710건 · 관련도순
Constructs venture return models with entry valuation, follow-on reserve, multiple scenario exits, and portfolio-level fund math. Use when modeling VC returns, calculating fund economics, or projecting portfolio outcomes.
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Evaluates SPAC de-SPAC transaction economics with sponsor dilution, redemption risk, PIPE analysis, and warrant impact. Use when analyzing SPAC deals, evaluating sponsor economics, or modeling de-SPAC outcomes.
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Evaluates proxy fight mechanics with shareholder base analysis, ISS/Glass Lewis recommendations, and vote probability modeling. Use when analyzing proxy contests, assessing vote outcomes, or evaluating director nomination campaigns.
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Evaluates activist investor campaigns with thesis assessment, proposed changes, and likely outcome analysis. Use when analyzing activist situations, evaluating campaign theses, or assessing activist track records.
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Evaluates distressed debt opportunities with recovery analysis, liquidity assessment, and potential restructuring outcomes. Use when screening distressed situations, analyzing stressed credits, or evaluating workout scenarios.
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Use this skill when a new product feature, vendor, AI use, monitoring tool or change in processing raises the question "do we need a DPIA", or someone asks "DPIA screening", "is this high risk processing", "data protection impact assessment threshold", "privacy impact screening for this feature", "does our AI feature need a DPIA" or "document why we did not do a DPIA". Describes the processing with numbers, tests it against the GDPR cases, the supervisory authority's list and the EDPB-endorsed criteria, reaches a reasoned outcome (required, recommended, not required) with a re-screen trigger, and keeps a screening log for accountability.
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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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Validate and scientifically interpret completed research runs, including negative, null, boundary, confounded, failed, and inconclusive outcomes. Use after execution to assess evidence and claim impact; not to edit methods or schedule experiments.
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Restate a problem off the framing it arrived in. Use when a framing came by default, not by choice — every option inside it loses, or an ask names a step or an artifact and leaves the outcome it serves unnamed.
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Multi-step recovery strategy to turn a specific negative review into a positive outcome
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Create consistent nn_cfd W&B experiment comparison reports from selected run IDs using wandb and wandb_workspaces. Use this whenever the user asks to report experiment results, create a W&B Report, compare CFD training/eval runs, document baseline-vs-variant outcomes, reproduce the project experiment report template, or publish a leaderboard-style report for milieu/nn_cfd.
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Apply the Minimum Sufficient Work (MSW) principle through the MSW Kernel to scope, execute, verify, and stop agent work. State the requested outcome and smallest proof, admit a claim only when deleting it would leave the contract unmet or unproven, do and prove each necessary claim, and halt at the fixed point. Use for implementation, debugging, review, research, planning, documentation, or any task where scope control, proportional evidence, or avoiding overwork matters. Also use when the user says MSW, Minimum Sufficient Work, do only what is necessary, avoid over-engineering, or stop when the result is proven.
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Record one authorized non-remediation terminal outcome in Proto UI's autonomous-maintenance state. Use after a supported no-finding result, an independently rejected finding, or a blocked mission whose terminal evidence is complete.
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Fires after fixing any non-trivial bug or regression. Asks: could the graph have caught this as a guarantee? Pairs with reflection-in-and-on-action — picks up after "earliest catchable signal" and asks the next question: was that signal expressible in graph? Triggers: (1) after any non-trivial bug fix is verified working, (2) after a regression report (something used to work, broke), (3) during step 6 (knowledge extraction) of the workflow, (4) when reflection-on-action surfaces a "would have been catchable" signal. Outcome is a triage decision (graph-reachable? rule expressible? rule sound?) and either a draft Linear issue + guarantee proposal, or a recorded "graph capability gap" note. Never auto-creates guarantees.
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Use when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, deferred scorers, or outcome-based evaluation for the agent.
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Fetch the team's shared skills — reset each disposable team clone to origin/main — and show the outcome per team as a Markdown board from the terum-skills CLI. Use when the user wants the newest catalogue, receipts or roster before listing, searching or installing.
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Expert prompt engineer specializing in designing, optimizing, and managing prompts for large language models. Masters prompt architecture, evaluation frameworks, and production prompt systems with focus on reliability, efficiency, and measurable outcomes.
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Build applications where agents are first-class citizens. Use this skill when designing autonomous agents, creating MCP tools, implementing self-modifying systems, or building apps where features are outcomes achieved by agents operating in a loop.
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Use when creating /goal directives, reusable testable outcome-oriented success standards for agentic workflows. Use whenever the user asks to create a goal, write a /goal directive, define what good looks like, set success criteria for an agent, define a reusable instruction, create a quality standard, write acceptance criteria for an agent workflow, or specify done conditions.
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Read every application.yaml in the workspace and report the state of the search — the ranked queue, what is waiting on you, what has gone quiet, and the regression of the pipeline's own predictions against actual outcomes. Writes the calibration findings back into job_search.md and preferences.yaml.
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Use for hands-on repository work when a quiet, risk-scaled engineering method can improve design, execution strategy, implementation, material incorporation, debugging, review, delegation, workspace lifecycle, or verification. Keep clear local changes direct, preserve the complete requested outcome, and add method only where risk or uncertainty justifies it. Do not use for general technical explanations, simple file lookup, casual discussion, or non-engineering writing.
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Choose responsibility for substantial work before deep execution: keep one responsibility's coupled parts in a single lane, and hand a bounded or independent lane to a worker when clean context, an unknown cause, independent review, or coordinator attention materially improves the outcome.
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Form and maintain execution strategy when order, effort, evidence timing or recovery materially affects the outcome, even with a settled goal. Reuse durable task records when continuity needs them; keep clear work direct.
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Run a reusable opportunity-to-artifact workflow: discover unmet needs, set up workspace factories, schedule recurring commander/worker/reporter prompts, batch-refine many items, optionally use SQLite state, build small artifacts, review quality, and track outcomes. Use when the user wants to repeatedly create apps, games, products, content, or experiments from market/user needs.
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