trajrl 1.0.0

TrajRL Python toolbelt — installs trajrl skill hub CLI plus trajectoryrl-inspector and bittensor-subnet-inspector.

trajrl

The official Python toolbelt for TrajectoryRL — an open skill factory that uses Bittensor's distributed compute and incentive layer with reinforcement learning to produce state-of-the-art agent skills.

A single pip install trajrl ships three CLI binaries:

Binary	Purpose
trajrl	Skill hub installer. Browse and install agent skills published on trajrl.com into your local agent skill directories (Claude Code, Cursor, Codex, Hermes, OpenClaw). Mirrors the npm trajrl CLI.
trajectoryrl-inspector	TrajectoryRL / SN11 deep analysis. Validators, miners, scores, weight distribution, scenario heatmaps, eval log archives.
bittensor-subnet-inspector	Generic Bittensor on-chain queries for any subnet — metagraph and subnet hyperparams (tempo / emission / burn).

CLI output is Rich tables in a TTY and JSON when piped.

Install

pip install trajrl

The PyPI distribution name is intentionally still trajrl while the layout stabilizes; see the roadmap in CLAUDE.md.

trajrl — skill hub installer

trajrl skills list                              # all available skills
trajrl skills list --tag dev-tools --tag data   # filter by tags (AND)
trajrl skills search "code review"              # free-text search
trajrl skills show self-learning                # render full SKILL.md
trajrl skills add self-learning                 # install into every detected agent
trajrl skills add self-learning --agent cursor  # only install for Cursor
trajrl skills add self-learning --target ./local-skills
trajrl skills sync                              # re-pull installed skills if newer upstream
trajrl skills sync --dry-run

Default agent skill directories (auto-detected by directory existence):

Agent	Path
Claude Code	~/.claude/skills/<slug>/SKILL.md
Cursor	~/.cursor/skills-cursor/<slug>/SKILL.md
Codex	~/.codex/skills/<slug>/SKILL.md
Hermes	~/.hermes/skills/<slug>/SKILL.md
OpenClaw	~/.openclaw/skills/<slug>/SKILL.md

trajectoryrl-inspector — SN11 analysis

trajectoryrl-inspector status                   # network health
trajectoryrl-inspector analyze --uid 5 --deep   # full validator analysis with miner drill-down
trajectoryrl-inspector analyze HOTKEY --logs    # include recent eval logs

trajectoryrl-inspector submissions              # recent pack submissions
trajectoryrl-inspector submissions --failed
trajectoryrl-inspector download --uid 63        # download a miner's pack + metadata
trajectoryrl-inspector download HOTKEY PACK_HASH

trajectoryrl-inspector logs --validator HOTKEY --limit 20
trajectoryrl-inspector logs --eval-id 20260329_1430_w42 --show
trajectoryrl-inspector logs --eval-id 20260329_1430_w42 --dump-to ./debug/

A miner eval archive contains:

SKILL.md                                 # miner's product
JUDGE.md                                 # scoring rubric used
metadata.json                            # final_score, delta, episode qualities
world.json                               # scenario context + salt
episodes/episode_N/
  testee_transcript.txt                  # agent's session log
  judge_transcript.txt                   # judge agent's grading log
  evaluation.json                        # per-criterion scores + summary
  episode.json                           # fixtures + instruction

Override the API base URL with --base-url or TRAJRL_BASE_URL.

bittensor-subnet-inspector — generic chain queries

bittensor-subnet-inspector metagraph --netuid 11
bittensor-subnet-inspector metagraph --netuid 11 --network test
bittensor-subnet-inspector emission --netuid 11

Override the network with --network or BT_NETWORK (finney | test | local | archive | ws(s)://endpoint).

JSON output

Piped output is JSON by default for every binary. Force JSON in a TTY with --json / -j.

trajrl skills list --json | jq '.skills[].slug'
trajectoryrl-inspector status | jq '.validators.validators[].hotkey'
bittensor-subnet-inspector metagraph -u 11 | jq '.neurons[0:5]'

Skills

The skill catalog in this repo teaches agents how to use the binaries:

• skills/trajectoryrl-inspector/SKILL.md
• skills/bittensor-subnet-inspector/SKILL.md

Links

• Subnet repo: https://github.com/trajectoryRL/trajectoryRL — incentive mechanism, evaluation framework, Season 1 spec
• Bench: https://github.com/trajectoryRL/trajrl-bench — three-container eval sandbox (sandbox + testee + judge)
• Website: https://trajrl.com — leaderboard, live subnet data, dashboards, skill hub
• Public API: PUBLIC_API.md — read-only, no auth, base URL https://trajrl.com