Agent skill

Parallel Feature Managers

by Cotal-AI in Cotal-AI/Cotal

Run several independent Cotal features concurrently by creating one Git worktree and one spawn-capable mesh manager per feature; each manager staffs a review panel in a dedicated channel, adds one…

Apache-2.0Auto-check passedAgent Workflows

Install Parallel Feature Managers

skills CLI
$ npx skills add Cotal-AI/Cotal --skill parallel-feature-managers -a claude-code

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

GitHub CLI
$ gh skill install Cotal-AI/Cotal parallel-feature-managers --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/Cotal-AI/Cotal.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/parallel-feature-managers .claude/skills/parallel-feature-managers && 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
parallel-feature-managers
GitHub stars
313
Token cost
~5.3k tokens
SKILL.md length
2,944 words
Files
1
Skills in repo
5
Repo updated
First seen
Licence
Apache-2.0

At a glance

Run several independent Cotal features concurrently by creating one Git worktree and one spawn-capable mesh manager per feature; each manager staffs a review panel in a dedicated channel, adds one…

  • Works in 7 steps: Establish the feature matrix → Make private repository context available → Create manager and reviewer personas… → …
  • The user asks to split multiple features across managers/worktrees
  • SKILL.md covers Models: cross-vendor panels…, Hard rules, 1. Establish the feature matrix and 2. Make private repository…, plus 5 more sections
  • Calls git and gh

What it does

Parallel Feature Managers is an agent skill from Cotal-AI/Cotal. Run several independent Cotal features concurrently by creating one Git worktree and one spawn-capable mesh manager per feature; each manager staffs a review panel in a dedicated channel, adds one independent cold reviewer briefed under the cold-review skill, owns plan-to-commit delivery, and escalates unresolved product decisions by DM to the coordinator for relay to the user. Use when the user asks to split multiple features across managers/worktrees, run parallel feature teams on Cotal, or have managers create…

Its SKILL.md is about 5.3k 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 Agent Workflows, covering Git worktrees. It works with Git and Model Context Protocol. The repository describes itself as: The open standard for agent coordination. The licence is Apache-2.0.

When your agent uses it

  • The user asks to split multiple features across managers/worktrees
  • Run parallel feature teams on Cotal
  • Have managers create their own review panels

Example prompts

  • “/parallel-feature-managers”

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Establish the feature matrix
  2. Make private repository context available
  3. Create manager and reviewer personas with policy first
  4. Seed channels and launch managers
  5. Monitor without taking over
  6. Completion gate
  7. Integrate and clean up only on request

What it can do on your machine

Read from SKILL.md and the folder at commit bf3a7b2. 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

    Shell commands in SKILL.md call:

    • git
    • gh

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

  • Network

    No URLs in SKILL.md. Its commands use git and gh, which can reach the network depending on how they are called.

    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

Parallel Feature Managers loads about 5.3k tokens when it runs. Until then it costs about 142 tokens; SKILL.md has 2,944 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~142
When it runs · the whole SKILL.md, loaded when a task matches
~5.3k

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 Cotal-AI/Cotal at commit bf3a7b2, republished under its Apache-2.0 licence (© Cotal-AI). 2,944 words, ~5,342 tokens.

Download SKILL.mdSave it as .claude/skills/parallel-feature-managers/SKILL.md (or your agent's skills folder).
name
parallel-feature-managers
description
Run several independent Cotal features concurrently by creating one Git worktree and one spawn-capable mesh manager per feature; each manager staffs a review panel in a dedicated channel, adds one independent cold reviewer briefed under the cold-review skill, owns plan-to-commit delivery, and escalates unresolved product decisions by DM to the coordinator for relay to the user. Use when the user asks to split multiple features across managers/worktrees, run parallel feature teams on Cotal, or have managers create their own review panels.

