role-intake

name	role-intake
description	Turn a job posting URL or pasted JD into a structured role.md file inside applications/<app_id>/. Extracts company, role, JD text, inferred lane (founding/AI eng/FDE/full-stack/local), required vs nice-to-have skills, fit notes against Mason's profile, and an honest truthfulness-gap list. Use whenever Mason shares a job link, pastes a JD, or asks "is this worth applying to?".

role-intake

This skill converts a raw job posting (URL, pasted JD, or both) into the structured applications/<app_id>/role.md file that every downstream drafting skill reads. It's also the natural entry point for "is this worth applying to?" triage.

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When to invoke this skill

• Mason pastes a job posting URL
• Mason pastes JD text
• Mason says "look at this role" or "what do you think of this one"
• The job-sourcer skill hands off a discovered candidate role
• Mason asks "is this worth applying to?" — run role-intake and read the fit_summary back

If Mason just mentions a company without a specific posting ("I should look at Cognition"), that's not role-intake — that's either job-sourcer or a manual conversation. Ask.

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Prerequisites

Before this skill runs, an application must exist in the tracker. If one doesn't:

1. First invoke application-logger to create it via pnpm log application_created. That will produce an app_id and an applications/<app_id>/ directory.
2. Then continue with this skill against that app_id.

Never write role.md to a directory that doesn't already exist. Never invent an app_id.

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Inputs you need

Input	How to get it
app_id	From application-logger (just created) or resolved via fuzzy match against data/applications.json
Company name	From the URL's domain, from Mason, or from the JD text
Role title	From the JD or from Mason
JD text	If URL provided: delegate to a subagent with WebFetch to pull and clean it (keeps main context lean). If pasted: use as-is.
Canonical URL	The company's own careers page if possible, not LinkedIn/Indeed

Canonicalization rule: if the URL is on LinkedIn, Indeed, Wellfound, or any aggregator, have the subagent try to find the same posting on the company's own careers page (Ashby, Greenhouse, Lever, or their own site) and use that as the canonical URL. Fall back to the aggregator only if the company listing isn't findable. Mason wants to apply through company sites.

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The pipeline

1. Fetch and clean the JD (if URL provided)

Delegate to an Explore subagent with a tight prompt:

"Fetch this job posting URL and return: (1) the canonical company careers-page URL for this same role if you can find it (not the aggregator link), (2) the full JD text cleaned of navigation/chrome/boilerplate, (3) the company name, (4) the role title, (5) location/remote policy, (6) stated salary band if any. Report in under 400 words. URL: "

Reading JD URLs in the main context wastes tokens. Always delegate.

2. Read Mason's profile for matching

Read in this order (use Read tool, these are small files):

• profile/skills.md — for Tier A (claimable) and Tier B (familiar) lists
• profile/preferences.md — for target lanes, skip lanes, location/remote rules, credentials notes
• profile/projects/*.md — only if the JD mentions something specific where you need evidence (e.g. "AI pipeline experience" → read voices-that-remain.md)
• profile/comp.md — for the comp-band fit check

Do not read every profile file every time. Read targeted.

3. Extract requirements from the JD

From the JD, pull two lists:

• Required — things the JD says are must-haves ("required", "must have", "X+ years of Y", hard filters like degree requirements)
• Nice-to-have — things the JD says would be a plus, bonus, or preferred

Be literal. If the JD says "3+ years of React," that's a required 3-year count — Mason does not have 3 years of professional React, period. Do not soften that into "React experience."

4. Compute fit and truthfulness gaps

Go item by item through Required and produce one of four verdicts per item:

Verdict	Meaning
✅ Claimable	Tier A in skills.md or backed by a projects/*.md file. Safe to put on the resume.
🟡 Partial	Tier B or analogous experience (e.g. JD wants "Postgres" → Mason has Postgres in VTR, fine; JD wants "8 years Postgres" → Mason does not). Note the gap.
🔴 Gap	Mason cannot claim this at all. Must be honest about it on the resume (omit) and flag it for interview-prep.
⚠️ Hard filter	A disqualifier like "PhD required" or "5+ years at a FAANG" — these don't become gaps, they become don't-apply decisions.

If there are ≥1 hard filters, the recommended_action is "skip — hard filter" and the rest of the analysis is still written for the record but no drafting skills should run.

5. Compute lane + fit score

Lane — which of Mason's target lanes does this role fit?

• founding — founding engineer at pre-seed/Series A
• ai-engineer — AI engineer at AI-native company
• fde — forward-deployed / solutions engineer at an AI lab
• fullstack-ai — full-stack at an AI-native startup
• local-raleigh — Raleigh/RTP local role regardless of lane
• off-lane — doesn't fit any lane (should usually be skipped)

Fit score — a single integer 1–10. Rubric:

• 10 — dream role: lane-matched, agent-friendly signals, comp in stretch band, specific thing Mason can point to ("they use Codex internally", "founder's tweet about agent-directed development")
• 8–9 — strong: lane-matched, comp in target band, no hard filters, all required items Claimable or Partial
• 6–7 — plausible: lane-matched, comp acceptable, at most 1–2 Partial items on required list
• 4–5 — stretch: some required items are Gaps; apply if quiet week, skip if busy
• 1–3 — skip: multiple Gaps on required, or off-lane, or comp below floor
• 0 — hard filter present; do not apply

Show your reasoning — two to four sentences, not just a number.

6. Write role.md

Write applications/<app_id>/role.md using the template below. Overwrite if it exists (but confirm with Mason if the existing file has Mason's hand edits — look for non-template sections).

