Slushpile
Seven agents try to reject you before a recruiter gets the chance.
What they find, you keep.
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Claude Code
claude plugin marketplace add aaddrick/slushpileclaude plugin install slushpile@slushpileThen, in the directory where you keep your job search:
/slushpile:onboard
Codex
codex plugin marketplace add aaddrick/slushpile --ref maincodex plugin add slushpile@slushpileCodex prefixes plugin skills with the plugin name:
$slushpile:onboard
Codex has no subagent dispatch, so the review pipeline runs its seven personas sequentially in one context instead of five of them in parallel. Same output, slower, and slightly more prone to one persona's reasoning bleeding into the next.
Cursor, Gemini CLI, and manual install
See INSTALL.md.
You are not being graded against the job description. You are being graded against the other seventy people who applied to the same requisition this week.
Almost every tool in this space gets that backwards. Feed a resume and a posting into a resume optimizer and it will tell you your keyword match went from 68% to 91%, which is a real number about the wrong question. If the 75th-percentile applicant in that queue matches at 94%, your 91% is a rejection, and nothing in the tool will ever tell you so.
The second thing they get wrong: they return one verdict. But the same resume and the same letter convert at maybe 2% through a cold portal submission and 30% through a referral. Those are not the same decision, and collapsing them into a single "strong match" is not a simplification. It is an error with a confident interface on top of it.
The third is the one nobody names. These tools have no memory. You paste a resume in, you get a number back, you close the tab, and the tool ends the session knowing exactly what it knew at the start. A search is forty applications over three months. Every one of them costs full price.
It builds a model of you, once. /slushpile:onboard interviews you and writes
three files: a profile, a preferences file, and a set of stories. The profile is
not a resume — it is the pool a resume gets cut from, several times longer than
anything you would ever send. Every later stage reads it, and nothing asks you
those questions twice.
It tries to talk you out of the role before you write anything. The search stage scores each posting against the estimated applicant pool, runs kill criteria, builds an expected-value matrix per application channel, and puts a contrarian in front of the tier list. Every other tool starts working after you have already decided to apply. The expensive mistake happens before that, and this is the only stage that can still catch it for free.
It attacks what it just wrote. A model asked whether its own draft is any good will tell you yes, at length. So the builder does not ask. It hands the resume and the letter to seven reviewers, five of whom run in parallel and cannot see each other's findings, each given only what its role would really have — the eleven-second screener is never shown the cover letter, because a screener who read the letter is not one. The builder fixes what comes back and sends it through again. Round two has to hold before it will let you submit, and it stops at three rounds, because past that the gaps are structural and more editing is motion.
It writes what it learns back into you. When a review says a section is thin, the interview that follows usually finds the experience was real and you never wrote it down. That goes into the profile permanently. The conversion estimates get corrected against the outcomes you record. Your twentieth application starts from a better place than your first, which in every other tool is simply not true.
What comes out is a verdict per channel — cold submission, warm referral, cold outreach, inbound from your public work — each with a probability range rather than a verdict word, and with materials quality scored separately from expected value. "1-3% interview" is information. "MAYBE" is not. Excellent materials sent to a wrong-fit role still have low expected value, and those two numbers routinely disagree.
Same resume, same posting, same afternoon.
Match score: 91% ✅
Great news — your resume is a strong match for this role!
✅ 14 of 16 required keywords found ✅ ATS-friendly formatting detected
⚠️ Consider adding: "stakeholder alignment", "OKRs"You're ready to apply!
Pool position: p55. Median applicant here has shipped at comparable scale at a company the hiring manager recognizes. Your open-source work is real and it is not rare in this pool — roughly a third of the p75 cohort has something equivalent.
Cold submission: REJECT, 1-3%. The years-of-experience dropdown on the form gates at 8. You have 6 in the titled function.
Warm referral: MAYBE, 20-30%. This is the only channel with a real path.
Materials quality: 8/10. The materials are not the problem.
Contrarian: SUBMIT_AS_PORTFOLIO_ONLY. Sending this cold spends an hour for a 2% shot. Two hours finding a referral is worth more than ten more cold applications.
One of these is a number about your document. The other is a decision about your afternoon.
Three commands, in order. A search runs on these alone.
