TrajectoryRL
Bittensor Subnet 11 — an open skill factory that uses distributed compute and RL to produce state-of-the-art skills for AI agents.
Supply asset profile
Coding and developer agents
Code review, repo analysis, testing, CI, GitHub, DevOps, and developer workflow skills.
Scenario
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Agent fit
Claude Code + OpenAI Agents + Cursor
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add trajectoryRL/trajectoryRL
Maintenance
fresh
Pushed today
Risk
Needs review
Dependency or permission surface needs review
GitHub quality
20
69/100 quality · 73/100 trust
Coverage tags
Review notes
Dependency or permission surface needs review · 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 reviewInstall readiness, security metadata, maintenance, and adoption risk.
Trust Score v5
Human review before install
Run only in a sandbox and compare close alternatives before using it for real work.
Stars
20 GitHub stars
Repo activity
20 stars, 17 forks
Maintenance
Pushed today
License
MIT
Install
npx skills add trajectoryRL/trajectoryRL
Install safety
standard package or runtime install path
Permission surface
shell or command execution, network or browser access
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Review before production
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 20 GitHub stars
- Stars/forks activity: 20 stars, 17 forks; issue activity unavailable in current metadata
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.
Suited tasks
- Coding agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Inspect source files
Suited agents
Install decision
- Command
- npx skills add trajectoryRL/trajectoryRL
- Policy
- review
- Human review
- yes
Trust and risk
- Trust
- 65/100
- Audit
- 79/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add trajectoryRL/trajectoryRLDo 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
- High-risk permission hints: Shell or command execution
Agent safety v2
55/100 · Review before install
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
high
Shell or command execution
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
- High-risk permission hints: Shell or command execution
- Dependency or permission surface needs review
Install targets
Install this skill in your agent workflow
Copy the registry command or an agent-specific install prompt for Codex, Claude Code, and Cursor.
OpenAgentSkill CLI
Use the registry command when your workflow supports the OpenAgentSkill installer.
$ npx skills add trajectoryRL/trajectoryRLAgent 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.
Resolve JSON
/api/agent/resolve?task=Use%20TrajectoryRL%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve text
/api/agent/resolve?task=Use%20TrajectoryRL%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Install handoff
/api/skills/trajectoryrl-trajectoryrl/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 TrajectoryRL in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20TrajectoryRL%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/trajectoryrl-trajectoryrl/install
Install command: npx skills add trajectoryRL/trajectoryRL
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/trajectoryrl-trajectoryrl/install
LLM text format
/api/skills/trajectoryrl-trajectoryrl/install?format=text
Find alternatives
/api/skills/search?q=TrajectoryRL&limit=3
Agent prompt
Use TrajectoryRL for this task. Review https://www.openagentskill.com/api/skills/trajectoryrl-trajectoryrl/install, then install with: npx skills add trajectoryRL/trajectoryRLRegistry 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/trajectoryrl-trajectoryrl
LLM text
/api/registry/manifest/trajectoryrl-trajectoryrl?format=text
Install alias
/api/registry/install/trajectoryrl-trajectoryrl
Recommend
/api/registry/recommend?task=Use%20TrajectoryRL%20in%20an%20agent%20workflow&limit=3
Agent fit
Coding agents
Use-case tags
Platforms
Python, Claude Code, OpenAI Agents, Cursor
Audit report
Needs review · 79/100
Review install readiness, maintenance, trust, quality, and metadata warnings before adding this skill to an agent workflow.
Agent decision cockpit
Fallback candidate for Coding agents
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Coding agents
Trust label
Prototype first
Install path
Command ready
Use when
- Coding agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 69/100 quality profile
Review first
- Low GitHub adoption signal
- No OpenAgentSkill engagement data yet
Implementation path
- 1Install it in a sandbox agent and run one Coding 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
FIX20 GitHub stars
Stars/forks activity
FIX20 stars, 17 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
PASSMIT
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
- Low GitHub adoption signal
- Quality score needs review
- GitHub adoption: 20 GitHub stars
- Stars/forks activity: 20 stars, 17 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, external package install surface
- 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
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Operate local tools
Local desktop
I need my agent to operate local files and desktop apps in a repeatable workflow.
