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

Run the preflight pattern from

references/nimble-playbook.md

(5 simultaneous Bash calls: date calc, today, CLI check, profile load, index.md load).

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
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

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

~/.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.

talent-sourcing

NPX Install

Tags

SKILL.md Content

Talent Sourcing

Instructions

Step 0: Preflight

Step 1: Parse Request & Confirm Search Parameters

Step 2: WSA Discovery

Step 3: Parallel Candidate Search (Sub-Agents)

Step 4: Deep Profile Extraction

Step 5: Score & Rank Candidates

Step 6: Output

Step 7: Save to Memory

Step 8: Share & Distribute

Step 9: Follow-ups

Error Handling