Parallel feature managers

Use the live Cotal mesh as a hierarchy:

  • The current session is the coordinator. It creates isolation, sets policy, monitors progress, and relays decisions. It does not duplicate implementation or panel review.
  • Each feature gets one manager rooted in its own Git worktree and branch. The manager owns the complete plan -> implementation -> review -> test -> commit loop.
  • Each manager spawns three or more read/review-only peers in one dedicated channel: engineer, security, and critic at minimum.
  • Near the final gate, each manager adds one independent cold reviewer, briefed under the cold-review skill, which is the single source for how that seat is briefed, isolated and graded. It is a control on the panel rather than a fourth panelist.

Models: cross-vendor panels are a correctness rule, not a preference

No two seats whose agreement is load-bearing may share a model family. A finding confirmed by a seat of the same family as the one that made it is an echo, not a confirmation. A panel of three same-model reviewers has approved a head carrying a defect that all three missed, and what surfaced it was a differently framed read rather than a fourth verifier.

State it that way rather than as a headcount. Availability is a property of the moment, not of the vendor: the same model has joined and delivered one hour and failed to join the next, on the same host with the same tooling. A rule phrased as "N distinct vendors" is unsatisfiable on a degraded fleet and silently so, and a rule that can be broken by the clock gets quietly ignored rather than obeyed. cold-review carries the degradation order and the floor.

Pin the model explicitly at spawn AND in the persona, because unrecorded capability is ungraded-in-effect: a reviewer whose effort or model nobody recorded produces a verdict nobody can weigh afterwards.

Managers may run a stronger model than their reviewers. Reviewers should not run the same model as the coordinator, so that the panel cannot inherit the coordinator's blind spots.

Verify the exact model identifiers against the connector's own catalog before spawning, and treat a declared reasoning-effort tier as unverified until a seat has actually launched with it: a catalog can declare tiers the provider refuses, and a refused tier kills the seat at launch.

Hard rules

  • Never switch the coordinator's branch. Create worktrees from a named committed base.
  • One manager per feature, one reviewer per channel lane, and one independent cold reviewer per feature. Select that seat's contact and destination under cold-review. A file destination is a norm-only fallback and cannot satisfy a gate that requires broker-attested poster identity. No sibling instances or tester fan-out unless the user requests it.
  • Managers alone edit their feature worktree. Reviewers only read, run non-mutating checks, and post findings with file/line references.
  • Each feature uses one panel channel, review.<feature-slug>, plus one dedicated cold record channel, review.<feature-slug>.cold-record. Keep acknowledgements and status chatter off the panel channel; use it for plans, findings, reasoned dispositions, code-review requests, and final results.
  • The independent cold reviewer is not on that channel. Its isolation, briefing and reporting are owned by cold-review; enforce them there rather than restating them per lane.
  • Product/design decisions travel by DM: manager -> coordinator -> user -> coordinator -> manager. A manager must not guess through an unresolved consequential choice.
  • Never merge, push, open a PR, or remove worktrees unless the user asks.
  • Never disturb the live mesh broker. Tests use throwaway spaces and random high ports; kill test processes by exact PID, never broad pkill.

1. Establish the feature matrix

For every requested feature, choose and record:

FieldExample
Featurechannels export
Slugchannels-export
Branchfeat/channels-export
Worktreesibling path such as Cotal-feature-channels-export
Manager personamgr-channels-export
PRexact number, or none until one exists
Channelreview.channels-export
Contractconcrete behavior, boundaries, tests, and known non-goals

Resolve branch/path collisions before creating anything. Inspect git status, git worktree list --porcelain, and existing branches. Do not clean or revert unrelated dirty state.

Create the worktrees sequentially because they mutate shared Git metadata:

sh
git worktree add -b feat/<slug> /absolute/sibling/Cotal-feature-<slug> <base>

Use the same base commit for all features unless the user explicitly wants stacked work.

2. Make private repository context available

Before launch, verify each worktree can read .internal/plans/STATUS.md and the relevant guidelines. Normally git submodule update --init .internal is sufficient.

