Agent skill

PR Cycle

by jaemk in jaemk/cached

PR review-and-update cycle — the orchestrator that takes a PR from review to resolved.

MITAuto-check: notesDevelopment

Install PR Cycle

skills CLI
$ npx skills add jaemk/cached --skill pr-cycle -a claude-code

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

GitHub CLI
$ gh skill install jaemk/cached pr-cycle --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/jaemk/cached.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/pr-cycle .claude/skills/pr-cycle && 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
pr-cycle
GitHub stars
2.1k
Token cost
~4.8k tokens
SKILL.md length
2,459 words
Files
2
Skills in repo
4
Repo updated
First seen
Licence
MIT

At a glance

PR review-and-update cycle — the orchestrator that takes a PR from review to resolved.

  • Works in 11 steps: Fetch ALL open comments and unresolved… → Run the local review (delegate to… → Evaluate all findings → …
  • Asked to run the pr cycle
  • SKILL.md covers Modes, Model tiers, Input and Steps
  • Runs Python scripts from its folder; calls git, gh and cargo

What it does

PR Cycle is an agent skill from jaemk/cached. PR review-and-update cycle — the orchestrator that takes a PR from review to resolved. It runs a local review (by delegating to the pr-review skill), fetches open GitHub review comments, evaluates all findings, applies valid fixes, runs CI, commits, pushes, resolves threads, and re-requests Copilot review. Supports three modes — full (default, everything), local (only the local-review-and-fix loop; no GitHub PR-conversation reads or mutations), and remote (only the GitHub PR feedback loop; no local review). The…

Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `pr.py`).

It sits in Development, covering Pull requests and Subagents. It works with GitHub and Rust. The repository describes itself as: Rust cache structures and easy function memoization. The licence is MIT.

When your agent uses it

  • Asked to run the pr cycle
  • Address pr comments
  • Resolve comments and re-request review
  • Review and fix the branch

Example prompts

  • “run the pr cycle”
  • “address pr comments”
  • “resolve comments and re-request review”
  • “/pr-cycle”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Bash, Read, Edit, Write, Agent

Workflow steps

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

  1. Fetch ALL open comments and unresolved threads
  2. Run the local review (delegate to pr-review)
  3. Evaluate all findings
  4. Apply fixes for valid findings
  5. Run CI
  6. Regenerate README if src/lib.rs changed
  7. Sync audit — docs, commit message, and PR summary
  8. Create a new commit and push
  9. Resolve all open threads and re-request Copilot review
  10. Minimize all comments from before this cycle
  11. Report

What it can do on your machine

Read from SKILL.md and the folder at commit 9da0ec2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Edit
    • Write
    • Agent

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • git
    • gh
    • cargo
    • make

    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

PR Cycle loads about 4.8k tokens when it runs. Until then it costs about 221 tokens; SKILL.md has 2,459 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~221
When it runs · the whole SKILL.md, loaded when a task matches
~4.8k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Edit, Write, Agent

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 jaemk/cached at commit 9da0ec2, republished under its MIT licence (© jaemk). 2,459 words, ~4,757 tokens.

Download SKILL.mdSave it as .claude/skills/pr-cycle/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
pr-cycle
description
PR review-and-update cycle — the orchestrator that takes a PR from review to resolved. It runs a local review (by delegating to the `pr-review` skill), fetches open GitHub review comments, evaluates all findings, applies valid fixes, runs CI, commits, pushes, resolves threads, and re-requests Copilot review. Supports three modes — `full` (default, everything), `local` (only the local-review-and-fix loop; no GitHub PR-conversation reads or mutations), and `remote` (only the GitHub PR feedback loop; no local review). The review sub-agents default to Sonnet but can be overridden per run (e.g. to opus). Use when asked to "run the pr cycle", "address pr comments", "resolve comments and re-request review", "review and fix the branch", "run the remote pr cycle", or after pushing a new round of changes. For a read-only review with no fixes/push, use `pr-review` instead.
allowed-tools
Bash, Read, Edit, Write, Agent

PR Cycle

Run one full iteration of the review → fix → push → re-request loop. This is the orchestrator: it produces findings, addresses them with fixes, and updates the PR (commit, push, resolve threads, re-request review).

