425 件 ・ 関連度順
概要と使いどころ
Distill any intent input into the SPEC kernel + companions — the canonical, preservation-validated machine contract for downstream work. Use when the user says "create a spec", "distill this into a spec", "validate this spec", or "update the spec".
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Build, normalize, and maintain the user's Career Context file so downstream platform outputs stay factual and consistent. Use when the user wants an agent to consolidate CV data, LinkedIn exports, GitHub history, project summaries, bio facts, achievements, or positioning into one professional source of truth before editing platform-specific assets.
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How a downstream app consumes the shared @kolu/surface stack (@kolu/surface · surface-app · surface-remote · surface-mcp) — declaring a typed reactive surface, serving it, consuming it (SolidJS hooks or a CLI), and mirroring a remote surface over ssh. Grounded in the real consumers: kolu, drishti, odu, and the TUIs. Load when wiring a surface server/client/mirror, or reaching for makeSession / a link / the `.use()` hooks. CHANGING the framework is gated separately — `.claude/rules/surface.md` (a paired, CI-green drishti PR pinned to final kolu HEAD).
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Preflight that resolves which private key(s) to use for the warp-deploy chain across every protocol in the route (EVM / Sealevel / Cosmos / Starknet / Tron). For each protocol the user supplies either a specific key spec (GCP secret name, env var, or keystore path) or a candidate list of GCP secret names for the agent to surface and the user to pick. The agent never enumerates GCP secrets on its own and never reads secret values without an explicit user-approved pick. Persists the resolution to `~/.hyperlane/key-contexts/ticket-id.yaml` for downstream warp-deploy skills to auto-load.
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Fetch and decode a pending Safe (multisig) governance batch from the Safe Transaction Service into a chain-tagged transactions JSON file that `/warp-route-check` (or any downstream consumer with the same input shape) ingests. Safe-API integration only — no forks, no replay, no simulation.
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Foundation skill for warp-route updates. Reads the current on-chain config of a warp route via `hyperlane warp read`, enumerates every artifact (router, ProxyAdmin, ISM, hook, fee contracts) per chain, classifies each artifact's owner (auto-detects Safe / ICA / EOA; asks the user for everything else — Turnkey, Privy, MPC, custom multisig, timelock, etc.), and persists a per-artifact owner table that downstream update skills consume to know which signing path each batch needs. Read-only — no signing, no on-chain mutations.
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Classify the information-need of a query and dispatch it to the appropriate retrieval or reasoning strategy. Use before read-side memory access, before multi-strategy retrieval, or any time you'd otherwise default to "one retriever for everything". Returns a strategy label, a token budget, and a retrieval depth so downstream handlers can be specialised. Backed by Pre-Route (arxiv 2605.10235v2) and MemFlow (arxiv 2605.03312v1), which together show LLMs possess latent routing ability elicitable via a structured prompt — and that externalising the routing decision improves small-model performance by ~2x. Triggers: "route this", "what strategy", "before retrieving", "intent classification", or any query whose ideal handling depends on what KIND of question it is.
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Use before you shorten, compress, or rewrite the BODY of an existing SKILL.md or agent .md — the moment you are about to cut text to reduce length. First inventory the file's operational anchors (exact code / command / API snippets, fail-closed workflow guards, and rule / threshold statements), preserve every one, then judge the rewrite by expected downstream task cost (exploration, debugging, recovery tokens) rather than by the resulting line count. A shorter skill that strips an anchor makes the agent MORE expensive per task, so length is never the objective and rewrite is not compression. Backed by Preserving Operational Anchors When Compressing Agent Instructions (arxiv 2606.09421v2). Triggers on shortening or refactoring a skill or agent body.
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Use when you have captured session evidence — session-retro / session- observatory-live traces in .planning/patterns/, tool logs, correction records — and want to induce a reusable skill from it. Segments the traces into candidate skill units (an LLM judgment, not a deterministic parse) and decomposes each candidate into a four-part structured spec: workflow structure, execution semantics, and runtime attachments (verification, safety, rollback, state). It emits a spec object, NOT a finished SKILL.md, and hands that spec to skill-forge. It sits between skill-integration (upstream frequency detector) and skill-forge (downstream author). Backed by Agent-Trace-to-Skill Induction (arxiv 2606.06893v1). Triggers on inducing a skill from captured traces, turning a repeated pattern into a skill spec, and preparing evidence for skill-forge.
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Same reverse-thinking method as dh:rt-ica (see dh:rt-ica or dh-glossary for the definition), but localizes any MISSING input to the affected task only, as a non-blocking information-completeness pre-pass before task decomposition and plan generation. Use when grooming backlog items, generating plans, decomposing tasks under uncertainty, or working in brownfield and refactor scenarios. Produces completeness summary (APPROVED-FOR-PLANNING, APPROVED-WITH-GAPS, or BLOCKED-FOR-PLANNING), missing input report with dependency mapping, required unblock actions, and planning annotations for downstream tasks. Use dh:rt-ica instead at the S2 implementation gate where missing inputs must halt execution.
