Build a targeted resume and cover letter for a role folder that already has a job description and role analysis. Runs iterative adversarial review rounds until the materials stabilize, then hands off for human review before submission.
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Explain what slushpile is, what each skill does, what order to run them in, where the workspace files live, and how to set up a voice agent. Use when the user asks how slushpile works, what to run next, or how to fix a workspace problem.
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Search a named company's careers board, or resolve a natural-language query into a company list and search each. Extract job descriptions, score pool-anchored and channel-conditional fit against the user's profile, run a contrarian gate before tiers are finalized, and create role folders with assessments. Updates the job search tracker.
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Set up a slushpile workspace. Ingests an existing resume or LinkedIn export, interviews the user to fill the gaps, then writes profile.md, preferences.yaml, and stories.md. Run once per workspace, before any other slushpile skill.
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Open the warm channel for a role the pipeline already assessed. Finds who the user already knows at the company, researches named targets from public professional presence when they know nobody, grades each path honestly, drafts the ask through the voice agent, and writes the contacts into the Referrals table in job_search.md.
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Redesign the resume and cover letter templates into your own house style. Changes typography, palette, and layout while holding the ATS constraints fixed, then proves the result still compiles and still extracts. Run any time; not a pipeline stage.
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Identify and remove phrasing, structure, and word choices that signal AI authorship. Runs iterative passes through fresh voice-agent instances with the orchestrator acting as gatekeeper on every change. Use on a cover letter before submission, or on any prose that has to read as human-written.
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Read every application.yaml in the workspace and report the state of the search — the ranked queue, what is waiting on you, what has gone quiet, and the regression of the pipeline's own predictions against actual outcomes. Writes the calibration findings back into job_search.md and preferences.yaml.
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An adversarial job search that gets better as it goes. Set up a job-search workspace, search a careers board and score roles against the realistic applicant pool, build a resume and cover letter, review them with seven agents that return a verdict per submission channel, then write what the review finds back into the profile. Invoke with /slushpile and name what you want to do.
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Run a seven-agent adversarial pipeline against a resume and cover letter before submission. Produces channel-conditional verdicts anchored to the realistic applicant pool, separates materials quality from submission expected value, and ends with a contrarian pass that can overturn the verdict.
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Interview the user to surface experience that is real but undocumented, mapped against a specific role's requirements, then update the profile. Use when a fit assessment or an adversarial review flags a section as thin.
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