The review half is owned by the separate pr-review skill, which spawns the two read-only review sub-agents and reports their findings. pr-cycle does not duplicate that logic — step 2 below delegates to pr-review. Reach for pr-review directly when you only want a read-only review (no fixes, no push, no GitHub-conversation changes); reach for pr-cycle when you want those findings actually addressed and the PR updated.

The helper script is .agents/skills/pr-cycle/pr.py. Run it outside the sandbox (GitHub API requires network). Multiple commands can be passed in one call so only one permission prompt is needed:

bash
.agents/skills/pr-cycle/pr.py [PR_NUMBER] COMMAND [COMMAND ...]

The leading PR_NUMBER is optional — it is required only for the GitHub commands (comments, threads, resolve, rerequest, minimize, codspeed). The local commands (ci, readme, pushpreview, diff) take no PR number, so e.g. pr.py ci works directly (no placeholder needed).

Available commands: comments, threads, resolve, rerequest, minimize, codspeed, ci, readme, pushpreview, diff.

Modes

This skill runs in one of three modes. The mode is taken from the input (see Input):

  • full (default) — the complete review → fix → push → resolve → re-request loop. Runs every step below.
  • local — only the local review and fix loop. Runs the pr-review skill (the local code-review and consumer sub-agents), evaluates their findings, applies fixes, runs CI, regenerates the README, and commits/pushes. Does not interact with the PR conversation on GitHub: it does not read PR comments or threads, does not edit the PR body, and does not resolve, minimize/hide, or re-request Copilot review.
  • remote — only the GitHub PR feedback loop. Fetches and evaluates open PR comments/threads, applies fixes, runs CI, regenerates the README, commits/pushes, then resolves threads, minimizes prior comments, re-requests Copilot review, and audits the PR body. Does not run the local review (pr-review).

Both local and remote still commit and push the fixes they make (a code change has to land to be useful), and both follow the push protocol (show the push preamble before pushing). "Does not interact with GitHub" for local means the PR-conversation operations — comment/thread reads and mutations, Copilot re-request, PR-body edits — not the git push itself.

Step-by-step applicability (✓ = runs in that mode):

StepWhatfulllocalremote
1Fetch open comments + threads✓✓
2Run local review (pr-review)✓✓
3Evaluate findings✓✓ (agents only)✓ (PR comments only)
4Apply fixes✓✓✓
5Run CI✓✓✓
6Regenerate README✓✓✓
7Sync audit (CHANGELOG / commit msg)✓✓✓
7cSync audit — PR body edit✓✓
8Commit + push✓✓✓
9Resolve threads + re-request Copilot✓✓
10Minimize prior comments✓✓
11Report✓✓✓

When a step is not applicable to the active mode, skip it entirely — do not run its pr.py command or gh call. In local mode you must not invoke any of comments, threads, resolve, rerequest, minimize, or gh pr edit/gh pr view --json body.

Model tiers

This skill is designed to keep expensive Opus reasoning concentrated in the judgment core and push everything else to cheaper models or to no model at all.

TierWhatStepsModel
0 — mechanicalAll GitHub API ops, make ci, README regen, push preamble1, 5, 6, 8(preamble), 9, 10script (pr.py)
1 — cheap delegationLocal review via pr-review (read-only sub-agents); fix application, fanned out across disjoint sub-agents2, 4b, 11Sonnet by default; per-group model override to Opus for harder groups (see 4b); review sub-agents overridable per-run (see Input)
2 — judgment coreClassify findings; write explicit fix specs + test assertions; partition the fan-out; sync audit3, 4a, 7Opus (session model)

A Sonnet session can drive the whole cycle; only Tier-2 actually needs strong reasoning, so consider switching the session to a cheaper model once the judgment core is done.

Input

Optional PR number, an optional mode keyword (full, local, or remote), and an optional review-agent model override, in any order.

