--- name: talent-sourcing description: | Finds qualified candidates for a role by searching LinkedIn, Indeed, GitHub, and other professional platforms using Nimble Web Search Agents. Accepts a job description, role title, or freeform request and returns a ranked candidate list with profiles, skills, and contact signals. Use this skill when the user wants to find, source, or recruit candidates for a role. Common triggers: "find candidates for", "source engineers in", "who can I hire for", "find me a [role]", "recruiting for", "talent search", "find a [role] in [city]", "build a candidate list", "sourcing for [role]", "who's available for", "find potential hires". Also triggers on a pasted job description followed by a sourcing request. Do NOT use for job market research or salary benchmarking — use market-finder instead. Do NOT use for researching a single known person — use company-deep-dive or meeting-prep instead. allowed-tools: - Bash(nimble:*) - Bash(date:*) - Bash(cat:*) - Bash(mkdir:*) - Bash(python3:*) - Bash(echo:*) - Bash(jq:*) - Bash(ls:*) - Read - Write - Edit - Glob - Grep - Agent - AskUserQuestion metadata: author: Nimbleway version: 0.22.0 --- # Talent Sourcing Candidate discovery powered by Nimble Web Search Agents. User request: $ARGUMENTS **Before running any commands**, read `references/nimble-playbook.md` for Claude Code constraints (no shell state, no `&`/`wait`, sub-agent permissions, communication style). --- ## Instructions ### Step 0: Preflight Follow the transport selection + standard preflight from `references/nimble-playbook.md` — pick CLI or MCP at session start, then run the standard preflight calls (date calc, today, profile, memory index) in parallel. Also simultaneously: - `mkdir -p ~/.nimble/memory/{reports,talent-sourcing}` From the results: - CLI missing or API key unset → read `references/profile-and-onboarding.md`, stop - Tag all `nimble` CLI calls: `nimble --client-source skill-talent-sourcing `. MCP path: not yet supported — see `references/nimble-playbook.md` for status. - Profile exists → note industry keywords if any; proceed to Step 1 - No profile → fine, talent-sourcing doesn't require onboarding; proceed to Step 1 ### Step 1: Parse Request & Confirm Search Parameters Parse `$ARGUMENTS` for: - **Role** — job title or function (e.g. "Senior React Engineer", "Head of Sales") - **Location** — city, metro, region, or remote (e.g. "New York City", "remote US") - **Skills / requirements** — specific technologies, years of experience, domain expertise - **Seniority** — junior, mid, senior, staff, director, VP, C-level - **Source preference** — specific platforms (LinkedIn, GitHub, Indeed, etc.) or "all" If a full job description was pasted, extract the above fields from it. If **role** is missing or ambiguous, ask with `AskUserQuestion`: > "What role are you hiring for, and where? (e.g. 'Senior ML Engineer, remote US' > or paste a job description)" Once parameters are clear, confirm with the user using `AskUserQuestion`: > "Searching for: **[Role]** | Location: **[Location]** | Key skills: **[Skills]** > | Seniority: **[Seniority]** > > Platforms to search: LinkedIn, Indeed, GitHub (for technical roles), AngelList / > Wellfound, and professional communities. > > - **Start search** > - **Adjust parameters first**" ### Step 2: WSA Discovery Discover available Web Search Agents for candidate-sourcing platforms. Run simultaneously: ```bash nimble agent list --search "linkedin people" --limit 20 nimble agent list --search "indeed resume" --limit 20 nimble agent list --search "github profile" --limit 20 nimble agent list --search "wellfound talent" --limit 20 ``` Filter results for `entity_type: SERP` or `entity_type: PDP`. Prefer `managed_by: "nimble"`. Validate promising agents with: ```bash nimble agent get --template-name {name} ``` Cache discovered WSA names and required params. If no WSAs found for a platform, fall back to `nimble search` for that platform. ### Step 3: Parallel Candidate Search (Sub-Agents) Spawn `nimble-researcher` agents (`agents/nimble-researcher.md`) with `mode: "bypassPermissions"`, max 4 concurrent. Assign one agent per platform: **Agent 1 — LinkedIn** Search for people matching the role criteria. Use Boolean-style query construction: ```bash nimble search --query "site:linkedin.com/in [Role] [Location] [Key Skills]" \ --max-results 15 --search-depth fast nimble search --query "[Role] [Location] linkedin profile [Skill1] [Skill2]" \ --max-results 10 --search-depth fast ``` If a LinkedIn WSA was discovered in Step 2, use it instead with the role title, location, and skill keywords as inputs. **Agent 2 — Indeed / Resumes** ```bash nimble search --query "site:indeed.com resume [Role] [Location] [Key Skills]" \ --max-results 10 --search-depth fast nimble search --query "[Role] resume [Location] [Key Skills]" \ --max-results 10 --search-depth fast ``` **Agent 3 — GitHub (technical roles only)** Skip this agent for non-technical roles (e.g. Sales, Marketing, Operations). ```bash nimble search --query "site:github.com [Role] [Location] [Key Skills]" \ --max-results 10 --search-depth fast nimble search --query "github [Key Skills] developer [Location] open to work" \ --max-results 10 --search-depth fast ``` **Agent 4 — AngelList / Wellfound + Communities** ```bash nimble search --query "site:wellfound.com [Role] [Location] [Key Skills]" \ --max-results 10 --search-depth fast nimble search --query "[Role] [Location] open to work OR seeking opportunities \ [Key Skills]" --max-results 10 --search-depth fast ``` Each agent returns: candidate name (if available), profile URL, current title, location snippet, inferred skills, availability signals ("open to work", "seeking", "available") with event date (if available) and source URL. ### Step 4: Deep Profile Extraction For the top candidates identified in Step 3 (aim for 10–20 unique profiles across all platforms), extract full profile details. Run all extractions simultaneously: ```bash nimble extract --url "[profile-url]" --format markdown ``` From each extracted profile, pull: - **Full name** - **Current role & company** - **Location** - **Skills / tech stack** - **Experience summary** (years, notable employers) - **Education** - **Availability signals** (open to work, recent job change, posting activity) - **Contact signals** (email, personal site, GitHub handle) For extraction failures, follow the fallback pattern in `references/nimble-playbook.md`. If a profile is behind a login wall and extraction fails, keep the search-snippet summary instead — do not skip the candidate. **Extraction budget:** extract up to 15 profiles. If more than 15 candidates were found in Step 3, prioritize by relevance score (seniority match + skill overlap + location match) before extracting. ### Step 5: Score & Rank Candidates Score each candidate (1–10) using these weighted signals: | Signal | Weight | |--------|--------| | Role / title match | 30% | | Skill overlap with requirements | 30% | | Location match | 20% | | Seniority match | 10% | | Availability signals | 10% | Group candidates into tiers: - **Tier 1 (Strong match, 7–10):** All required signals present - **Tier 2 (Partial match, 4–6):** Most signals present, 1–2 gaps - **Tier 3 (Stretch, 1–3):** Worth reviewing if Tier 1/2 list is thin ### Step 6: Output Before presenting results, check `~/.nimble/memory/talent-sourcing/[role-slug].md` — if a candidate was surfaced in a prior run, mark them `(previously surfaced)` rather than re-presenting them as new. Present a structured candidate report: ``` ## Candidate Report: [Role] in [Location] Searched: LinkedIn, Indeed, GitHub, Wellfound Found: [N] candidates | Tier 1: [N] | Tier 2: [N] | Tier 3: [N] **TL;DR:** [2-3 sentence summary of the strongest candidates and any notable patterns] --- ### Tier 1 — Strong Match #### 1. [Name] — [Score]/10 - **Current role:** [Title] at [Company] - **Location:** [Location] - **Skills:** [Skill1], [Skill2], [Skill3] - **Experience:** [X years, notable employers] - **Availability:** [signal] — [event date or "date unknown"] — [source URL] - **Profile:** [URL] - **Contact signals:** [email / personal site / GitHub] ... --- ### What This Means [1-2 sentences on hiring outlook: supply/demand signal, speed recommendation, any standout sourcing channel] ``` Omit fields where data is unavailable. Do not fabricate details — use "unknown" for missing fields. Add a one-sentence **"Why this candidate"** note for each Tier 1 result. ### Step 7: Save to Memory Make all Write calls simultaneously: - Report → `~/.nimble/memory/reports/talent-sourcing-{YYYY-MM-DD}.md` (full candidate report with all tiers) - Per-role → `~/.nimble/memory/talent-sourcing/[role-slug].md` (candidate list; write or update) - Profile → update `last_runs.talent-sourcing` in `~/.nimble/business-profile.json` using the python3 snippet in `references/profile-and-onboarding.md`. Skip if the file does not exist. Update `~/.nimble/memory/talent-sourcing/index.md` with a row for this search. Follow the wiki update pattern from `references/memory-and-distribution.md`. ### Step 8: Share & Distribute **Always offer distribution — do not skip this step.** Follow `references/memory-and-distribution.md` for connector detection, sharing flow, and source links enforcement. ### Step 9: Follow-ups Offer next steps using `AskUserQuestion`: > **What's next?** > - **Go deeper on a candidate** — extract full profile + find contact info > - **Expand search** — broaden location, relax seniority, try more platforms > - **Narrow search** — add a required skill or tighten location > - **Export list** — save as CSV or formatted doc > - **Done** **Sibling skill suggestions:** > - Run `company-deep-dive` on a candidate's current employer for deal context > - Run `meeting-prep` before reaching out to a Tier 1 candidate --- ## Error Handling See `references/nimble-playbook.md` for the standard error table. Skill-specific handling: - **Profile behind login wall:** Keep search-snippet summary; note "full profile unavailable — LinkedIn/Indeed login required" in the candidate entry. - **< 5 total candidates found:** Notify the user, suggest broadening location to remote or relaxing seniority, then ask whether to re-run with adjusted params. - **Search 500 on a platform:** Retry once with a simplified query; if still failing, skip that platform and note it in the report header. - **GitHub agent skipped for non-technical role:** Note "GitHub not searched for this role type" in the report header.