Agent skill

Ulw Loop

by rlaope in rlaope/oh-my-hermes

[omh] Ambitious project goal needing many build cycles: agentic interviewer - planner - researcher - builder - reviewer cycles until a real gate.

MITAuto-check passed

Install Ulw Loop

skills CLI
$ npx skills add rlaope/oh-my-hermes --skill ulw-loop -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install rlaope/oh-my-hermes ulw-loop --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ulw-loop .claude/skills/ulw-loop && rm -rf skills-src

Use ~/.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/

Facts

Skill name
ulw-loop
GitHub stars
3.2k
Token cost
~5.2k tokens
SKILL.md length
2,725 words
Files
4 (incl. references)
Skills in repo
143
Repo updated
First seen
Licence
MIT

At a glance

[omh] Ambitious project goal needing many build cycles: agentic interviewer - planner - researcher - builder - reviewer cycles until a real gate.

  • The user says: loop
  • SKILL.md covers Why This Exists, Do Not Use When, Examples and Completion Checklist, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Long horizon goal

What it does

Ulw Loop is an agent skill from rlaope/oh-my-hermes. [omh] Ambitious project goal needing many build cycles: agentic interviewer - planner - researcher - builder - reviewer cycles until a real gate. Use when the user says: loop, goal loop, long horizon goal, never stop, research plan goal feedback, token exhaustion resume, permission profile, star 10k.

Its SKILL.md is about 5.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/board-iteration.md`, `references/goal-constraint-discipline.md` and `references/measured-loop-discipline.md`).

The repository describes itself as: All in one plugin for Hermes Agent ⚚ the coding intelligence, a long-term memory system and model optimized workflow packages. The licence is MIT.

When your agent uses it

  • The user says: loop
  • Long horizon goal
  • Research plan goal feedback
  • Token exhaustion resume

Example prompts

  • “/ulw-loop”

What it can do on your machine

Read from SKILL.md and the folder at commit 74abedb. It shows what the files ask for, not the result of running them.

  • Tool permissions

    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.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ulw Loop loads about 5.2k tokens when it runs, and up to ~9.5k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 2,725 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~79
When it runs · the whole SKILL.md, loaded when a task matches
~5.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.5k

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.

Safety

Auto-check passed

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.

SKILL.md

The full file from rlaope/oh-my-hermes at commit 74abedb, republished under its MIT licence (© rlaope). 2,725 words, ~5,166 tokens.

Download SKILL.mdSave it as .claude/skills/ulw-loop/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
ulw-loop
description
[omh] Ambitious project goal needing many build cycles: agentic interviewer -> planner -> researcher -> builder -> reviewer cycles until a real gate. Use when the user says: loop, goal loop, long horizon goal, never stop, research plan goal feedback, token exhaustion resume, permission profile, star 10k.

Loop

This is a Hermes-native loop workflow skill.

Why This Exists

loop exists for goals whose correct implementation cannot be known upfront but can be discovered through bounded cycles of definition, action, verification, and revision without confusing planned cycles with observed progress.

Do Not Use When

  • The user asks for one bounded delivery cycle; use ultrawork's delivery-boundary capability instead.
  • Scope and milestones are already known and only durable checkpoint/resume tracking is needed; use ultrawork's durable-checkpoint capability.
  • The user gives only a north-star outcome such as revenue, stars, or adoption and has not accepted a bounded first loop goal.
  • The goal is too vague to name an observable problem, next artifact, verification signal, or stop condition.
  • The goal depends mainly on external waiting, adoption, revenue, or community response without observable local next actions.
  • The permission profile does not allow repeated research, handoff, queue, or feedback cycles.

Examples

Good example:

  • Prompt: ./loop make OMH a credible Hermes workflow pack with install, docs, QA, and feedback cycles.
  • Expected behavior: Start a permission-scoped loop, maintain loop_cycle/v2 selected-driver state, choose the next concrete task, and keep external outcomes as waiting states.
  • Why: The request is long-horizon and needs repeated discovery, verification, feedback, and resume decisions.

Bad example:

  • Prompt: ./loop merge this already reviewed one-line README fix.
  • Expected behavior: Use a direct delivery or PR workflow instead of starting a persistent loop.
  • Why: The task is bounded and should stop after merge evidence rather than create ongoing cycles.