If initialization fails because the superproject pins an unavailable commit:

  1. Compare the committed pointer (git ls-tree <base> .internal) with the active local submodule HEAD (git -C .internal rev-parse HEAD). This is repository-state drift, not a Cotal ACL problem.
  2. Prefer fixing/publishing the intended submodule commit and superproject pointer when authorized.
  3. For a temporary parallel run, materialize the known-good local .internal commit independently into each feature worktree from the active local submodule repository.
  4. Tell managers the superproject may show M .internal; they must read it but never stage, edit, or include it in feature commits.

Do not leave managers blocked merely because a fresh worktree cannot fetch a private commit that is already available and verified locally. Do not pretend the mismatch is clean either; report the permanent repair needed.

3. Create manager and reviewer personas with policy first

Manager agents need capabilities: [spawn] plus channel ACLs. Reviewer agents need channel ACLs and must not have spawn. cotal_persona cannot grant policy, and cotal_spawn cannot override allowSubscribe or allowPublish, so author every persona file before spawning. These files are local/ignored in this repo. A clean machine that has only a default persona cannot staff the panel: the spawn names below fail unless those files exist, and a leftover local persona is not a substitute because its grants may be missing or wider than the lane.

yaml
---
name: mgr-<slug>
role: feature-manager
model: <pinned manager model>
description: Owns <feature> and its review panel.
tags: [manager, <slug>]
subscribe: [review.<slug>]
allowSubscribe: [review.<slug>, review.<slug>.cold-record]
allowPublish: [review.<slug>]
capabilities: [spawn]
---

Author matching files for the three panel seats and the cold seat before any spawn. Give every reviewer persona a lane-scoped filename such as review-<slug>-engineer; the persona catalog is shared by concurrent lanes, so generic filenames collide even when the worktrees and channels do not. Panel personas subscribe and publish only on review.<slug>. The cold persona lists empty subscribe and allowSubscribe so non-join is a property of the seat, not an instruction, and may publish only to review.<slug>.cold-record. Do not reuse or redefine a reviewer persona from another lane.

yaml
---
name: review-<slug>-engineer
role: reviewer
model: <pinned panel model, family A>
subscribe: [review.<slug>]
allowSubscribe: [review.<slug>]
allowPublish: [review.<slug>]
capabilities: []
---
yaml
---
name: review-<slug>-freelance
role: reviewer
model: <pinned cold model, not the author's family>
subscribe: []
allowSubscribe: []
allowPublish: [review.<slug>.cold-record]
capabilities: []
---

Repeat the panel template for review-<slug>-security and review-<slug>-critic with different model families. If a referee is later required, author review-<slug>-referee the same way as the cold seat, with empty subscribe and allowSubscribe plus one dedicated record-channel allowPublish grant, before spawning it.

The manager prompt must include all of the following:

