Ray Trend Search
imraywang/rayskills
Researches what people are saying about a topic over a recent window across X, Reddit, YouTube and the public web, reporting each source's status with links.
Full execution protocol for MODE: DEEPRESEARCH — orchestrator-worker deep research over external sources: decompose, iterative websearch/webfetch retrieval, parallel sme synthesis, dual-reviewer…
$ npx skills add ZaxbyHub/opencode-swarm --skill deep-research -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ZaxbyHub/opencode-swarm deep-research --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/ZaxbyHub/opencode-swarm.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/deep-research .claude/skills/deep-research && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "deep-research" agent skill from https://github.com/ZaxbyHub/opencode-swarm/tree/main/.claude/skills/deep-research into .claude/skills/deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ZaxbyHub/opencode-swarm/tree/main/.claude/skills/deep-researchType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ZaxbyHub/opencode-swarm --skill deep-research -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ZaxbyHub/opencode-swarm deep-research --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZaxbyHub/opencode-swarm.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/deep-research .agents/skills/deep-research && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deep-research" agent skill from https://github.com/ZaxbyHub/opencode-swarm/tree/main/.claude/skills/deep-research into .agents/skills/deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ZaxbyHub/opencode-swarm --skill deep-research -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ZaxbyHub/opencode-swarm deep-research --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZaxbyHub/opencode-swarm.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/deep-research .cursor/skills/deep-research && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "deep-research" agent skill from https://github.com/ZaxbyHub/opencode-swarm/tree/main/.claude/skills/deep-research into .cursor/skills/deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ZaxbyHub/opencode-swarm.git --path .claude/skills/deep-research--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ZaxbyHub/opencode-swarm --skill deep-research -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ZaxbyHub/opencode-swarm deep-research --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZaxbyHub/opencode-swarm.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/deep-research .gemini/skills/deep-research && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "deep-research" agent skill from https://github.com/ZaxbyHub/opencode-swarm/tree/main/.claude/skills/deep-research into .gemini/skills/deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ZaxbyHub/opencode-swarm deep-researchInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ZaxbyHub/opencode-swarm --skill deep-research -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ZaxbyHub/opencode-swarm.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/deep-research .github/skills/deep-research && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "deep-research" agent skill from https://github.com/ZaxbyHub/opencode-swarm/tree/main/.claude/skills/deep-research into .github/skills/deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ZaxbyHub/opencode-swarm --skill deep-research -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ZaxbyHub/opencode-swarm deep-research --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ZaxbyHub/opencode-swarm.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/deep-research .opencode/skills/deep-research && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "deep-research" agent skill from https://github.com/ZaxbyHub/opencode-swarm/tree/main/.claude/skills/deep-research into .opencode/skills/deep-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deep-research", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
deep-researchFull execution protocol for MODE: DEEPRESEARCH — orchestrator-worker deep research over external sources: decompose, iterative websearch/webfetch retrieval, parallel sme synthesis, dual-reviewer…
Deep Research is an agent skill from ZaxbyHub/opencode-swarm. Full execution protocol for MODE: DEEPRESEARCH — orchestrator-worker deep research over external sources: decompose, iterative websearch/webfetch retrieval, parallel sme synthesis, dual-reviewer claim verification, critic challenge of high-stakes claims, and a cited report. Loaded on demand by the architect when the deep-research command emits a [MODE: DEEPRESEARCH ...] signal.
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Deep research, Web search and Fact-checking and source verification. The repository describes itself as: Architect-centric agentic swarm plugin for OpenCode. Hub-and-spoke orchestration with SME consultation, code generation, and QA review. The licence is MIT.
8 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit b63a4bd. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
TAVILY_API_KEYBRAVE_SEARCH_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Deep Research loads about 2.7k tokens when it runs. Until then it costs about 100 tokens; SKILL.md has 1,420 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from ZaxbyHub/opencode-swarm at commit b63a4bd, republished under its MIT licence (© ZaxbyHub). 1,420 words, ~2,685 tokens.