Completion Checklist

  • The request is classified as task, project, north-star ambition, external-wait, or unclear before a loop starts.
  • The current loop_status_card/v1 names the queue item, tick status, verification_plan, and next action.
  • failure_mode_summary checks verification_gap, comprehension_debt, and cognitive_surrender before progress advances.
  • Completion is backed by linked goal/runtime evidence; queued loop ticks alone are not observed work.
  • Native /goal activation and continuation are backed by loop_goal_driver_observation/v1, and each observed role advance is backed by loop_phase_transition/v1.

Recovery Notes

  • Prefer the native omh_loop tool when the plugin is loaded: assess, start, status, feedback, permit, run_once, goal_driver_observe, and queue_observe reach the same loop_cycle/v2 state, and a mutation submits the record_revision that status reported. Where it is absent, the same lifecycle is omh loop assess|start|status|feedback|permit|run-once|goal-driver-observe|queue observe, and every other Loop surface stays on that CLI.
  • If a queued tick is pending, show it as prepared queue state and use loop status/run-once before claiming progress.
  • If feedback is unclear, ask one gate question or route back to research/plan rather than advancing the loop.
  • If the goal turns into external waiting, record the waiting state and next observable signal instead of continuing locally.
  • Checkpoint on context/budget exhaustion. Migrate loop_cycle/v1 with migrate-driver --apply before external binding.
  • Resume paused native goals with re-registered gates; external goals follow driver recovery. Transfers require observed stopped/absent reconciliation; handoffs never dispatch.
  • If the loop runs out of next actions, re-read the scoped files, recombine the near-miss attempts, then escalate to a more radical change before declaring the loop blocked.

Workflow Lane

  • Current lane: Intent -> plan (oh-my-hermes, meta-router, deep-interview, context, plan, ralplan, adversarial-consensus, codebase-onboarding, +8 more) - clarify, plan, ship, or loop goals.
  • If intent belongs to another lane, hand back to oh-my-hermes or name the adjacent workflow.
  • Shared product, routing, compatibility, and evidence rules: omh-routing/references/skill-common-rail.md.

Use When

Use when the user starts a high-level goal or invokes loop. Direct loop invocation means start/continue through interviewer, planner, researcher, builder, reviewer, and loop-controller lanes until a real gate stops it.

Strong routing signals: `loop`, `./loop`, `$loop`, `goal loop`, `long horizon goal`, `never stop`, `research plan goal feedback`, `token exhaustion resume`, `permission profile`, `star 10k`, `10k star`, `loop engineering`, `keep running until done`, `루프`, `목표 루프`, `장기 목표`, `끝까지`, `토큰 고갈`, `피드백 루프`, `끝날 때까지 계속`, `계속 돌려줘`

Catalog Metadata

Category: goal-loop Phase: continuous-goal-loop Hermes role: planner Quality tier: loop-gated Reasoning demand: heavy

Quality bar:

