Results for “candidates”

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:review or asks what has been learned and what should be promoted or pruned.

Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.

Evaluate Google AI Overview presence and click-cannibalization risk for BID-passing Pleasur.ai Stage 01 keyword candidates using current Ahrefs evidence. Use after BID validation and before prioritization, in either presence-only or human-approved deep-completeness mode.

Validate Pleasur.ai Stage 01 blog-keyword candidates with the Business potential, Intent, and Difficulty method using current Ahrefs evidence. Use before AIO vetting and prioritization to reject poor business fits, mismatched search intent, and SERPs the brand cannot realistically win.

Find adjacent vendors and subs (not top market primes) who fill a capability gap against a displacement target using USASpending flows and SAM entity signals. Use when user defines a teaming gap and wants vault-ready partner shortlist with citations.

Populate symbol database with crypto symbols before training. Trigger when: (1) live trader fails with 'CRYPTO VIOLATION', (2) no crypto models in models/rl_symbols/, (3) db.get_candidates(asset_types=['crypto']) returns 0, (4) starting fresh training without crypto.

Extract warm prospects from competitor and industry post engagers across LinkedIn, Twitter, and blogs

Extract candidate name, contact details, work history, and skills from resumes.

Track key contacts who change jobs and draft warm outreach to movers at ICP companies

搜索 NPC 角色。

Improve existing content and close the learning loop without cannibalizing new-content work.

Optimize a LinkedIn profile — headline, about, experience, and keywords — for recruiter search and credibility.

Find keyword gaps, SERP opportunities, and low-competition high-intent keywords

Generate 5–10 business idea candidates from a blank page or a founder's domain context — using pain mining, jobs-to-be-done, trend × capability mapping, constraint relaxation, adjacency search, and founder-market-fit prompts. Each candidate is a structured idea card (segment, JTBD, current alternative, why-now, distribution wedge, monetisation, "feels like"). Load when the user asks to generate business ideas, brainstorm startup ideas, find ideas to work on, says "what business should I start", "give me startup ideas", "I don't know what to build", "ideate ventures", "blank-page idea generation", "find me a startup idea", "explore business opportunities". Sub-skill of `venture-exploration`. Hard-bans "Uber for X" / "AI for X" with no specific JTBD, "everyone" segments, and idea cards missing any of the 7 required fields. Does NOT design or evaluate ideas generated — for that use `idea-evaluation`.

Search for business grants, government funding, startup accelerators. Auto-fill applications.

Execute the full AgentHub competition lifecycle in a single command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.

Build and maintain one coherent company story across all audiences — employees, investors, customers, candidates, and partners. Detects narrative contradictions and ensures the same truth is framed for each audience's needs.

Comprehensive web application testing patterns with Playwright selectors, wait strategies, and best practices

Naming Strategist

Outreach triggered by funding events with post-funding needs analysis and personalized drafts

Build a USASpending relationship snapshot for the incumbent and buying agency on a pursuit row. Use when user needs award flows and rel counts before battlecard or teaming work — deterministic from DuckDB bulk.

Screens recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position) and assigns A/B/C/D grades to identify momentum trade candidates.

Analyzes Stockbee-style Day 1 Episodic Pivot candidates by scoring catalyst quality alongside price/volume confirmation, gap/range expansion, volume shock, neglect/revaluation context, liquidity, and risk to the EP-day low.

Extract coverage-checked, independently testable MoonSpec user stories from a technical or declarative design and write breakdown output under artifacts/story-breakdowns (gitignored). Use when the user asks to run or reproduce `/moonspec.breakdown`, split a broad design into one-story candidates, preserve source coverage, or build a coverage matrix before `/moonspec.specify`.

Turn "which leads should I work?" into a lead-by-lead work plan. Per-lead status (never-touched, engaged-not-replied, gone-cold, hot), days-dark per lead, last-touch quality, a re-engagement ranker, and a recommended next action for every lead. Built for SDRs and full-cycle reps, customizable to your CRM and your outreach tool. Trigger on "which leads should I work?", "who haven't I touched?", "show me cold leads", "hot leads to follow up on", "who's gone dark?", "re-engagement candidates", or any lead-level work-assignment question.