copy), technical or on-page SEO audits (use suede-seo-audit), or social-channel production (use suede-social).

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