  • Treat direct loop, ./loop, $loop, and OMH loop invocations as a start/continue signal rather than a picker or passive clarification path.
  • Classify the goal as task, project, ambition, external-wait, or unclear inside the loop, then keep progressing until a real permission, evidence, verification, context, budget, or external-wait gate appears.
  • A mid-run user message is an interjection, not a stop: answer it briefly and, in the same reply, continue the run — re-read the phase todo when one is active and dispatch or advance the next pending step, or name the armed wait it is waiting on -- handle, bound completion signal, deadline -- instead of re-reading status. Only the user's explicit stop or cancel, or the engine's own completion gate, ends the run; when the interjection changes scope, say so and update the declared plan or todo instead of silently abandoning it. A mid-run message is the latest steering for the active task, not automatically a replacement objective: it replaces the objective when the user says so and steers the current one otherwise.
  • A follow-up that needs new authority, materially expands the scope, or changes external state not already authorized is described first and started only on the user's approval: the turn ends by naming that next action and asking whether to take it, as one question carrying the choices the user has, never by declaring what will not be done; persistence never broadens the authorized scope. A refused escalation is answered the same way, with a safer alternative inside the boundary or the authorization the boundary asks for — never a workaround or an indirect execution.
  • The closing brief scales to the change: one or two sentences plus the observed validation for a simple change, more only when the complexity earns it. Lead with the result or decision, in the user's words; omit abandoned approaches unless they explain a tradeoff the reader needs; narrate no internal bookkeeping (todo transitions, waits). When the work stops at a boundary or at a decision the user owns, end with the next action offered as a question, and state what was left undone as the option it leaves open, never as a refusal. Required closing lines stay outside this scaling: the observed run summary, and any prepared-not-observed or unmerged work, are stated whatever the brief's length.
  • Expose core OMH roles: interviewer, planner, researcher, builder, reviewer, and loop controller.
  • Route tiny direct tasks to one-cycle delivery surfaces instead of forcing loop overhead.
  • Reframe a north-star ambition into a bounded arena, observable problem, next loop goal, and next verification without shrinking its ambition.
  • Separate task discovery, distribution, execution, verification, next-task decision, runtime tick queueing, durable-checkpoint/handoff, feedback, waiting, and resume decisions.
  • Expose a permission profile before executor/runtime dispatch, repository mutation, PR, merge, or external publishing.
  • Expose the automation, worktree, skill, connector, and subagent building-block states without treating planned blocks as observed work.
  • Choose workflow patterns such as single-step, fan-out-and-synthesize, adversarial verification, tournament, or triage batch as orchestration metadata only.
  • Keep repeated scaffold shape stable, summarize within bounded budgets, and add verifier lanes only when risk or evidence warrants them.
  • Keep prepared worktree/subagent/connector plans, observed executor work, linked goal completion, and external waiting as distinct evidence states.
  • Use cheap inner-loop checks frequently and expensive outer-loop checks sparingly.
  • Keep the practical small-loop recipe visible: test as stop signal, plan -> execute -> verify, one task at a time.
  • Surface verification_gap, comprehension_debt, and cognitive_surrender as warnings before a loop starts looking self-steering.
  • Session-bound host_observed resumable_goal plus explicit coding ownership prepares one executor goal. Otherwise use native /goal and /goal gate add. Never prepare two controllers.
  • Ingest bounded snapshots via omh loop goal-driver-observe. External state guides recovery, not checkpoint decisions; native turns still require activation and contiguous same-session evidence.
  • Treat ticks as preparation only. Advance one legal role phase through loop_phase_transition/v1 only after its named gate has observed evidence.
  • Treat a judge done verdict, a turn-ceiling pause, or a gate-retry pause as narration; completion still requires the linked goal ledger completion gate and observed evidence.
  • Treat any future change to the default as a maintainer-reviewed product decision, not a runtime phase or automatic loop outcome.
  • Compare only observed outcomes under matched task, model/provider, permissions, turn budget, and verification surface; unresolved evidence keeps the current default.
  • Keep promotion governance separate from goal-ledger completion and ordinary measured-loop keep/discard decisions; do not invent subjective scorers, fixed numeric thresholds, minimum run counts, weighted percentages, or per-turn artifact quotas.
  • Name the one element gating this loop from the loop_constraint_assessment/v1 block before choosing the next action; if none is binding, say so from the recorded reason rather than assuming.
  • For an iteration that must outlive this session, run under another profile, or needs its own Hermes worktree, load references/board-iteration.md: builder and verifier rows chained by parents, a needs_input block when the loop must stop for the user, and resume from board readback rather than memory.
  • When the goal is measurable, declare the evaluation contract before the first attempt - exact command, metric name, direction, and the rule that the loop may not modify the scoring harness - and bind every keep or discard decision to it; when no such contract exists, say the goal is unmeasured instead of scoring it by judgement.
  • Run a measurable cycle as attempt, commit, measure, then keep or reset; a reset is the normal discard, and rewinding to an older commit is for a run of discards that traces to one bad ancestor.
  • For a measurable loop, keep a human-scannable ledger the loop itself appends to - one tab-separated line per cycle carrying commit, metric, cost, keep or discard or crash, and a one-line description - beside the JSON loop artifacts.
  • Send long-running cycle output to a log file and pull only the declared metric and error lines into context; read the whole log only when the cycle crashed.
  • Choose the wait strategy before starting long-running work and bind it to a completion signal the host exposes, never to a status loop: a command that fits one tool call runs once in the foreground with a duration-sized timeout; a longer terminal command runs in the background with completion notification armed and no process-status polling; a delegated lane relies on its delivered result while the parent continues independent work or ends the turn; a CI, PR, deploy, file, port, log-line, or external-session condition uses the host's monitor when observed, else exactly ONE bounded watcher or adaptive backoff outside model turns. Record the handle and observation mode at dispatch; every armed wait needs a hard deadline, a cancellation path, and a fallback naming the missing capability. Each wait closes in one terminal state with bounded evidence; an unbounded idle or busy-wait is a defect and a lost notification times out. One decision-changing midpoint peek and any user-requested status check stay allowed; neither is the wait mechanism. Ladder and terminal states: shared rail.
  • On an equal metric keep the simpler change, always keep an improvement achieved by deletion, and do not let a small gain buy added complexity.
Show full SKILL.md (942 more words)Show less

Handoff policy:

Keep loop orchestration, role sequencing, verification-tier selection, deterministic runtime ticks, loop_engineering/v1 status, feedback evaluation, and permission narration in Hermes; prepare executor/runtime/worktree/connector/verifier handoffs only for concrete work and record completion only from linked evidence.

