Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add VonTerraProject501c3/slushpile --skill helpgit clone --depth 1 https://github.com/VonTerraProject501c3/slushpileWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vonterraproject501c3/slushpile/help)<a href="https://agentmods.dev/skills/vonterraproject501c3/slushpile/help"><img src="https://agentmods.dev/badge/skills/vonterraproject501c3/slushpile/help/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vonterraproject501c3/slushpile/help"><img src="https://agentmods.dev/badge/skills/vonterraproject501c3/slushpile/help.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00060 | $0.02274 |
| Opus 5 | $0.00030 | $0.01137 |
| Sonnet 5 | $0.00012 | $0.00455 |
| Haiku 4.5 | $0.00006 | $0.00227 |
Grade A, and why
help scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Help
Answer the user's actual question from what follows. Do not print this whole file at them — pick the section that matches what they asked and answer from it in a few lines.
If they asked a general "what is this" or "what do I run", give them the Pipeline section and the single next command for their situation. Check the workspace state before recommending anything: if preferences.yaml is absent, the answer is always onboarding, whatever they asked.
What slushpile is
A job application pipeline. Ten skills and eight agents that take a role from a careers-board listing to a resume and cover letter that have already survived an adversarial review.
The idea it rests on: you are not graded against the job description, you are graded against the other applications in the same queue. Every scoring step is anchored to that queue rather than to the posting. This is why the output sometimes says a differentiator the user is proud of is median, and why it reports probabilities per submission channel instead of one verdict.
It never submits anything. Every skill writes files. The user reads them and sends them.
The pipeline
| Order | Command | What it does |
|---|---|---|
| Once | /slushpile:onboard |
Builds the workspace: profile.md, preferences.yaml, stories.md |
| Per company | /slushpile:job-board-search <company|query> |
Searches, extracts postings, scores pool-anchored fit, runs the contrarian gate, creates role folders. Takes a company name, or a query describing the work and where — a query is resolved into a company list and confirmed before anything is searched |
| As needed | /slushpile:explore-experience <role folder> |
Interviews to surface experience the user has but never wrote down |
| Per role | /slushpile:outreach <role folder> |
Finds who they already know at the company, grades the path honestly, drafts the referral ask or the cold note, and writes the contacts into the Referrals table. Run it wherever the warm channel is the highest-EV one and no referrer exists yet |
| Per role | /slushpile:application-builder <role folder> |
Builds resume and cover letter, iterates against review until stable |
| Per role | /slushpile:adversarial-review <role folder> |
Seven agents, five in parallel, verdict per channel |
| As needed | /slushpile:removing-ai-tells <file> |
Strips AI-authorship signals from prose, with gatekeeper review |
| Any time | /slushpile:redesign-templates |
Restyles the resume and letter templates, holding the ATS constraints fixed |
| Any time | /slushpile:status |
The queue, what is waiting on them, and whether the pipeline's predictions are holding up |
| Any time | /slushpile:help |
This |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 136 lines · 60 tokens per session scan A 8d93a0116ceb
help is a skill published in the GitHub repository VonTerraProject501c3/slushpile (15 stars, last pushed 23d ago), licensed MIT. It adds 60 tokens to every session and 2,274 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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