.claude/skills/deep-research/SKILL.md (or your agent's skills folder).Read-only, multi-source, fact-checked research that produces a cited report. The
architect is the orchestrator: it owns retrieval (web_search + web_fetch),
decomposes the question, runs an iterative gather→assess→re-plan loop, dispatches
parallel sme workers for synthesis, verifies claims against sources with 2
reviewers, challenges high-stakes claims with the critic, and writes the final
answer. This mode does NOT mutate source code, does NOT delegate to coder, and
does NOT call declare_scope.
Parse the [MODE: DEEP_RESEARCH ...] header to extract:
depth: standard | exhaustive (default: standard)max_researchers: integer 1..6 — parallel synthesis workers per round (default: 3, or 5 for exhaustive)rounds: integer 1..4 — maximum iterative research rounds (default: 2, or 3 for exhaustive)output: report | brief (default: report)questionIf the header is malformed or the question is empty, report the error and stop.
Read council.general from the resolved opencode-swarm config (global
~/.config/opencode/opencode-swarm.json first, then project
.opencode/opencode-swarm.json override). If council.general.enabled is not
true OR no search API key is configured (neither council.general.searchApiKey
nor TAVILY_API_KEY / BRAVE_SEARCH_API_KEY), surface to the user:
"Deep research needs external search. Set council.general.enabled: true and configure a search API key (Tavily or Brave) in global ~/.config/opencode/opencode-swarm.json or project .opencode/opencode-swarm.json."
Then STOP. Do NOT produce ungrounded research from training memory.
(web_search requires the key; web_fetch only requires the enabled flag and is
architect-only. The sme workers do NOT have web_fetch and must not be expected to
fetch sources. An sme may have web_search, but in this mode it synthesizes only
from the evidence you gather — do NOT rely on sme-side searching; pass it the
RESEARCH CONTEXT.)
Break the question into 2..max_researchers focused subtopics that together cover
it without overlap. State the subtopics and a one-line scope for each. Record the
CURRENT DATE in ISO YYYY-MM-DD form for time-sensitive grounding.
Repeat for up to rounds rounds. Maintain a running EVIDENCE LEDGER keyed by
subtopic.
For each round:
web_search
queries (specific, keyword-focused; default freshness: "auto"; never append a
training-cutoff year). Preserve each result's normalized query,
temporalIntent, freshness, and removedStaleYears metadata.web_fetch on the URL to
read the primary source text (snippets are not enough for a load-bearing
claim). Prefer fetching 1–4 sources per subtopic per round. Each web_search
result carries a per-result evidenceRef; each web_fetch result carries
evidence.ref. Record these — every reported claim must trace to one.Grounding rules:
web_search or web_fetch returns an error or no results for a
time-sensitive subtopic, note it and try an alternate query/source; do not
fabricate. If a subtopic cannot be grounded at all, mark it UNVERIFIED in the
report rather than inventing an answer.RESEARCH CONTEXT — <subtopic>
================
[E1] <title> — <url> (ref: <evidenceRef>)
<key extracted facts / quoted snippet>
[E2] ...Dispatch up to max_researchers the active swarm's sme agent calls with
dispatch_lanes_async when available — one per subtopic. Before the first
dispatch, verify from the session's actual tool list whether the controller's
lane tools are present; when they are absent, use the native parallel subagent
path from the start rather than discovering the gap on first failure. Record the returned
batch_id, then continue architect-owned retrieval quality work that does not
depend on worker output: tighten the evidence ledger, check source authority,
prepare reviewer shard structure, and identify unresolved gaps. Do not write final
claims from running lanes. Dispatch promptly — do not accumulate extensive planning
prose before the call, or output truncation may swallow the tool call itself. Keep each
lane prompt compact: send shared context ONCE via the common_prompt field, or have