7. Log that research/analysis happened (optional)

If you did substantial research beyond the JD (e.g. pulled in founder tweets, funding stage, etc.), consider logging a note_added event via the application-logger skill. For pure JD intake, no log event is needed — the role.md file itself is the artifact.

8. Report to Mason

Report concisely:

• The canonicalized URL (if different from what Mason gave you)
• The lane + fit score + one-line reasoning
• The top 1–3 gaps
• A single recommendation: apply / apply if slow week / skip — hard filter / skip — off-lane / skip — comp below floor

Keep it short. Mason should be able to decide in <30 seconds.

---

The role.md template

# <Company> — <Role Title>

**app_id:** <app_id>
**URL (canonical):** <company careers page URL>
**URL (source):** <original URL if different>
**Location / remote:** <from JD>
**Posted:** <date if known>
**Stated salary:** <band or "not disclosed">
**Source:** <hn | workatastartup | linkedin | referral | ...>

---

## Lane

**<lane>** — <one sentence why>

## Fit score: <n>/10

<2-4 sentences of reasoning — what matches, what doesn't, why the number>

## Recommendation

**<apply | apply if slow week | skip — hard filter | skip — off-lane | skip — comp below floor>**

<one sentence why>

---

## Required (from JD)

- [✅ | 🟡 | 🔴 | ⚠️] Requirement 1 — verdict note
- [...] Requirement 2 — verdict note
- ...

## Nice-to-have (from JD)

- [✅ | 🟡 | 🔴] Nice-to-have 1 — verdict note
- ...

## Truthfulness gaps (things to NOT put on the resume, DO put on the study guide)

- Gap 1 — what the JD wants, what Mason has that's closest, why it's still a gap
- Gap 2 — ...

## What to lead with on the resume / cover letter

- Project 1 and why it maps to this role
- Specific skill/tool from Tier A that this JD prioritizes
- The agent-directed angle only if this company signals it's agent-friendly

## Company notes (sparse — leave full research to company-research skill)

- Funding stage: <if obvious from JD>
- Team size: <if obvious>
- Hiring thesis signal: <is this company explicitly agent-friendly? AI-native? degree-filtered?>
- Anything surprising or worth flagging

---

## JD text (canonical, cleaned)

<full JD text, cleaned of nav/boilerplate, preserved verbatim so downstream skills can search it>

---

Rules and guardrails

• Never invent JD requirements. If the JD doesn't say "X years of Y," don't write it.
• Never soften a Gap into Partial. The whole point of this analysis is honesty. A gap on the resume is a lie; a gap on the study guide is a study topic.
• Never write to a directory that doesn't exist. Always verify applications/<app_id>/ exists first.
• Never read data/events.jsonl directly. Read data/applications.json instead (it's the derived view).
• Never link an aggregator URL when a company URL is available. Canonicalize.
• Fit scores are advisory, not binding. Mason decides what to apply to. The score helps him triage when there are many candidates.
• If the JD has a hard-filter degree requirement ("Bachelor's in CS required"): check profile/preferences.md — Mason has explicit guidance on how hard vs soft degree requirements are treated. A hard "required" is a skip; a soft "preferred" is fine to apply past.
• Comp check: compare stated salary to profile/comp.md. If stated salary is below the floor, verdict is skip — comp below floor even if the fit is otherwise good.

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Examples

Example 1 — agent-friendly YC startup

Mason: "https://www.ycombinator.com/companies/acme/jobs/xyz-founding-engineer"

1. Call application-logger first: creates app_id 2026-04-09-acme-founding-engineer, scaffolds folder.
2. Delegate to subagent: fetch URL, try to find canonical Ashby/Greenhouse link.
3. Read skills.md + preferences.md + projects/voices-that-remain.md (relevant because JD mentions AI pipelines).
4. Extract requirements. Compute verdicts. Lane = founding, fit = 9/10.
5. Write applications/2026-04-09-acme-founding-engineer/role.md.
6. Report: "Acme Founding Engineer — lane: founding, fit: 9/10. One partial gap (JD wants Kubernetes, you have container-free GCP deploys). Canonical URL on Ashby: ... Recommendation: apply. This company explicitly calls out 'engineers who direct coding agents' in the JD — lead with the VTR shipping velocity story."

Example 2 — hard filter, skip

Mason: pastes JD for a senior role at a big tech company that requires 8 years of experience and a PhD

1. Call application-logger if Mason wants it tracked (skipped applications are still worth tracking — you learn from them).
2. Extract requirements. Verdict: ⚠️ Hard filter (8 years, PhD).
3. Write role.md with recommended_action: skip — hard filter.
4. Report: "BigCo Senior ML Engineer — skip. Hard filter: 8+ years + PhD required. Logged as 'interested' so we can see what jobs we've ruled out, but no drafting skills should run."

Example 3 — comp below floor

Mason: "https://..." (small agency, quotes $55k)

1. Extract comp from JD. Below comp.md floor of $70k.
2. Write role.md, verdict skip — comp below floor.
3. Report: "Agency full-stack — skip. Stated salary $55k is below your $70k floor. Otherwise fit would be 6/10."

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What this skill does NOT do

• Does not write the resume (that's resume-tailor).
• Does not do deep company research beyond what's visible in the JD (that's company-research).
• Does not apply to the job or submit anything anywhere.
• Does not change the application's status to applied — that's application-logger when Mason actually applies.
• Does not write to data/ files directly.