「candidate」の検索結果

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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Use the moment a request is underspecified and you are about to act on an ASSUMED goal. Enumerate the candidate goals the request could mean, project the action or artifact each one implies, and check whether they diverge: if two or more plausible goals lead to materially different actions or artifacts, ask exactly ONE targeted clarifying question; otherwise proceed on the most-likely goal and state the assumption in one line. Clarification is an evidence-producing action, not a delay — but one question, never an interrogation. Distinct from gsd-spec-phase (GSD-phase-bound, emits a heavy SPEC.md) and intent-router (fetch strategy, not goal disambiguation). Backed by agent goal-state inference (arxiv 2606.16813v1). Triggers on acting under an assumed goal when the request admits more than one materially different reading.

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Retroductive hypothesis sampling via MCMC: given observations, importance-sample explanations weighted by P(observation|hypothesis) using Gay.jl colored particles as hypothesis candidates.

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Single-pass SOLID review — dispatches one reviewer over the complete ruleset, emitting the fixed candidate schema.

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Assess and refactor oversized or multi-domain skills. First determines whether splitting or references/ extraction is appropriate — then executes the correct action. Use when a skill exceeds token thresholds (SK006/SK007) or covers multiple independent domains. Performs candidate assessment before any structural changes; cohesive single-intent skills are redirected to references/ extraction instead of splitting. When splitting is warranted — domain analysis gate, split plan, new SKILL.md generation, validation, and backwards-compatible facade conversion.

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Use when creating or updating the project skill discovery config — generates or regenerates .dh/skill_discovery.yaml by scanning the repo to infer tech stack, inventorying installed skills via npx skills list, loading candidate skill content before suggesting, and writing a config-driven skill injection file. Triggers on /dh:setup-skill-discovery invocations and programmatic --auto calls from add-new-feature Phase 3.

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Integrates patterns from external sources (URLs or local files) into local skills, agents, and plugins. Use when comparing external agent definitions against local equivalents, extracting best practices from frameworks like GSD or BMAD-METHOD, enhancing local skills with external workflow patterns, or adding interoperability with external tool ecosystems. Runs a 3-phase workflow — parallel candidate mapping, contextual enhancement, and validation — with source attribution and backlog tracking for deferred enhancements.

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Build an interactive account org chart, leadership map, buying committee, or manager/report neighborhood around a person. Use for decision makers, reporting-line candidates, stakeholder mapping, and warm intro paths. Skip standalone email lookup and unrelated cross-company prospecting.

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Use when a set of candidate Skills or app capabilities is technically valid but still needs to become a clear, useful ChatGPT/Codex Plugin that people can understand, discover, and invoke for real work.

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Run a daily job search and auto-apply session for the candidate in profile/profile.md. Use when the user runs /seekter-run or asks to start applications, search for jobs, run today's scan, or apply to postings.

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Set up Seekter for a new candidate. Interviews the user one question at a time and writes profile/profile.md, profile/settings.json and profile/documents/. Use when the user runs /seekter-init, says "set up seekter", or when profile/profile.md is missing.

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Use whenever the user wants to find, shortlist, vet, or enrich US recruiting and staffing firms — executive search/retained search, RPO, tech/sales/healthcare recruiting, contingent/contract staffing, and temp staffing. Triggers on "find me an executive search firm for a CFO search", "shortlist three retained-search boutiques in NY focused on tech", "we need RPO support for a 50-engineer hiring push", or "pull contact info for these 8 staffing firm domains", even when described indirectly (need help hiring at scale, executive recruiter for senior roles). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Skip when the user wants to hire someone as their own employee (job-board questions, in-house recruiter hires, "where should I post the role"), individual job-seekers looking for recruiters to represent them, candidate-side career coaching, non-US firms, individual freelance recruiters.

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Evaluate an open source project's AGENTS.md and produce a corpus entry for the /agents-md directory. Use when adding a project to the directory, refreshing an existing entry, or running a batch of candidate repositories.

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Run a learning retrospective on the Kai harness. Mines gate-failure logs and 30-day performance results into lessons, triages candidate lessons (promote/keep/retire), and graduates repeated lessons into enforced gate checks with golden corpus cases. Use when "retro", "what have we learned", "triage lessons", "promote lessons", "why does this keep failing", "harness retrospective", or monthly / after any heavy content sprint.

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Generate multiple app, favicon, or website icon candidates with either the local QM Icon Studio CLI or Codex built-in image generation with reference icons, then present a contact sheet so the user can choose. Use when building websites or apps and needing reusable icon options.

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Analyse files for ArchitectKB template repository suitability

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Use when evaluating, comparing, installing, or recommending a Claude Code plugin, Agent Skill, MCP server, agent toolkit, or GitHub-hosted AI extension, especially when popularity is weak evidence or the candidate requests shell, network, filesystem, or credential access.

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Triages AWS CloudWatch alarms by correlating alarm state changes with CloudTrail events and EC2 instance health using boto3. Classifies alarms by severity, identifies root cause candidates, and updates OpsGenie alerts.

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Use when Codex is already in the validation phase of a security scan or the user explicitly asks to determine whether one or more candidate security findings are valid. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.

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Use only when the user explicitly requests verification that a security fix remediates a reported vulnerability. Do not invoke automatically while implementing fixes, reviewing ordinary code changes, or running tests. Do not use for non-security fixes, candidate finding validation, or full repository scans.

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Use when Codex is already in the finding-discovery phase of a security scan or the user explicitly asks to discover candidate security findings in a repository or code change. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.

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Adds new visual examples to the tdmcp prompt cookbook documentation — both the English and Portuguese versions. Use when asked to: add more cookbook examples, create visual examples for the docs, show more surprising things you can do, add entries to the prompt cookbook, expand the cookbook, or any request to extend the cookbook with new prompts and results. Runs a 3-phase pipeline: curate candidates → write EN → write PT.

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Consolidate the td-trend-scout reports under `_workspace/hype-scout/` into one prioritized HYPE_TOOL_BACKLOG.md — dedupe trends across surfaces, vet each candidate against the real tdmcp codebase (layer fit, operator coverage, bridge work), rank by Hype × Build-Ease, and emit a 'Ready for tdmcp-pipeline' top-5. Use when a td-hype-synthesizer agent is consolidating scouts at the end of the tdmcp-hype-scout harness.

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Scout ONE external surface of the TouchDesigner community for what's HYPED in 2025-2026 that could become a tdmcp tool. Use when a td-trend-scout sub-agent is assigned a surface (community-showcase, tutorials, generative-ai, hardware-interactive, or vfx-aesthetics) during the tdmcp-hype-scout harness — produces `_workspace/hype-scout/01_scout_<surface>.md` with cited trend candidates ranked by hype intensity, recency, and build-ease in tdmcp.

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。