Workflow fit
Add it to a complete workflow
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.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Alternative shortlist
Compare before you install
Similar skills in this category, ranked with the same readiness and quality signals.
Claude Scientific Skills
A comprehensive collection of ready-to-use scientific and research skills for AI agents.
Awesome Claude Skills
A curated list of resources and tools for enhancing Claude AI workflows.
Khazix Skills
A collection of practical, installable AI agent skills for disk cleanup, AI news retrieval, and project management, following the Agent Skills standard.
Overview
# TrajectoryRL
> **Bittensor Subnet 11** — A reinforcement learning playground that continuously produces state-of-the-art skills for AI agents
[](LICENSE) [](https://www.python.org/downloads/) [](https://github.com/opentensor/bittensor)
Every platform shift creates a new software category. PCs gave us desktop apps. Smartphones gave us mobile apps. Agents are the next platform, and **skills are the software that runs on them**. The world needs far more skills than human developers can ship. Agents will write skills for other agents. TrajectoryRL is the RL playground where that happens.
The competition runs 24/7 on Bittensor. Miners compete every epoch to produce the best agent skills, validators evaluate them in real sandboxes with real protocols, and the winning skills surface automatically. Every season the bar rises. You don't bring us your prompt. **Skills ship, you install them.**
```bash pip install trajrl ```
One install gives any agent (Claude Code, Cursor, Codex, OpenClaw, Hermes, Manus, …) access to every skill the subnet has shipped. Source, catalog, and docs: [`trajrl`](https://github.com/trajectoryRL/trajrl).
## Overview
``` ┌──────────────────────────────────────────────────────────────┐ │ TRAJECTORYRL SUBNET (SN11) │ │ │ │ MINERS VALIDATORS │ │ ┌───────────────┐ ┌───────────────────┐ │ │ │ Write SKILL.md│ on-chain │ Read commitments │ │ │ │ Upload pack │ commitment │ from chain │ │ │ │ to public URL │─────────────────> │ │ │ │ │ │ │ Fetch packs, │ │ │ └───────────────┘ │ verify has
Platform Compatibility
Technical Details
- Version
- 1.0.0
- License
- MIT
- Last Updated
- 7/27/2026
- Published
- 7/27/2026
Frameworks & Tools
Decision snapshot
Fallback candidate
recent repository activity
Audit snapshot
Install review
Install and adoption review
- Security
- 80/100
- 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 audit before production use.
Growth loop
Share kit
Scenario-led draft for TrajectoryRL, ready for a manual X post.
The best agent skills feel small at first, then remove a task your agent used to improvise. TrajectoryRL: Bittensor Subnet 11 — an open skill factory that uses distributed compute and R... 20 stars https://www.openagentskill.com/skills/trajectoryrl-trajectoryrl?ref=x #AIAgents
Optional reply with install command
Listing + install path for TrajectoryRL: https://www.openagentskill.com/skills/trajectoryrl-trajectoryrl?ref=x Install: npx skills add trajectoryRL/trajectoryRL
Listing source
Community indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- trajectoryRL
- 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 community indexed listing is attributed to trajectoryRL 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/trajectoryrl-trajectoryrl)
[](https://www.openagentskill.com/skills/trajectoryrl-trajectoryrl)
[](https://www.openagentskill.com/skills/trajectoryrl-trajectoryrl/audit)
[](https://www.openagentskill.com/skills/trajectoryrl-trajectoryrl)Author
trajectoryRL
@trajectoryrl
Platform Fit
Health Signals
- GitHub stars
- 20
- Quality score
- 42/100
- Last GitHub push
- Jul 27, 2026
- Framework hints
- 1
- 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 adoption20 GitHub starsFIX
- Stars/forks activity20 stars, 17 forks; issue activity unavailable in current metadataFIX
- Recent maintenancePushed todayPASS
- License clarityMITPASS
- README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
- Dependency/runtime riskcommand execution surface, external package install surfaceCHECK
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