Creator · JetBrains
Last updated · Aug 24, 2026
brainstorming
Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip th
Sandbox only
Install targets
Codex install prompt
Install the "brainstorming" agent skill from https://github.com/JetBrains/thinkrail/tree/main/packages/pi-thinkrail-workflow/skills/brainstorming. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip this because a change looks small. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jetbrains-brainstorming","task":"Install brainstorming","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add JetBrains/thinkrail --skill brainstorming
Maintenance
fresh
Pushed today
Risk
Needs review
Financial research output is not financial advice; require human review before any live investment decision
GitHub quality
38
62/100 Quality · 77/100 Trust
Coverage tags
Review notes
Financial research output is not financial advice; require human review before any live investment decision · Low GitHub adoption signal
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Sandbox onlyUseful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Human review before install
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
38 GitHub stars
Repo activity
38 stars, 8 forks
Maintenance
Pushed today
License
Apache-2.0
Install
npx skills add JetBrains/thinkrail --skill brainstorming
Install safety
standard package or runtime install path
Permission surface
filesystem or document access
Agent outcomes
No agent outcome data yet
Docs
Usable metadata, review docs
Risk summary
Review before production
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 38 GitHub stars
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is declared
- No Agent Proven outcome evidence yet
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
View technical data+
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Search sources
Suited agents
Install decision
- Command
- npx skills add JetBrains/thinkrail --skill brainstorming
- Policy
- review
- Human review
- yes
Trust and risk
- Trust
- 69/100
- Audit
- 79/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add JetBrains/thinkrail --skill brainstormingDo not use when
- teams that need a vendor-supported SLA
- production agents without a repository review
- Low GitHub adoption signal
- No OpenAgentSkill engagement data yet
- Financial research output is not financial advice; require human review before any live investment decision
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Agent safety v2
63/100 · Review before install
Usable candidate, but the agent should surface permission and audit notes before installation.
Require human approval before installing into a real workspace.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
- Financial research output is not financial advice; require human review before any live investment decision
Agent resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/api/agent/resolve?task=Use%20brainstorming%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20brainstorming%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/jetbrains-brainstorming/install
Agent should check
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copy prompt
Task: Use brainstorming in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20brainstorming%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jetbrains-brainstorming/install
Install command: npx skills add JetBrains/thinkrail --skill brainstorming
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/jetbrains-brainstorming/install
LLM text format
/api/skills/jetbrains-brainstorming/install?format=text
Find alternatives
/api/skills/search?q=brainstorming&limit=3
Agent prompt
Use brainstorming for this task. Review https://www.openagentskill.com/api/skills/jetbrains-brainstorming/install, then install with: npx skills add JetBrains/thinkrail --skill brainstormingRegistry metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/jetbrains-brainstorming
LLM text
/api/registry/manifest/jetbrains-brainstorming?format=text
Install alias
/api/registry/install/jetbrains-brainstorming
Recommend
/api/registry/recommend?task=Use%20brainstorming%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code
Audit report
Needs review · 79/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 62/100 quality profile
review first
- Low GitHub adoption signal
- No OpenAgentSkill engagement data yet
Implementation path
- 1Install it in a sandbox agent and run one Research agents task end to end.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Trust profile
Sandbox only
Useful candidate with missing or mixed trust signals. Keep it in an isolated workspace until the outcome loop proves task fit.
GitHub adoption
CHECK38 GitHub stars
Stars/forks activity
CHECK38 stars, 8 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
PASSApache-2.0
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Install command has no obvious high-risk pattern
- Outcome loop is ready but needs first real agent run
Review before install
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 38 GitHub stars
- Stars/forks activity: 38 stars, 8 forks; issue activity unavailable in current metadata
- No real agent outcome reports yet
- Human review required before unattended installation
Recommended action
Run only in a sandbox and compare close alternatives before using it for real work.
Quality profile
Promising candidate for agent workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Use this skill in these scenarios
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
Workflow automation
I need my agent to automate a repeated workflow across tools and files.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Workflow fit
Add it to a complete workflow
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Alternative shortlist
Compare before you install
Similar skills that may fit this task.
Last30days Skill
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GPT Researcher
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DeepResearch
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Overview
--- name: brainstorming description: "Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip this because a change looks small." ---
# Brainstorming
## Brainstorm before you build
- Before starting any creative or feature work — a new feature, added functionality, a behavioral change, a nontrivial design decision — stop and run this workflow before writing implementation code. - The aim: turn the request into a validated design, recorded as a spec-graph `task-spec`, that the user has explicitly approved — not a guess you implement and hope lands. - Never implement during brainstorming. If you catch yourself opening a source file to make a change before the design is approved, stop.
## Anti-pattern: "this is too small to need this"
Every request goes through this, however small it looks. A one-line config change and a new subsystem both benefit from a few minutes of "what does the user actually want and why" — that is where wrong assumptions get caught cheaply. Scale the *depth* to the task; never skip the workflow entirely.
