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
nimbleCLI calls:nimble --client-source nimble-agent-skills <subcommand>. MCP requests are attributed at the transport level — seereferences/nimble-playbook.md. - 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:
bashnimble extract:templates list --limit 100 # then filter items for "linkedin people" nimble extract:templates list --limit 100 # then filter items for "indeed resume" nimble extract:templates list --limit 100 # then filter items for "github profile" nimble extract:templates list --limit 100 # then filter items for "wellfound talent"
Filter results for entity_type: SERP or entity_type: PDP. Prefer
managed_by: "nimble". Validate promising agents with:
bashnimble extract:templates get --extract-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:
bashnimble 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
bashnimble 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).
bashnimble 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
bashnimble 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:
bashnimble 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-sourcingin~/.nimble/business-profile.jsonusing the python3 snippet inreferences/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-diveon a candidate's current employer for deal context- Run
meeting-prepbefore 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.