Required inputs:

  • loopability assessment
  • north-star goal summary when present
  • bounded arena
  • observable problem
  • next verification
  • goal reframe
  • success criteria
  • permission profile
  • feedback or wait signal

Expected outputs:

  • loopability_assessment/v1 task/project/ambition classification
  • loop_start_card/v1 setup prompt
  • loop_cycle/v2
  • loop_engineering/v1 pipeline/building-block snapshot
  • loop verification_policy for inner/outer checks
  • loop failure_mode_summary over verification gap, comprehension debt, and cognitive surrender
  • small-loop guidance: test as stop signal, plan -> execute -> verify, one task at a time
  • loop_status_card/v1 next action
  • loop_runtime/v1 queued tick with verification_plan refs
  • loop_queue_handoff/v1 only when permitted
  • executor-neutral handoff only when permitted
  • external-wait or checkpoint boundary
  • loop_goal_driver_handoff/v1 selected goal
  • loop_goal_driver_observation/v1 metadata-only activation and same-session contiguous turn evidence
  • loop_phase_transition/v1 evidence-backed progress record

Artifact expectations:

  • loop_cycle/v2: loop_driver/v1 and loopability_assessment/v1 metadata
  • loop_engineering/v1 status over automation, worktree, skill, connector, subagent, verification policy, and failure modes
  • loop_runtime/v1 queue entries with context_policy_ref, cost_policy_ref, and verification_plan
  • loop_subagent_result_contract/v1 for prepared subagent handoffs
  • loop_status_card/v1 wrapper payload with loopability_assessment, failure_mode_summary, small_loop_guidance, and native-goal observation status
  • loop_start_card/v1 wrapper setup card
  • linked goal_ledger/v1 only when completion evidence is required
  • loop_goal_driver_handoff/v1 selected goal with OMH completion ownership
  • loop_goal_driver_observation/v1 native history or loop_executor_goal_observation/v1 advisory external snapshots ingested through goal-driver-observe
  • loop_phase_transition/v1 stored only when evidence advances an observed phase

Safety rules:

  • Do not treat loop persistence as permission to bypass the selected permission profile.
  • Do not treat a runtime tick as worktree creation, subagent dispatch, connector I/O, implementation, review, CI, merge, publication, or completion evidence.
  • Do not claim goal completion from loop state; require linked goal_ledger/v1 completion evidence.
  • When context or token budget runs out, checkpoint or rely on resumable state instead of pretending the loop is complete.
  • External results such as market response, stars, or adoption are waiting states unless observed evidence is supplied.
  • Do not let unattended loop progress bypass verification; missing or failed verification returns to plan/research or waits for evidence.
  • Do not let comprehension debt or cognitive surrender hide behind green-looking loop status.
  • Do not claim a goal is complete because the upstream judge said done, the turn budget ran out, or a gate paused the loop.

Goal Driver Ownership

Hermes narrates; the selected executor runs its goal; OMH verifies. Driver recovery never overrides checkpoint next_action.

Constraint Discipline

Before choosing the next action, name the one element gating this loop's goal progress - the binding constraint - then work it in order:

  • Identify - read the binding constraint from recorded state: wait_reason, blocked and prepared_not_observed queue counts, failure-mode warnings, and the linked goal completion gate.
  • Exploit - convert work the loop has already paid for: observe the prepared item or satisfy the one open criterion before preparing anything new.
  • Subordinate - pace every other lane to the constraint; an idle non-constraint lane is healthy, a growing prepared pile is cost.
  • Elevate - only after exploit and subordinate still leave it binding, escalate: more budget, a wider permission envelope, another executor - named as a costed last resort.
  • Repeat - re-identify at the next iteration boundary; resolving one constraint surfaces the next.

The loop_constraint_assessment/v1 block on the loop_status_card/v1 answers Identify deterministically from recorded state. The constraint assessment explains why the loop is gated; the card's own next_action stays the recorded directive. When the two differ, the binding constraint names what to fix and next_action names the recorded step.

Load references/goal-constraint-discipline.md for the full method: the translation table, the five focusing steps, and the anti-patterns.

Measured Loops

The measured-loop rules in the quality bar above apply when a loop has a score.