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Register a plan artifact via the MCP backlog server. Use when you produce a document or report that downstream agents or worktree-isolated environments need to retrieve — feature-context, codebase-analysis, architect, T0-baseline, TN-verification, or research artifacts. Triggers include "store an artifact", "register a plan artifact", "write a report to the backlog", "upload artifact content".
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Creates a GitHub milestone on the current repository and returns its number for downstream use. Use when starting a new sprint, release, or theme grouping of backlog items. No args triggers guided intake (title, due date, description); 'quick {title}' skips to description only. Checks for duplicates before creating. Returns milestone number for /group-items-to-milestone.
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Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.
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Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.
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Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go to text-classification.
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Maps your company's GTM motion (PLG, SLG, or Hybrid) to concrete funnel stages with inputs, outputs, and qualification criteria per stage. Detects motion type from website + Exa research, then produces pre-close + post-close stage definitions, the FETE mapping (Fit / Engagement / Tier / Eligibility), and closed-lost re-entry paths. Triggers - "funnel strategy", "funnel stages", "pipeline stages", "sales process", "lead qualification", "GTM motion mapping", or any work defining how leads flow through the business. Upstream - recommended company-context, icp-research. Downstream - feeds lifecycle-marketing, outreach-emails, content-strategy. NOT for lead scoring logic (use /lead-scoring) or lifecycle email campaigns (use /lifecycle-marketing).
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Develop positioning strategy with anchor, differentiators, status-quo alternatives, and a positioning statement. Reads from marketing/icp/ICP.md + marketing/competitors/aggregate.md. Writes to marketing/positioning/positioning.md as the canonical positioning every downstream skill reads. Triggers - "positioning", "category definition", "how do we position", "positioning strategy", "anchor + differentiators"
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Build a 10-component messaging library from your positioning + ICP + competitor aggregate. Produces positioning statement, value propositions, key differentiators, taglines, CTA variants, proof points, messaging hierarchy, status-quo alternatives. Writes to marketing/messaging/messaging.md as the canonical messaging library every downstream content / paid / outbound / lifecycle skill reads. Triggers - "product messaging", "messaging library", "value props", "messaging framework", "build messaging", "messaging components"
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Extract visual identity from screenshots (primary) or website URL (fallback) and compile into a DESIGN.md-format brand kit — YAML token frontmatter (colors, typography, rounded, spacing, components) + 8 ordered prose sections. The YAML tokens are machine-authoritative; downstream visual skills consume them directly. Writes to marketing/brand/brand-kit.md. Triggers - "brand kit", "brand identity", "visual identity", "design tokens", "extract brand", "DESIGN.md"
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Synthesize multiple per-competitor research files (produced by /competitor-research) into a single canonical threat matrix. Writes to marketing/competitors/aggregate.md as the locked canonical version every downstream skill reads. Triggers - "competitor aggregate", "synthesize competitors", "competitor matrix", "threat matrix", "lock competitor canonical"
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Build structured ICP documentation by scraping case studies, testimonials, and solutions pages from your website. Produces firmographics, champion + economic buyer personas, pain points, decision criteria, anti-ICP, customer proof points, and voice-of-customer synthesis. Writes to marketing/icp/ICP.md as the canonical ICP every downstream skill reads. Triggers - "icp research", "ideal customer profile", "persona research", "customer segments", "who are our customers", "build ICP"
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Maintain the downstream pkg/oidc OpenID Provider and portal adapter, including client configuration, code/PKCE, claims, consent rendering, response policies, provider tokens, and conformance. Excludes upstream OAuth providers and portal refresh-token transport.
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Portable Codebase Argus agent playbook for evidence-first multi-agent review of GitHub pull requests, CI failures, GitHub Actions logs, GitHub App webhook review, /argus PR comment commands, autofix branch planning, OpenAI/Claude/Gemini/Codex provider tribunals, downstream merge/rebase work, and long-lived fork sync against an upstream repository.
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Generate, rank, and gate a reusable hook bank — sourced pains × named hook formula families, scored on clarity/specificity/curiosity/proof-backing, gated at the 10/16 ad threshold, delivered in 3 lengths with per-hook provenance and downstream routing. Use when "hook bank", "generate hooks", "hook generator", "write hooks for", "ad hooks", "scroll stoppers", "opening lines", "hook ideas", "first 3 seconds", "hook matrix", "I need 50 hooks", or any request to batch-produce attention-openers for ads, social, or video.
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。