/slushpile:onboard once per workspace — builds your profile,
preferences, and stories
/slushpile:job-board-search search a careers board, extract postings,
score pool-anchored fit, contrarian gate,
create role folders
/slushpile:application-builder build the resume and cover letter, then
iterate them against the review until they
stabilize
/slushpile:application-builder dispatches all three of these itself, in the
course of building an application. Run one directly only to work on materials
this pipeline did not build — a resume written elsewhere, a letter drafted by
hand.
/slushpile:explore-experience interview to surface experience you have
but never wrote down
/slushpile:adversarial-review seven agents, five in parallel, verdict
per channel
/slushpile:removing-ai-tells strip AI-authorship signals from prose,
with a gatekeeper on every change
/slushpile:redesign-templates restyle the resume and letter templates,
holding the ATS constraints fixed
/slushpile:status the queue, what is waiting on you, and whether
the pipeline's predictions are holding up
/slushpile:help what to run next, and how to read the output
| Agent | Simulates |
|---|---|
| Triage screener | 11 seconds, F-pattern, 347 resumes already read today |
| Requirements analyst | 30 seconds, methodical, checks every qualification against evidence |
| ATS simulator | A parser. Not a reader. Structure, keywords, and years-of-experience math |
| Fatigued reader | Application #61 of 80. What annoys, what gets skimmed, what closes the tab |
| Pool analyst | A recruiter who knows what the queue actually looks like |
| Hiring manager | The person who has to justify the interview slot to their skip-level |
| Contrarian | Whoever should have asked whether any of this was worth doing |
The first five run in parallel and cannot see each other's work. The hiring manager sees all five. The contrarian sees everything, including the hiring manager, and can overrule it.
The rest of it lives in docs/:
- Getting started: what to gather before onboarding, and what to install.
- Skills: every command, and when to run it.
- The workspace: the files this writes into your directory.
- Your voice agent, Troubleshooting.
- Architecture: the diagrams, why the review is shaped this way, and how scoring and calibration work.
An eighth agent writes the cover letter. It writes in one specific person's style, built from a corpus of their own writing.
slushpile ships aaddrick-voice as a working example so the pipeline runs
out of the box. It is the plugin author's voice, not yours. Letters written with
it will sound like a specific stranger — fine for seeing the pipeline work,
wrong for anything you actually send.
Generate your own with
written-voice-replication.
It analyzes a corpus of your writing across 25 dimensions and outputs a voice
agent, a voice skill, and a numeric profile with measurable targets.
aaddrick-voice is that pipeline's own worked example.
Then point preferences.yaml at it:
voice:
agent: "your-name-voice"
is_mine: trueWhile is_mine is false, every skill that drafts prose warns you before it
runs. That warning is the only thing standing between you and twelve
applications sent in a stranger's voice.
/slushpile:onboard writes three files into your directory: profile.md,
preferences.yaml, and stories.md. Every personal fact the pipeline uses
lives there. Nothing is hardcoded into the plugin, and the repository has a CI
gate that fails if a personal fact leaks into a skill.
That workspace will contain your full employment history, your compensation figures, and your constraints. Keep it in a private repository, or in no repository at all. The onboarding skill will say this to you and it will not initialize one for you.
The pipeline never submits anything. No skill touches an application portal, an email, or a form. It writes files. You read them and you send them.
This tool will tell you that a differentiator you are proud of is median. It will tell you a role you want has a 2% conversion rate. It will occasionally tell you not to apply.
That is the product. A pipeline that grades most applications INTERVIEW and converts 5% of them is not producing signal, it is producing optimism, and it will keep doing that indefinitely because nothing in it ever pushes back. The contrarian pass, the pool anchoring, and the per-channel probabilities all exist to make the output usable specifically when it says no.
There is a Calibration table in the workspace tracker for exactly this reason:
you record what the pipeline predicted and what actually happened, and the
priors get corrected by your own history rather than by anyone's confidence.
The nine skills and eight agents are Markdown. Fork, edit, install your copy:
claude plugin uninstall slushpileclaude plugin marketplace remove slushpileclaude plugin marketplace add <your-username>/slushpileclaude plugin install slushpile@slushpileIf you change a skill, run the gates before you push. See CONTRIBUTING.md.
MIT. See LICENSE.