## The workflow
1. **Orient.** Use the spec-graph skill's tools first — `spec_grep`/`spec_get`/`spec_graph` — to find what the project already says about the area; read code second, to confirm details. 2. **Scope check.** If the request bundles multiple independent features or subsystems, say so and brainstorm them one at a time (or in parallel sub-sessions, the user's call) — don't blend unrelated decisions into one task-spec. 3. **Open a task-spec.** As soon as you understand roughly what's being asked, `spec_create` a `task-spec` at **`.thinkrail/context/TASK-<slug>.md`** (id, title, status: draft, parent: the nearest relevant module) to hold the design as it develops. `.thinkrail/context/` is the workspace's gitignored scratch dir (host-seeded, zero git footprint) yet stays scannable by the spec tools — the home for every temp doc, never committed. This file is the one artifact — update it live as decisions land; don't also keep a separate scratch doc. This works even in a project with no existing spec graph: a `task-spec` only needs frontmatter `id` and `type` to be a valid spec, no pre-existing graph required — don't skip this step just because nothing else in the project is specced yet. 4. **Clarify.** Ask what you need via `ask_user_question`, composing rounds per the **asking-user-questions** concept skill — read it before the first round. Resolve a full round, update the task-spec with what you learned, and only open a new round if the answers raised a genuinely new question. Per that concept's degradation norms, skipped questions or a host with no UI are not blockers: record your best-guess assumptions in the task-spec, explicitly marked unconfirmed, and continue. 5. **Propose approaches.** Once the ask is clear, write 2-3 approaches into the task-spec with trade-offs and a recommendation. When approaches are easiest to compare side by side, ask via a single-select `ask_user_question` with each approach as an option (label = approach name, description = its trade-off) instead of prose alone. 6. **Present the design.** Write it into the task-spec in sections scaled to their complexity; confirm with the user as each section lands, not only at the end. 7. **Self-review.** Before asking for final sign-off, reread the task-spec for: placeholders/TBDs, sections that contradict each other, scope that's actually multiple task-specs, and ambiguous requirements — fix what you find, don't just flag it. 8. **Promote.** When the design settles a boundary, contract, or decision that belongs in a durable spec, fold it into the relevant module's `SPEC.md` now — `spec_create` for a new module, `spec_update` for its frontmatter (draft → active as it firms up), `edit` for prose. Run `spec_validate` after structural changes. 9. **Final review, then build.** Ask the user to review the (now-promoted) design once more. Once approved, implement directly against it — there is no separate plan-writing step here. Before handing off, self-review the implementation diff the way step 7 reviewed the spec: no silent lint/type suppressions (a gate error is a design signal — question the flagged state or dependency before guarding it; any genuinely-needed suppression gets explicit user sign-off first), no nontrivial derivation duplicated across files (centralize it), no rationale left as code comments (near-zero comments: decisions and invariants go to the owning spec per the writing-specs bar; only lint directives and rare one-line hazard notes survive), and when the change replaced a pattern, sweep the repo for remnants of the old one. Keep the task-spec and the durable specs honest as the code lands, and retire the task-spec once **the work itself** is done, not merely once the design was promoted.
## What a good task-spec looks like
- Scoped to one piece of work — if it's accreting unrelated decisions, split it. - States the request, the decision(s) made and why, the approaches considered and why they were or weren't picked, and anything the user explicitly deferred or declined to answer. - Gets promoted, not copied: once a decision belongs in a module's `SPEC.md`, move it there and reference it from the task-spec rather than keeping two copies that can drift.
Technical details
- Version
- 1.0.0
- License
- Apache-2.0
- Last updated
- Aug 24, 2026
- Published
- Aug 24, 2026
Decision snapshot
Fallback candidate
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 86/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- Success rate
- —
- Recent failure
- —
- Outcomes
- 0
- Output quality
- —
- Failed
- 0
- Not relevant
- 0
- Installs
- 0
- Risk blocked
- 0
- Setup needed
- 0
- Production
- 0
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Add to agent workflow
Free and open source. Review the report before installing into production agents.
Growth loop
Share kit
Scenario-led draft for brainstorming, ready for a manual X post.
A practical pick for design or creative work: brainstorming: Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a... 38 stars https://www.openagentskill.com/skills/jetbrains-brainstorming?ref=x
Optional reply with install command
Listing + install path for brainstorming: https://www.openagentskill.com/skills/jetbrains-brainstorming?ref=x Install: npx skills add JetBrains/thinkrail --skill brainstorming
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- JetBrains
- Source
- JetBrains/thinkrail
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This Registry indexed listing is attributed to JetBrains but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](https://www.openagentskill.com/skills/jetbrains-brainstorming)
[](https://www.openagentskill.com/skills/jetbrains-brainstorming)
[](https://www.openagentskill.com/skills/jetbrains-brainstorming/audit)
[](https://www.openagentskill.com/skills/jetbrains-brainstorming)Author
JetBrains
@jetbrains
Tags
Platform fit
Health signals
- GitHub stars
- 38
- Quality score
- 34/100
- Last GitHub push
- Aug 24, 2026
- Framework hints
- Unknown
- OpenAgentSkill views
- 0
- Install copies
- 0
- Outbound clicks
- 0
Community signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & safety
Sandbox only
- GitHub adoption38 GitHub starsCHECK
- Stars/forks activity38 stars, 8 forks; issue activity unavailable in current metadataCHECK
- Recent maintenancePushed todayPASS
- License clarityApache-2.0PASS
- README/SKILL.md completenessPublic metadata needs stronger README/SKILL.md contextINFO
- Dependency/runtime riskno major dependency risk hints in public metadataPASS
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