mentorship-program – Other skill for AI agents
Mentorship register: mentor and mentee pair, department, focus area, mentee goal, session counts, last and next session, overall rating, progress notes and status. Use for mentorship tracking.
Install the mentorship-program 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/mentorship-program-164e6.tgz | tar xz -C ~/.claude/skills
Call it in Claude Code: /mentorship-program – then describe the task you want done.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/mentorship-program-164e6.tgz | tar xz -C ~/.codex/skills
Call it in Codex: $mentorship-program – then describe the task you want done.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/mentorship-program-164e6.tgz | tar xz -C ~/.hermes/skills
Call it in Hermes Agent: /mentorship-program – then describe the task you want done.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/mentorship-program-164e6.tgz | tar xz -C ~/.deepcode/skills
Call it in DeepSeek: /mentorship-program – then describe the task you want done.
SKILL.md content
Mentorship Program
What it is: Internal mentoring.
Overview
Works out the smallest useful Mentorship Program 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 5: Develop. Fits: Scale stage. Table code: n/a.
When to Use This Skill
- mentorship program
- mentor matching
- internal mentoring tracker
- mentor mentee pairs
Also use it when the user says "internal mentoring", 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 people want to be mentored?
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.
- People - Mentees and mentors? / How many pairs? / Same team or cross-team?
- Programme - How long? / Session cadence? / Structured or informal?
- Matching - How matched today? / By skill or interest? / Who approves?
- Current process - Anything running now? / How tracked? / What failed before?
- Outcome - What do you need? / Matching, session tracking or feedback?
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: mentorship-program
intent: null # setup | advice | review | fix | build | convert | export
scale: null # Starter | Growth | Scale, only if the answer changes it
areas:
"People": null
"Programme": null
"Matching": 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: Match on a stated goal rather than availability, and track only sessions and a closing rating.
Why this one: Mentorship fails when pairs are assigned by convenience and never meet. A stated goal for the mentee is what makes a pair worth recording.
Workflow: Mentee goal → Match → Sessions → Mid review → Close with rating
Session and end dates may be unknown for proposed pairs or completed programmes; omit them rather than inventing dates. Validate actual sessions occur on/after start and before an actual end; counts must be nonnegative whole sessions. Record whether End Date is planned or actual. Agree matching consent and feedback access; a private development note or informal rating must not become an automatic performance decision.
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.
Mentorship Pair,Department,End Date,Focus Area,Last Session,Mentee Goal,Mentee Name,Mentor Name,Mentorship ID,Next Session,Overall Rating,Progress Notes,Sessions Completed,Sessions Planned,Start Date,Status
Priya Nair + Rohit Verma,Delivery,2026-11-27,Stakeholder management and commercial conversations.,2026-03-08,Lead two client calls end to end this quarter.,Priya Nair,Rohit Verma,,2026-03-22,4,Two of four goals on track. Delegation still needs work.,5,12,2026-02-02,ActiveCREATE TABLE mentorship_program (
mentorship_pair VARCHAR(255),
department VARCHAR(255),
end_date DATE,
focus_area VARCHAR(255),
last_session DATE,
mentee_goal VARCHAR(255),
mentee_name VARCHAR(255), -- relation -> target record
mentor_name VARCHAR(255), -- relation -> target record
mentorship_id SERIAL PRIMARY KEY,
next_session DATE,
overall_rating VARCHAR(255),
progress_notes TEXT,
sessions_completed NUMERIC NOT NULL,
sessions_planned NUMERIC NOT NULL,
start_date DATE NOT NULL,
status VARCHAR(100) NOT NULL,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);
CREATE INDEX idx_mentorship_program_status ON mentorship_program (status);{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"title": "Mentorship Program",
"type": "object",
"additionalProperties": false,
"properties": {
"Mentorship Pair": { "type": "string" },
"Department": { "type": "string" },
"End Date": { "type": "string", "format": "date" },
"Focus Area": { "type": "string" },
"Last Session": { "type": "string", "format": "date" },
"Mentee Goal": { "type": "string" },
"Mentee Name": { "type": "string" },
"Mentor Name": { "type": "string" },
"Mentorship ID": { "type": "integer" },
"Next Session": { "type": "string", "format": "date" },
"Overall Rating": { "type": "string" },
"Progress Notes": { "type": "string" },
"Sessions Completed": { "type": "number" },
"Sessions Planned": { "type": "number" },
"Start Date": { "type": "string", "format": "date" },
"Status": { "type": "string" }
},
"required": [
"Sessions Completed",
"Sessions Planned",
"Start Date",
"Status"
]
}| CSV column | Notion property | Set after import |
|---|---|---|
| Mentorship Pair | Title | Use as the database title |
| Department | Text | Leave as Text |
| End Date | Date | Convert to Date |
| Focus Area | Text | Leave as Text |
| Last Session | Date | Convert to Date |
| Mentee Goal | Text | Leave as Text |
| Mentee Name | Relation (link to the target database) | Convert to Relation, link to the target database |
| Mentor Name | Relation (link to the target database) | Convert to Relation, link to the target database |
| Mentorship ID | Text (preserve source ID) | Keep imported IDs as Text; optionally add a separate Unique ID property |
| Next Session | Date | Convert to Date |
| Overall Rating | Text | Leave as Text |
| Progress Notes | Text | Leave as Text |
| Sessions Completed | Number | Convert to Number |
| Sessions Planned | Number | Convert to Number |
| Start Date | Date | Convert to Date |
| Status | Select (add options after import) | Convert to Select, add options: "Proposed", "Active", "Paused", "Completed", "Ended" |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 another table stays relation.
Field Reference
| # | Field | Type | SQL | JSON Schema | Notion | CSV example |
|---:|---|---|---|---|---|---|
|
… (full content is in the download package)
Related skills
- meta-tag-comparison-tool – Meta Tag Comparison Tool. Use when working with HTML or extracted metadata from two or…
- moatmri – Analyze AI disruption pressure across a business, map competitive exposure, and produce…
- mobile-games – Mobile game development principles. Touch input, battery, performance, app stores.
- moltnode-chain-reader – Read 31 blockchains over keyless JSON-RPC with MoltNode (balances, blocks, contract…
- molykit – CRITICAL: Use for MolyKit AI chat toolkit. Triggers on: BotClient, OpenAI, SSE…
- monte-carlo-performance-diagnosis – Diagnoses pipeline performance issues -- slow jobs, expensive queries, latency trends…
- monthly-closing-statements – Monthly closing register: period dates, cash/bank/party/inventory/TDS reconciliation…
- mtls-configuration – Configure mutual TLS (mTLS) for zero-trust service-to-service communication. Use when…