recruitment-pipeline – Other skill for AI agents
Recruitment pipeline: candidate, position, stage, source, applied and interview dates, interview score, notice period and offer. Use for hiring tracking.
Install the recruitment-pipeline skill with one command
Pick the tool you use, copy the command and paste it in your terminal. The skill lands in that tool's skills folder and is ready in your next session.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/recruitment-pipeline-d1297.tgz | tar xz -C ~/.claude/skills
Call it in Claude Code: /recruitment-pipeline – then describe the task you want done.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/recruitment-pipeline-d1297.tgz | tar xz -C ~/.codex/skills
Call it in Codex: $recruitment-pipeline – then describe the task you want done.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/recruitment-pipeline-d1297.tgz | tar xz -C ~/.hermes/skills
Call it in Hermes Agent: /recruitment-pipeline – then describe the task you want done.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/recruitment-pipeline-d1297.tgz | tar xz -C ~/.deepcode/skills
Call it in DeepSeek: /recruitment-pipeline – then describe the task you want done.
SKILL.md content
Recruitment Pipeline
What it is: Full hiring process.
Overview
Works out the smallest useful Recruitment Pipeline setup for the business in front of it, then
builds it only when asked. The default output is a short recommendation, not a
spreadsheet. Artifacts - CSV, SQL DDL, JSON Schema, Notion mapping - are produced on
request, from one field list so they cannot drift apart.
Layer: Layer 2: Acquire. Fits: Growth stage. Table code: n/a.
When to Use This Skill
- recruitment pipeline
- hiring tracker
- applicant tracking spreadsheet
- interview scorecard
Also use it when the user says "full hiring process", or describes the same process happening in a
spreadsheet, a document or someone inboxes.
Do not use it for: payroll calculation, tax filing, or legal advice. This skill produces
empty templates only - it never holds or processes real employee or customer data.
How It Works
Follow the shared execution contract. The module-specific rules below define only domain fields, decisions, calculations, and safety constraints.
Step 1 - Identify intent
Read the request and pick the intent before asking anything.
- "set up" or "build" or "create" -> the user wants artifacts; go to Step 2.
- "our process is ..." or "it is in a sheet" -> the user wants to move an existing process; capture it, then Step 2.
- "is this right" or "review" or "audit" -> the user wants a check, not a build; answer from what they share.
- "how do I ..." -> advice question; answer directly and offer the build only if it helps.
Ask only if this is the highest-value missing fact; otherwise proceed without an opener:
> Q: How many roles are open right now?
Step 2 - Ask only what is missing
Skip anything the user already answered, in any earlier message. Ask the rest one at a
time, and stop as soon as the remaining answers would not change the output.
- Roles - How many open? / Which departments? / How many applicants each?
- Process - How many interview rounds? / Who interviews? / Who decides?
- Data - Scores or comments? / CVs stored where? / Timeline tracked?
- Current process - How do you track it today? / Spreadsheet or ATS? / What is slow?
- Outcome - What do you need? / Pipeline view, time-to-hire or both?
Never invent an answer. If the user does not know, record it as unknown and carry on.
Step 3 - Hold the internal context
Hold the answers in this shape. It stays internal - it is not shown to the user unless
they ask, and it never carries a value the user did not give.
module: recruitment-pipeline
intent: null # setup | advice | review | fix | build | convert | export
scale: null # Starter | Growth | Scale, only if the answer changes it
areas:
"Roles": null
"Process": null
"Data": null
"Current process": null
"Outcome": null
requested_outputs: [] # csv | sql | json | notion | xlsx - requested formats only
confirmed_facts: [] # only what the user actually said
open_questions: [] # the unanswered ones, in the order worth askingStep 4 - Recommend the smallest workflow
If an artifact was requested, build it after resolving essential missing facts. Otherwise give a short recommendation and offer the relevant artifact.
Recommended approach: A pipeline works as stages with a clear exit criterion. Add scoring only if two interviewers need to compare on the same scale.
Why this one: The value of a pipeline is knowing where candidates stall. That needs stage transitions with dates, not a CV folder.
Workflow: Apply → Screening → Interview → Offer → Hired
Step 5 - Build only on request
Once the user asks for it, derive the fields from the confirmed context and emit the
requested artifacts. For machine-readable text, keep prose outside the data; for files,
provide a usable link. Report material validation failures or limitations separately.