lanes read it from a file by absolute path, instead of inlining the same large blob into
every lane prompt — oversized inline prompts produce malformed or truncated tool-call
JSON. Each sme dispatch must
include:
DOMAIN: the subtopicTASK: "Synthesize an evidence-grounded answer for this subtopic. Cite each
claim by its evidence ref (E1, E2, …). Do NOT introduce facts that are not in
the provided RESEARCH CONTEXT. Flag any contradictions between sources and any
claim you cannot support."INPUT: the full RESEARCH CONTEXT block for that subtopic + the CURRENT DATEOUTPUT: claims with evidence refs, contradictions noted, confidence (0–1)SKILLS: noneThe sme synthesizes only from the provided evidence — it does not fetch. While
synthesis lanes run, poll with collect_lane_results without wait (or
wait: false) to process completed worker responses as they settle while
continuing independent architect work between polls. Before Step 5, call
collect_lane_results with wait: true for every open synthesis batch only if
lanes are still pending and no independent work remains. Do not advance to Step 5
until every synthesis lane is settled. Collect all completed worker responses into
a candidate findings set, each finding tagged with its subtopic, evidence refs,
and the worker's confidence. Treat missing, stale, cancelled, or failed lanes as
explicit coverage gaps. If dispatch_lanes_async is unavailable, use
blocking dispatch_lanes as the first fallback and record that async advisory lanes were
unavailable. This changes only when the architect waits, not whether every
synthesis lane must settle before Step 5. Do not substitute Task-tool dispatch
unless lane tools are unavailable; when they are unavailable, Task is the final fallback
and must be verified as equivalent by agent type, prompt, scope, and
isolation.
When a lane result includes output_ref, treat output as a preview and call
retrieve_lane_output before extracting claims, summarizing a subtopic, or marking
the subtopic clean. If the result is output_degraded, transcript_incomplete, or
truncated without a usable ref, mark the affected subtopic UNVERIFIED or
re-dispatch a narrower lane; do not treat preview absence as evidence absence.
Split the candidate findings into 2 shards. Dispatch 2 parallel
the active swarm's reviewer agent calls. Each reviewer receives its shard plus
the relevant RESEARCH CONTEXT and the instruction:
"For each claim, verify it is actually supported by its cited evidence ref. Verdict per claim: SUPPORTED / UNSUPPORTED / OVERSTATED / CONTRADICTED. A claim with no evidence ref, or whose cited source does not actually say it, is UNSUPPORTED. Do not add new claims or new research."
Drop or downgrade any claim that is not SUPPORTED. Merge duplicate claims that both reviewers verified.
For claims that are decision-critical, surprising, or where sources conflict,
dispatch the active swarm's critic agent:
"Challenge each claim: is the evidence strong enough for the weight it carries? Are contradicting sources fairly represented? Verdict: SURVIVES / DOWNGRADE / REJECT with reasoning."
Do NOT challenge well-supported, low-stakes claims. Final confidence on a claim is the critic's assessment where it ran, else the reviewer's.
Present the report directly to the user. This mode writes no user-visible files —
evidence is written under .swarm/evidence-cache/ by the tools, and the report
itself is the chat answer (matching MODE: DEEP_DIVE). Apply these rules:
[title](url) from the gathered evidence. Pick
the strongest source per claim; do not cite duplicates.output=brief: a few tight paragraphs + a bulleted key-findings list. For
output=report: full per-subtopic sections, a "Confidence & limitations" note,
and a "Sources" list..swarm/ (evidence is
written under .swarm/evidence-cache/ by the tools automatically).web_search, web_fetch); sme workers
synthesize only from the evidence you provide and must not run their own searches or
fetch sources here, even if web_search is available to them.© ZaxbyHub, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in .claude/skills/deep-research of ZaxbyHub/opencode-swarm.
