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 sunlight-research-ai/sunlight-skills --skill sunlight-deepresearchgit clone --depth 1 https://github.com/sunlight-research-ai/sunlight-skillsWrote 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/sunlight-research-ai/sunlight-skills/sunlight-deepresearch)<a href="https://agentmods.dev/skills/sunlight-research-ai/sunlight-skills/sunlight-deepresearch"><img src="https://agentmods.dev/badge/skills/sunlight-research-ai/sunlight-skills/sunlight-deepresearch/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/sunlight-research-ai/sunlight-skills/sunlight-deepresearch"><img src="https://agentmods.dev/badge/skills/sunlight-research-ai/sunlight-skills/sunlight-deepresearch.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 | $0.00072 | $0.01966 |
| Opus 5 | $0.00036 | $0.00983 |
| Sonnet 5 | $0.00014 | $0.00393 |
| Haiku 4.5 | $0.00007 | $0.00197 |
Grade A, and why
sunlight-deepresearch 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 12d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sunlight Deep Research
Overview
Run deep research as a disciplined subagent orchestration workflow: decompose the question, send focused investigators, collect evidence, resolve conflicts, synthesize, and verify before final delivery.
When to Use
- The user asks for deep research on a broad, ambiguous, technical, market, product, academic, or strategic topic.
- The user wants a reusable research process rather than a one-pass answer.
- The user asks to compare options, investigate claims, map a domain, produce a sourced report, or find risks and unknowns.
- The user wants to run the workflow in Codex, Claude Code, OpenCode, or another agentic assistant.
- The user wants to implement the workflow in an application; LangGraph may be used, but is not required.
When NOT to Use
- Do not use for simple factual questions that can be answered directly with one reliable source.
- Do not use when the user explicitly wants a direct answer without a research process.
- Do not require LangGraph, a codebase, a database, or a hosted backend unless the user asks to build a durable app.
Contract
- Do not answer broad research questions from a single pass when subagents are available.
- Treat every
sunlight-deepresearchrun as thorough research; do not offer lighter modes. - Create file-backed research artifacts as the run progresses: brief, plan, source registry, per-source evidence files, subagent notes, evaluator audits, and final report.
- Break the objective into independent research tracks before dispatching work.
- Give each subagent a narrow brief, explicit output shape, and source/evidence requirements.
- Require each investigator to complete a full query sweep and coverage check before stopping.
- Register every useful fetched link in a source registry and create a per-source evidence file before using it in findings.
- Ask subagents to separate evidence, inference, uncertainty, and open questions.
- Synthesize only after reviewing the returned findings.
- Run citation, source-quality, coverage, and contradiction evaluators against the actual final report before final delivery.
- Resolve conflicts with targeted follow-up research instead of smoothing them over.
- Before dispatching investigators, run
python3 skills/sunlight-deepresearch/scripts/search-providers.py --checkfrom the user's project or run folder to detect configured Linkup, Exa, and Tavily keys. If the skill path differs, use the installed skill's script path. - Run a final verifier or critic pass before presenting the final report.
- Block final delivery when key findings or factual sentences lack linked sources.
- State limitations, confidence, and unresolved questions in the final output.
What ships with it
15 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 125 B
- references/compression-template.md 1.3 KB
- references/evidence-tracking-template.md 3.3 KB
- references/orchestration-pattern.md 12 KB
- references/persistence-and-runtime.md 3.1 KB
- references/report-template.md 2.4 KB
- references/research-brief-template.md 1.1 KB
- references/research-plan-template.md 1.3 KB
- references/search-providers.md 9.5 KB
- references/structured-data-template.md 805 B
- references/subagent-brief-template.md 3.3 KB
- references/wave-checkpoint-template.md 1.1 KB
- scripts/create-research-plan.py 6.3 KB runs code
- scripts/evaluate-source-coverage.py 6.6 KB runs code
- scripts/search-providers.py 11 KB runs code
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.
- 12d ago First seen · 116 lines · 72 tokens per session scan A 7532b800c63d
sunlight-deepresearch is a skill published in the GitHub repository sunlight-research-ai/sunlight-skills (5 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 1,966 once invoked, about $0.0004 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 2026-08-31.
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…