  • Exact feature contract, branch, and absolute worktree path.
  • Own the feature end to end and commit only intended feature files.
  • Read repo instructions, current docs, and .internal before editing.
  • Join review.<slug> first.
  • Spawn only personas authored in this section. Spawn review-<slug>-engineer, review-<slug>-security, and review-<slug>-critic, seated so that no two of them whose agreement is load-bearing share a model family. That is the ordinal rule above and not a headcount. Where the vendor set is short, name the collision mechanically and the class it leaves uncovered. Naming is disclosure, not completion: a panel whose load-bearing approvals are same-family echoes cannot complete, including A, A, A and A, B, A. Do not treat a staffed-but-collided panel as a passing gate. Give each its own detached worktree as cwd. A reviewer grades in its own tree, never in the tree it is grading, and "read-only" must name the git write verbs explicitly (checkout, switch, stash, reset, clean, restore) rather than only saying "do not edit source".
  • DM each returned reviewer identity to join the channel and remain read/review-only.
  • Run plan review before editing, code/test review after implementation, fold valid findings, and ask all three for a final disposition.
  • After the panel and implementation converge, spawn exactly one review-<slug>-freelance in its own detached worktree and brief it under the cold-review skill, which owns what that seat is given, where its verdict goes, what the verdict binds, and how the rules degrade when the vendor set is short. Do not restate any of it here.
  • Resolve cold findings under cold-review's canonical rule, never by telling that seat to reconsider. If the manager authored the change and a blocker needs independent refutation, send the coordinator only the exact-sha finding and artifact. The coordinator provisions a fresh review-<slug>-referee in its own detached worktree; that seat had no part in authoring, grading or supervising the lane, does not share a model family with the author, and receives none of the author's rationale. The coordinator provisions the referee but does not serve as it. If a fold changes code, return the result to the channel panel for another final pass and re-pin the cold seat to the new sha with its delivery limit explicitly reset in writing. If that seat is gone, author a fresh cold persona with empty subscribe and allowSubscribe plus only its dedicated record-channel allowPublish grant, spawn it, and brief it under cold-review on the new sha as a new one-delivery seat. That is a successor, not a third closure route for the old sha.
  • Re-resolve the head as an action, not as a later assertion. At briefing, at any completion claim, and again before merge, the manager or coordinator first resolves the review target. For a PR, run git ls-remote origin refs/pull/<n>/head and cross-check gh pr view <n> --json headRefOid. For a branch with no PR, resolve git ls-remote origin refs/heads/<branch> and omit the PR-only API cross-check. Fetch the resolved object into the detached review worktree if it is not already present, then run git cat-file -t on that object with a known-good control and a known-missing full-width object id, such as forty zeroes, that must fail. Do not make the negative control by appending a character to the real id: Git may resolve the valid leading object id and return success. Name the actor who ran the instruments. If applicable instruments disagree, stop. A verdict or refutation that names a different sha does not close this head.
  • Run the relevant tests itself. Reviewers do not edit source.
  • Escalate only unresolved consequential choices with this exact structure:
text
DECISION NEEDED: <one-line question>
Options: <A>; <B>; ...
Recommendation: <manager's recommendation and why>
Impact: <observable behavior / compatibility / risk>
Blocked: <what cannot proceed>; Continuing: <what can proceed>
  • On completion, DM the coordinator the commit id, tests run, all three panel dispositions, the terminal cold verdict and any public refutations, and residual risks.
Show full SKILL.md (1,256 more words)Show less

4. Seed channels and launch managers

Create the panel channel and dedicated cold record channel before inviting the team, with replay enabled and a short operator note. Then spawn each manager through cotal_spawn:

text
name: mgr-<slug>
role: feature-manager
agent: <connector>
model: <pinned manager model>
cwd: /absolute/path/to/feature-worktree

Launch managers in parallel only after all worktrees, personas, and channels exist. Track the returned reviewer identity, not an assumed name; a restarted or duplicated launch may still be auto-numbered even though the persona filename is lane-scoped.

Verify with cotal_roster that every manager appears and holds its full panel. Do not spawn missing-looking duplicates prematurely; allow startup time and recheck first.

A spawn that reports a timeout is not evidence the spawn failed. It may already have succeeded, and retrying submits a second goal that duplicates the effect. Read the outcome from the process listing before acting, and never retry on a timeout alone.

Verify a seat by REPLY, never by presence. A seat can report as running and be silently unreachable. Ask for a nonce artifact it must produce, such as an echo of a random token joined to the short commit it is sitting on, and check the raw output. A handshake that states the expected answer ("confirm you are at <path> on <sha>") is leading: an echo-compatible reply proves something can mirror text, not that a shell ran.

The independent reviewer is intentionally absent during initial staffing. The feature manager spawns it only at the cold-review gate, and briefs it by applying the cold-review skill itself: that file addresses the briefer, and the graded seat never loads it. It owns that seat's isolation, briefing, and verdict rules. Do not restate them here: one source for the rule, or the two copies drift and the stale one is invisible to whoever is editing the other. Auto-numbering applies to the lane-scoped cold persona too, so track the returned identity.