A loop is measurable when one command produces one number and a direction. Fix that evaluation contract before the first attempt, declare it in the loop's own state, and let it decide what is kept - the loop never edits the scoring harness that judges it. A loop with no such command says it is unmeasured and keeps deciding on verification evidence instead of inventing a score.

The two disciplines compose and do not compete: the binding constraint chooses which attempt to make, and the metric chooses whether that attempt is kept.

The metric never decides completion. The loop still stops at its permission, evidence, verification, context, budget, and external-wait gates, and closing the goal still requires linked goal_ledger/v1 evidence.

Load references/measured-loop-discipline.md for the full method: the contract fields, the keep and discard rules, the ledger columns, the log rail, and the idea-exhaustion ladder.

Runtime Evidence

Preferred harness for this skill: goal-loop.

sh
omh runtime record --skill loop --harness goal-loop --status started

Record observed delegation results; otherwise return not_available or not_observed. Prepared OMH routing is not execution, review, CI, merge-readiness, or merge evidence.

  • When wrapper metadata includes memory_review_card/v1 or handoff_context_pack/v1, treat it as reviewed OMH-local or wrapper-supplied context only. Use conflict-free context summaries to shape plans and handoffs, but do not claim Hermes internal memory was read or changed. Preserve workflow intent and stop conditions; verify before claiming completion. Reply in the user's own words and the host's own voice: its SOUL.md persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a stop condition or a decision the user owns ends the turn, offer the next action as a question rather than declaring what will not be done.

Use Hermes-native subagent/delegation features when available: native subagents -> Hermes delegation when available, otherwise sequential lanes.

Shared product, compatibility, topology, memory, harness, and execution rules: omh-routing/references/skill-common-rail.md. Load it when applicable; otherwise name an unavailable capability.

© rlaope, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in skills/ulw-loop of rlaope/oh-my-hermes.

  • SKILL.md
  • references/board-iteration.md
  • references/goal-constraint-discipline.md
  • references/measured-loop-discipline.md

Open the folder on GitHubat commit 74abedb

Compare with similar skills

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Nature Paper CardYuan1z0825/nature-skills46k2 repos~2.1kAutomated safety check: PassApache-2.0

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Questions about Ulw Loop

What does Ulw Loop do?

[omh] Ambitious project goal needing many build cycles: agentic interviewer - planner - researcher - builder - reviewer cycles until a real gate. Ulw Loop is an agent skill from rlaope/oh-my-hermes. [omh] Ambitious project goal needing many build cycles: agentic interviewer - planner - researcher - builder - reviewer cycles until a real gate.

When should I use Ulw Loop?

Ulw Loop fits situations like: the user says: loop; long horizon goal; research plan goal feedback; token exhaustion resume.

How do I install Ulw Loop in Claude Code?

Run `npx skills add rlaope/oh-my-hermes --skill ulw-loop -a claude-code`. Or copy the skill folder (skills/ulw-loop in rlaope/oh-my-hermes) into .claude/skills/ulw-loop in your project. Claude Code loads it when a task matches its description.

How do I install Ulw Loop in Codex?

Run `npx skills add rlaope/oh-my-hermes --skill ulw-loop -a codex`. Or copy the skill folder (skills/ulw-loop in rlaope/oh-my-hermes) into .agents/skills/ulw-loop in your project. Codex loads it when a task matches its description.

Can I use Ulw Loop in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rlaope/oh-my-hermes --skill ulw-loop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ulw-loop, .gemini/skills/ulw-loop, .github/skills/ulw-loop and .opencode/skills/ulw-loop in your project.

What does Ulw Loop need to run?

SKILL.md names no scripts, command-line tools or credentials: Ulw Loop is instructions for the agent only.

Does Ulw Loop access the network?

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.

Is Ulw Loop safe to install?

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.

What licence does Ulw Loop use?

Ulw Loop is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ulw Loop use?

About 5.2k tokens (SKILL.md is roughly 21k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.3k tokens, read only when the agent opens those files.

What are the alternatives to Ulw Loop?

Skills that share tags, products or a category with Ulw Loop: NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars), Hypothesis Generation (spacering-net/codeg, 3.8k stars), GitHub Deep Research (bytedance/deer-flow, 83k stars) and Tavily Web Search (allenpeng0705/EnvoyMesh, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ulw Loop?

rlaope (a GitHub user) maintains it in rlaope/oh-my-hermes, which has 3,201 GitHub stars. The repository holds 143 skills in this directory. The repository was last updated on October 7, 2026.

Source: rlaope/oh-my-hermes on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.