**A selected Notion output is rendered by notion-manual-import, so route the
Notion step there.** When the user selects Notion, hand that step to
@notion-manual-import: it holds the CSV, the property
mapping, the import steps and the verification checklist, and it renders the Field
Reference below instead of defining a table of its own. Do not restate the mapping
here and do not improvise the import steps. Manual CSV and mapping outputs need no
connection. For requested workspace changes, follow the shared contract: verify actual
tool access and the target before writing. A user saying "connected" is not tool evidence.
Never ask for a Notion password or token.
For an Excel-compatible CSV, use UTF-8 with a byte order mark so Excel opens the
text correctly. A CSV is not an .xlsx workbook; create .xlsx only when the user
requests a workbook.
A CSV carries no types, so after it, name the columns
that need a number, date or currency format applied.
Candidate Name,AI Match Score,Department,Email,Experience (Years),Hired Employee,Interview Date,Interview Score,Interviewer,Notes,Notice Period,Offered Salary,Currency,Phone,Position,Applied Date,Rec ID,Resume URL,Salary Expectation,Source,Stage
Karan Malhotra,0.82,Delivery,[email protected],6,,,,,"Application received; interview and offer are not yet recorded.",60 days,,INR,+91 98xxxxxx21,Delivery Manager,2026-01-15,,https://example.com/cv.pdf,1500000.00,Referral,AppliedCREATE TABLE recruitment_pipeline (
candidate_name VARCHAR(255),
ai_match_score NUMERIC,
department VARCHAR(255),
email VARCHAR(255),
experience_years NUMERIC,
hired_employee VARCHAR(255), -- relation -> target record
interview_date DATE,
interview_score NUMERIC,
interviewer VARCHAR(255),
notes TEXT,
notice_period VARCHAR(255),
offered_salary NUMERIC(14,2),
currency VARCHAR(255),
phone VARCHAR(255),
position VARCHAR(255),
applied_date DATE NOT NULL,
rec_id SERIAL PRIMARY KEY,
resume_url TEXT,
salary_expectation NUMERIC(14,2),
source VARCHAR(255),
stage VARCHAR(100) NOT NULL,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "Recruitment Pipeline",
"type": "object",
"additionalProperties": false,
"properties": {
"Candidate Name": {
"type": "string"
},
"AI Match Score": {
"type": "number"
},
"Department": {
"type": "string"
},
"Email": {
"type": "string",
"format": "email"
},
"Experience (Years)": {
"type": "number"
},
"Hired Employee": {
"type": "string"
},
"Interview Date": {
"type": "string",
"format": "date"
},
"Interview Score": {
"type": "number"
},
"Interviewer": {
"type": "string"
},
"Notes": {
"type": "string"
},
"Notice Period": {
"type": "string"
},
"Offered Salary": {
"type": "number"
},
"Currency": {
"type": "string"
},
"Phone": {
"type": "string"
},
"Position": {
"type": "string"
},
"Applied Date": {
"type": "string",
"format": "date"
},
"Rec ID": {
"type": "integer"
},
"Resume URL": {
"type": "string",
"format": "uri"
},
"Salary Expectation": {
"type": "number"
},
"Source": {
"type": "string"
},
"Stage": {
"type": "string"
}
},
"required": [
"Applied Date",
"Stage"
]
}| CSV column | Notion property | Set after import |
|---|---|---|
| Candidate Name | Title | Use as the database title |
| AI Match Score | Number | Convert to Number |
| Department | Text | Leave as Text |
| Email | Email | Convert to Email |
| Experience (Years) | Number | Convert to Number |
| Hired Employee | Relation (link to the target database) | Convert to Relation, link to the target database |
| Interview Date | Date | Convert to Date |
| Interview Score | Number | Convert to Number |
| Interviewer | Text | Leave as Text |
| Notes | Text | Leave as Text |
| Notice Period | Text | Leave as Text |
| Offered Salary | Number (format: currency) | Convert to Number, set format to Currency |
| Currency | Text | Leave as Text |
| Phone | Text | Leave as Text |
| Position | Text | Leave as Text |
| Applied Date | Date | Convert to Date |
| Rec ID | Text (preserve source ID) | Keep imported IDs as Text; optionally add a separate Unique ID property |
| Resume URL | URL | Convert to URL |
| Salary Expectation | Number (format: currency) | Convert to Number, set format to Currency |
| Source | Text | Leave as Text |
| Stage | Select (add options after import) | Convert to Select, add options: "Applied", "Screening", "Interview", "Offer", "Hired", "Rejected", "Withdrawn" |The rows above are documentation examples only. Emit empty templates unless the user explicitly requests examples. Money stays currency, dates stay date,
and anything pointing at anoth
… (full content is in the download package)
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