5. Monitor without taking over

Join each review channel and set it quiet, so channel traffic is available on demand without waking the coordinator. Keep DMs open: decisions and completion reports must wake the coordinator.

Use:

  • cotal_roster for staffing and current activity.
  • cotal_inbox for decisions, findings, and completions.
  • Read-only git status --short --branch and git log --oneline in each worktree for branch state.

Do not redo the manager's implementation, review its diff in parallel, or send acknowledgement noise. Intervene only for infrastructure, violated team policy, a real decision, or a stalled team.

When a manager sends DECISION NEEDED:

  1. Check that existing code/docs/conventions do not already settle it.
  2. Relay the concise options, recommendation, and impact to the user.
  3. Wait for the user's choice; do not choose for them.
  4. DM the decision back verbatim enough to preserve its constraints.
  5. Record any cross-feature consequence and notify other affected managers privately.

Infrastructure blockers are coordinator work, not user decisions. Resolve worktree, submodule, dependency, or mesh-access issues directly when safe.

6. Completion gate

A feature is complete only when:

  • The manager has folded or reasoned against every panel finding. Cold findings are governed only by the dedicated closure rule below; this bullet must not privately override them.
  • Every panel reviewer gives final approval, and no two reviewers whose agreement is load-bearing share a model family. "Spans more than one vendor" is NOT this condition: a panel staffed A, B, A satisfies it and violates the rule, and an A-A pair is the same-family echo the rule exists to reject. Where the vendor set is too short, name the collision mechanically and the failure class it leaves uncovered, per cold-review. A named collision is not a third completion path. If the remaining load-bearing approvals share a family, the feature is not complete. Refuse below that floor rather than recording a same-family panel as reviewed.
  • A terminal cold verdict landed first-hand before finding closure is evaluated: APPROVE or named blockers at the exact sha, at the destination the brief named. A silent, failed, or non-verdict cold lifecycle is not completion. On the broker-attested route, the cold persona has empty subscribe and allowSubscribe, may publish only to its dedicated record channel, and panel personas may not read it. A file is a norm-only fallback and cannot satisfy a gate requiring broker-attested poster identity.
  • After that terminal verdict exists, every cold blocker is closed, by one of exactly two routes and no third: the cold seat posted APPROVE at the exact sha; or each blocker at that sha was answered by a public refutation from a permitted party under cold-review's override rule, naming the same exact sha and left standing in the record beside the finding. An unanswered cold blocker is neither, and is not completion. Requiring the seat's own approval alone would deadlock the override the first time it was used correctly: the seat is one-delivery and may never be told to reconsider, so a refuted blocker could be cleared only by changing code to satisfy a finding just publicly refuted, or by a zero-delta re-pin to manufacture an approval, which is laundering. The override answered the question and the gate has to let the answer count.
  • A verdict relayed by the manager satisfies nothing, and the manager confirms the seat's own post landed by re-fetching the destination. The sha and destination are retrospective evidence. A post on the dedicated Cotal record channel also attests the cold seat's principal. A GitHub comment attests only a GitHub account and is not cold-seat evidence when the workstation credential is shared. Historical non-join is auditable only from retained launch-time ACL evidence; a current subscription snapshot is not proof. For a file destination, poster is a norm: the file's presence does not prove the seat wrote it. That the brief carried no findings is also a norm resting on the briefer, so it is deliberately not a gate condition here; listing a norm in the grammar of a control is the false assurance cold-review exists to prevent.
  • Every approval and public refutation names the exact head it grades or answers. The manager or coordinator re-resolves that head at briefing, at the completion claim, and again at merge, using the instruments in the manager prompt: Git ref and object type for every lane, plus the exact PR API cross-check when a PR exists. Neither a verdict nor a refutation is ever carried across a sha.
  • Required focused and integration tests pass.
  • The feature is committed on its own branch.
  • git status is clean except an explicitly acknowledged local .internal pointer mismatch.
  • The completion DM includes commit, tests, panel approvals, the terminal cold verdict and any public refutations, and residual risk.