  • Mode: if one of local / remote is present in the input, use it; otherwise default to full. Phrasings map as: "local review" / "just the local reviewers" / "local pr cycle" → local; "remote" / "address the pr comments" / "resolve and re-request" / "remote pr cycle" → remote; anything else (or "run the pr cycle") → full.
  • Review-agent model: the model used by the local review sub-agents (pr-code-reviewer, pr-consumer-reviewer) defaults to sonnet, but can be overridden. If the input names a model (e.g. "use opus for the reviewers", "review with opus", "opus reviewers", "model=opus"), pass that model through to the pr-review delegation in step 2 (which forwards it to the Agent tool's model parameter on both sub-agent spawns). Only the review sub-agents are affected — this does not change the session model or the model used by pr-fix-implementer. This override is only meaningful in modes that run the local review (full, local); ignore it in remote mode.
  • PR number: if omitted, infer it from the current branch using gh pr view --json number. In local mode no PR number is needed at all — the pr.py wrapper commands used there (ci, readme, pushpreview, diff) take no PR argument, so call them directly (e.g. pr.py ci). This also means local mode works without gh installed.

Announce the resolved mode (and, when the reviewers will run, the review-agent model) at the start — e.g. "Running pr-cycle in local mode with opus reviewers" — before executing any step.

Steps

1. Fetch ALL open comments and unresolved threads

Modes: full, remote. Skip entirely in local mode.

Run outside the sandbox:

bash
.agents/skills/pr-cycle/pr.py PR_NUMBER comments threads

comments fetches all inline review comments, separates them into new (post-last-push) vs pre-existing unresolved, and prints each with its ID, created_at, file:line, author, and body. Record every comment for evaluation in step 3 — do not filter by timestamp.

threads lists all review thread node IDs with their resolution status. Unresolved threads will all be resolved in step 10.

2. Run the local review (delegate to pr-review)

Modes: full, local. Skip entirely in remote mode (no local review runs).

The review itself lives in the pr-review skill — do not re-implement it here. Run that skill against this PR/branch to obtain the local findings, passing through the review-agent model override from the Input if one was given. pr-review will:

  • acquire the diff (.agents/skills/pr-cycle/pr.py PR_NUMBER diff, equivalent to git diff origin/master),
  • shard the changed material into appropriately sized, randomized chunks and spawn one pr-code-reviewer and one pr-consumer-reviewer per shard in parallel (read-only, each carrying its own rubric; Sonnet by default, or the overridden model on all spawns), and
  • return a consolidated findings report with severity and a per-finding verdict.

Carry pr-review's findings forward into step 3, where they are evaluated alongside any GitHub PR comments (in full mode). Do not act on fixes inside pr-review — it is read-only; addressing findings is step 4 here.

3. Evaluate all findings

Modes: all. The set of findings depends on the mode:

  • full — all open inline PR comments (from step 1) plus both sub-agent reports (from step 2).
  • local — only the two sub-agent reports. Do not reference PR comments.
  • remote — only the open inline PR comments. There are no sub-agent reports.

Present all in-scope findings together. For each finding:

  • Valid: the concern is real and the code should change
  • Already fixed: the concern was valid but the code has already been corrected (the comment is stale) — mark for resolution only
  • Invalid: the finding is incorrect or environment-specific (e.g. rustc version mismatch on trybuild golden files)

Explain your reasoning for each verdict. Do not apply any fix silently — call out what you are doing and why.

4. Apply fixes for valid findings

Modes: all.

4a. Write a fix spec for each valid finding (Opus / judgment core)

For each valid finding, produce an explicit fix spec before touching any file:

Finding: <one-line summary>
Target:  <file path>
Location: <file:line or unique surrounding snippet>
Change:  <exact new text, or precise add/remove/replace description — never "improve the wording">
Test:    <exact function name + exact assertions that would fail on unfixed code>
         OR "no test needed (doc-only)"

The spec must be precise enough for Sonnet to apply without judgment calls. Test design always stays here — this repo requires tests that fail on the unfixed code and pass on the fixed code; a trivially-passing test defeats the rule.

