Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AutoConference/AutoConference-skillnpx agentmods add skills/autoconference/autoconference-skill/idea-discoveryWrote 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/autoconference/autoconference-skill/idea-discovery)<a href="https://agentmods.dev/skills/autoconference/autoconference-skill/idea-discovery"><img src="https://agentmods.dev/badge/skills/autoconference/autoconference-skill/idea-discovery/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/autoconference/autoconference-skill/idea-discovery"><img src="https://agentmods.dev/badge/skills/autoconference/autoconference-skill/idea-discovery.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 | $0.00056 | $0.06559 |
| Opus 5.5 | $0.00022 | $0.02624 |
| Sonnet 5 | $0.00011 | $0.01312 |
| Haiku 4.5 | $0.00006 | $0.00656 |
Grade A, and why
idea-discovery 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 3d 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.
This is a copy
92% identical to idea-discovery — 24 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 522 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow 1: Idea Discovery Pipeline
Orchestrate a complete idea discovery workflow for: $ARGUMENTS
Overview
This skill chains sub-skills into a single automated pipeline:
/research-lit → /idea-creator → /novelty-check → /research-review → /research-refine-pipeline
(survey) (brainstorm) (verify novel) (critical feedback) (refine method + plan experiments)
Each phase builds on the previous one's output. The final deliverables are a validated idea-stage/IDEA_REPORT.md with ranked ideas, plus a refined proposal (refine-logs/FINAL_PROPOSAL.md) and experiment plan (refine-logs/EXPERIMENT_PLAN.md) for the top idea.
Constants
- PILOT_MAX_HOURS = 2 — Skip any pilot experiment estimated to take > 2 hours per GPU. Flag as "needs manual pilot" in the report.
- PILOT_TIMEOUT_HOURS = 3 — Hard timeout: kill any running pilot that exceeds 3 hours. Collect partial results if available.
- MAX_PILOT_IDEAS = 3 — Run pilots for at most 3 top ideas in parallel. Additional ideas are validated on paper only.
- MAX_TOTAL_GPU_HOURS = 8 — Total GPU budget across all pilots. If exceeded, skip remaining pilots and note in report.
- AUTO_PROCEED = true — When
true, checkpoints are informational: report the selected option and continue in the same turn. Set tofalseto ask for explicit user confirmation and end the turn at each selection checkpoint. - REVIEWER_MODEL =
gpt-5.6-sol— Model used via Codex MCP. Must be an OpenAI model (e.g.,gpt-5.6-sol,o3,gpt-4o). Passed to sub-skills. - OUTPUT_DIR =
idea-stage/— All idea-stage outputs go here. Create the directory if it doesn't exist. - ARXIV_DOWNLOAD = false — When
true,/research-litdownloads the top relevant arXiv PDFs during Phase 1. Whenfalse(default), only fetches metadata. Passed through to/research-lit. - COMPACT = false — When
true, generate compact summary files for short-context models and session recovery. Writesidea-stage/IDEA_CANDIDATES.md(top 3-5 ideas only) at the end of this workflow. Downstream skills read this instead of the fullidea-stage/IDEA_REPORT.md. - RENDER_HTML = true — When
true(default), auto-renderidea-stage/IDEA_REPORT.mdto HTML at workflow end via/render-html. Uses--no-review(the source MD already went through novelty + cross-model review during Phase 3). Setfalseto skip, or pass— render html: false. - REF_PAPER = false — Reference paper to base ideas on. Accepts: local PDF path, arXiv URL, or any paper URL. When set, the paper is summarized first (
idea-stage/REF_PAPER_SUMMARY.md), then idea generation uses it as context. Combine withbase repofor "improve this paper with this codebase" workflows. - RESUMABLE = true — Record stage evidence under
.aris/runs/<run_id>.jsonand require a deterministic evidence gate before declaring the final report complete.
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.
- 3d ago First seen · 522 lines · 56 tokens per session scan A e45bac9c0d10
idea-discovery is a skill published in the GitHub repository AutoConference/AutoConference-skill (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 56 tokens to every session and 6,559 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to idea-discovery, differing in 24 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…