Park completed teams until integration is requested. Report progress to the user as a compact matrix: completed commit, in-review findings, implementing, or decision needed.

7. Integrate and clean up only on request

When asked to land the work, inspect all feature commits and expected shared-file conflicts first. Parallel features commonly touch docs/cli.md, generated docs bundles, package.json, flag inventories, and changesets. Merge/cherry-pick deliberately, resolve by preserving both behaviors, then run the aggregate gate once on the integrated result and request a final cross-feature review when conflicts changed code.

After landing and verification:

  1. cotal_despawn the three panel reviewers, independent reviewer, and manager for each feature.
  2. Remove only clean, landed worktrees and their branches according to the user's cleanup request.
  3. Remove lane-scoped reviewer and manager personas only if they are no longer useful.
  4. Restore channel attention/subscriptions if desired.

Never tear down peers before their final result is captured, never remove an unmerged worktree, and do not delete the dedicated cold record channel or its retained verdict history as routine cleanup.

© Cotal-AI, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/parallel-feature-managers of Cotal-AI/Cotal.

Open the folder on GitHubat commit bf3a7b2

Compare with similar skills

Parallel Feature Managers 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.

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Questions about Parallel Feature Managers

What does Parallel Feature Managers do?

Run several independent Cotal features concurrently by creating one Git worktree and one spawn-capable mesh manager per feature; each manager staffs a review panel in a dedicated channel, adds one…. Parallel Feature Managers is an agent skill from Cotal-AI/Cotal. Run several independent Cotal features concurrently by creating one Git worktree and one spawn-capable mesh manager per feature; each manager staffs a review panel in a dedicated channel, adds one independent cold reviewer briefed under the cold-review skill, owns plan-to-commit delivery, and escalates unresolved product decisions by DM to the coordinator for relay to the user.

When should I use Parallel Feature Managers?

Parallel Feature Managers fits situations like: the user asks to split multiple features across managers/worktrees; run parallel feature teams on Cotal; have managers create their own review panels.

How do I install Parallel Feature Managers in Claude Code?

Run `npx skills add Cotal-AI/Cotal --skill parallel-feature-managers -a claude-code`. Or copy the skill folder (.claude/skills/parallel-feature-managers in Cotal-AI/Cotal) into .claude/skills/parallel-feature-managers in your project. Claude Code loads it when a task matches its description.

How do I install Parallel Feature Managers in Codex?

Run `npx skills add Cotal-AI/Cotal --skill parallel-feature-managers -a codex`. Or copy the skill folder (.claude/skills/parallel-feature-managers in Cotal-AI/Cotal) into .agents/skills/parallel-feature-managers in your project. Codex loads it when a task matches its description.

Can I use Parallel Feature Managers 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 Cotal-AI/Cotal --skill parallel-feature-managers -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/parallel-feature-managers, .gemini/skills/parallel-feature-managers, .github/skills/parallel-feature-managers and .opencode/skills/parallel-feature-managers in your project.

What does Parallel Feature Managers need to run?

Going by SKILL.md and its folder, Parallel Feature Managers needs the command-line tools its instructions call (git and gh).

Does Parallel Feature Managers access the network?

SKILL.md contains no URLs. Its commands use git and gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Parallel Feature Managers 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 Parallel Feature Managers use?

Parallel Feature Managers is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Parallel Feature Managers use?

About 5.3k 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.

What are the alternatives to Parallel Feature Managers?

Skills that share tags, products or a category with Parallel Feature Managers: Agtx Task Sweep (fynnfluegge/agtx, 1.7k stars), Devcontainer Dev (stacklok/toolhive-studio, 170 stars), Pm (Apra-Labs/apra-fleet, 101 stars) and Swarm (guaardvark/guaardvark, 257 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Parallel Feature Managers?

Cotal-AI (a GitHub organization) maintains it in Cotal-AI/Cotal, which has 313 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 9, 2026.

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