Borrowing it
Nothing to install: this file belongs to VonTerraProject501c3/slushpile. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/VonTerraProject501c3/slushpile/main/GEMINI.mdgit 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/instructions/vonterraproject501c3/slushpile/gemini-md)<a href="https://agentmods.dev/instructions/vonterraproject501c3/slushpile/gemini-md"><img src="https://agentmods.dev/badge/instructions/vonterraproject501c3/slushpile/gemini-md/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/instructions/vonterraproject501c3/slushpile/gemini-md"><img src="https://agentmods.dev/badge/instructions/vonterraproject501c3/slushpile/gemini-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00613 | $0.00613 |
| Opus 5 | $0.00307 | $0.00307 |
| Sonnet 5 | $0.00123 | $0.00123 |
| Haiku 4.5 | $0.00061 | $0.00061 |
Grade A, and why
slushpile GEMINI.md 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
slushpile
An adversarial job search that gets better as it goes. 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, then write what that review found back into the profile every later role is built from.
The design rests on one idea: an application is not graded against the job description, it is graded against the other applications in the same queue. Every scoring step here is anchored to that queue rather than to the posting.
Setup, once per workspace
Run the onboarding skill in the directory where you keep your job search. It reads whatever materials you already have, interviews you for the rest, and writes four files:
profile.md— every factual claim the pipeline may make on your behalfpreferences.yaml— your constraints: compensation, relocation, targetingstories.md— the stories a cover letter gets built around- a voice agent — how you write. Generated separately by
https://github.com/aaddrick/written-voice-replication, then named in
preferences.yaml. A working example ships asaaddrick-voice; it is the plugin author's voice, not yours.
Nothing in this pipeline hardcodes a fact about you. Those four files are where every personal fact lives.
The skills
@./skills/onboard/SKILL.md @./skills/job-board-search/SKILL.md @./skills/outreach/SKILL.md @./skills/explore-experience/SKILL.md @./skills/application-builder/SKILL.md @./skills/adversarial-review/SKILL.md @./skills/removing-ai-tells/SKILL.md @./skills/redesign-templates/SKILL.md @./skills/status/SKILL.md @./skills/help/SKILL.md
The agents
The review pipeline dispatches seven personas. On a harness without subagent dispatch, adopt each definition in turn and run them sequentially, writing each report out before starting the next.
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 · 70 lines · 613 tokens per session scan A ffee0786123b
slushpile GEMINI.md is an instructions file published in the GitHub repository VonTerraProject501c3/slushpile (15 stars, last pushed 23d ago), licensed MIT. It adds 613 tokens to every session, about $0.0031 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.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.