Open the folder on GitHubat commit b63a4bd
Deep Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Deep Research this skillZaxbyHub/opencode-swarm | 494 | — | ~2.7k | Automated safety check: Pass | MIT | |
| Ray Trend Searchimraywang/rayskills | 159 | — | ~2.1k | Automated safety check: Pass | Custom licence | |
| Argo Search and Verificationtaxueseek/argo | 188 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Deep Research Loopmadebyaris/advance-minimax-m3-cursor-rules | 126 | — | ~2.9k | Automated safety check: Pass | MIT | |
| Multi Source Searchsandbaseai/sandbase-skills | 203 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Research LookupK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.6k | Automated safety check: Pass | MIT |
imraywang/rayskills
Researches what people are saying about a topic over a recent window across X, Reddit, YouTube and the public web, reporting each source's status with links.
taxueseek/argo
Unified web search, page fetching and evidence checking across hundreds of sources, with result verification, a research-dossier mode and vertical search engines.
madebyaris/advance-minimax-m3-cursor-rules
Runs multi-step research with a loop of search, compress, reflect and synthesize, scaling effort from a quick sourced answer to an exhaustive cited report.
sandbaseai/sandbase-skills
Portable multi-source research with cross-source validation and an offline evidence ledger.
K-Dense-AI/claude-scientific-writer
Compile current scholarly evidence for a scientific manuscript or research brief.
asgeirtj/system_prompts_leaks
Deep research harness — fan-out web searches, fetch sources, adversarially verify claims, synthesize a cited report.
ZaxbyHub/opencode-swarm
Runs an evidence-gated, quote-grounded audit of a codebase for security, QA, accessibility, performance and more, and writes a verified report without changing source files.
ZaxbyHub/opencode-swarm
Drives a bug report from validation and root-cause tracing through a critic-reviewed plan, an approved minimal fix and a PR-ready closure, never merging without recorded human approval.
ZaxbyHub/opencode-swarm
Codex adapter for opencode-swarm that governs commits, pushes, draft PRs, PR body updates and CI closeout, deferring to the repo's canonical commit-pr protocol.
ZaxbyHub/opencode-swarm
Keeps plans, decisions, evidence and reviewer verdicts in small files so long multi-phase tasks survive context compaction and session resumes.
ZaxbyHub/opencode-swarm
Ingests existing pull request feedback such as review comments and CI failures, verifies each claim, fixes confirmed issues and reports closure status for every item.
ZaxbyHub/opencode-swarm
Monitor a pull request after creation and act autonomously on pushed PR activity.
Categories
Full execution protocol for MODE: DEEPRESEARCH — orchestrator-worker deep research over external sources: decompose, iterative websearch/webfetch retrieval, parallel sme synthesis, dual-reviewer…. Deep Research is an agent skill from ZaxbyHub/opencode-swarm. Full execution protocol for MODE: DEEPRESEARCH — orchestrator-worker deep research over external sources: decompose, iterative websearch/webfetch retrieval, parallel sme synthesis, dual-reviewer claim verification, critic challenge of high-stakes claims, and a cited report.
Deep Research fits situations like: tasks that involve Deep research; tasks that involve Web search; tasks that involve Fact-checking and source verification.
Run `npx skills add ZaxbyHub/opencode-swarm --skill deep-research -a claude-code`. Or copy the skill folder (.claude/skills/deep-research in ZaxbyHub/opencode-swarm) into .claude/skills/deep-research in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ZaxbyHub/opencode-swarm --skill deep-research -a codex`. Or copy the skill folder (.claude/skills/deep-research in ZaxbyHub/opencode-swarm) into .agents/skills/deep-research in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ZaxbyHub/opencode-swarm --skill deep-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-research, .gemini/skills/deep-research, .github/skills/deep-research and .opencode/skills/deep-research in your project.
Going by SKILL.md and its folder, Deep Research needs credentials named TAVILY_API_KEY and BRAVE_SEARCH_API_KEY. Our summary lists: A credential in TAVILY_API_KEY; A credential in BRAVE_SEARCH_API_KEY.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Deep Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Deep Research: Ray Trend Search (imraywang/rayskills, 159 stars), Argo Search and Verification (taxueseek/argo, 188 stars), Deep Research Loop (madebyaris/advance-minimax-m3-cursor-rules, 126 stars) and Multi Source Search (sandbaseai/sandbase-skills, 203 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ZaxbyHub (a GitHub organization) maintains it in ZaxbyHub/opencode-swarm, which has 494 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on October 10, 2026.
Source: ZaxbyHub/opencode-swarm on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.