Common fix types:

  • Documentation/comment updates: src/stores/, src/lib.rs, cached_proc_macro/src/lib.rs
  • Test additions/corrections: tests/cached.rs
  • Trybuild golden file regeneration: TRYBUILD=overwrite cargo test --no-default-features --features "proc_macro,time_stores" compile_fail_macro_arg_validation
  • Macro code changes: cached_proc_macro/src/
Show full SKILL.md (1,124 more words)Show less
4b. Partition the fixes and fan out across disjoint sub-agents

Once every valid finding has a spec, apply them by fanning out across as many parallel sub-agents as the specs allow, rather than applying them serially in the orchestrator. Two rules govern the fan-out:

Disjoint partitioning (correctness). Parallel agents share one working tree, so two agents must never write the same file — concurrent edits to one file race and corrupt each other. Partition the specs into groups whose written-file sets do not overlap:

  • For each spec, compute the full set of files it writes — the Target file(s) and the test file its Test clause adds to (often tests/cached.rs).
  • Any two specs that share a written file MUST land in the same group. A common sink like tests/cached.rs therefore pulls every test-adding spec into one group — that is expected; keep that group together rather than risking a race.
  • Otherwise split into as many groups as possible — ideally one spec per group — to maximize parallelism. More disjoint groups means more concurrency.

Appropriate model per group (cost). Each group is handled by a pr-fix-implementer agent spawned with the Agent tool's model parameter set to the tier the group's hardest fix needs:

  • model: sonnet (the agent's default) — mechanical or repetitive groups: doc/comment updates, a pattern replicated across the sharded stores, golden-file regen, simple test additions.
  • model: opus — groups containing a subtle logic change, a macro change in cached_proc_macro/src/, or any fix whose application still needs real reasoning. The spec from 4a is already precise enough to hand off (it must be, to be valid); raising the implementer's model buys more careful application, not more decision latitude.

Spawn all groups in a single message (multiple Agent calls) so they run concurrently. Each agent's prompt is the verbatim fix spec(s) for its group. Before spawning, state the partition: list each group, the files it owns, its model, and why that model.

Inline fallback. Skip the fan-out and edit directly only in the degenerate case where it cannot pay off: a single spec, or a few tiny one-off edits that all touch one overlapping region (so they cannot be partitioned anyway). State "applying inline because: <reason>".

After all agents report back, verify with:

bash
.agents/skills/pr-cycle/pr.py PR_NUMBER ci

Then spot-check: confirm that at least one of the newly added tests would fail if its corresponding fix were reverted. (Read the test and reason through it; you do not have to literally revert the fix.)

5. Run CI

Modes: all. Run outside the sandbox:

bash
.agents/skills/pr-cycle/pr.py PR_NUMBER ci

This runs make ci, filters Redis/Docker noise, and exits non-zero only on real failures. If it exits non-zero, fix the reported failures and re-run.

If trybuild golden files drift, regenerate them:

bash
TRYBUILD=overwrite cargo test --no-default-features --features "proc_macro,time_stores" compile_fail_macro_arg_validation
6. Regenerate README if src/lib.rs changed

Modes: all. Run outside the sandbox:

bash
.agents/skills/pr-cycle/pr.py PR_NUMBER readme

No-ops automatically when src/lib.rs is unchanged vs origin/master.

7. Sync audit — docs, commit message, and PR summary

Modes: all for parts (a), (b), (d). Part (c) — PR-body edit — runs only in full and remote; skip it in local (it reads and mutates the PR on GitHub).

Before staging anything, do a consistency check. The goal: every artifact that describes "what this PR does" must match the actual diff.

a. Diff summary — produce a concise internal summary of what the branch actually changes:

bash
git diff origin/master --stat
git diff origin/master -- CHANGELOG.md

b. CHANGELOG.md — read the [Unreleased] section. For each bullet:

  • Does it describe something that is actually in git diff origin/master? If a bullet refers to a feature or behavior that was removed, reverted, or renamed, update or remove it.
  • Is anything significant in the diff that is NOT mentioned? Add it.
  • Check accuracy of any named types, method signatures, attribute names, or feature gates — they must exactly match the code.

c. PR description (full / remote only — skip in local) — read the current PR body:

bash
gh pr view PR_NUMBER --json body

Apply the same audit: every claim must match the diff. Pay special attention to:

  • Named types or methods that were renamed or removed
  • Feature/behavior claims that no longer apply (e.g. "replaces AtomicU64 across all stores" when only two stores were changed)
  • Test counts that are now stale

Update the PR body if anything is inaccurate:

bash
gh pr edit PR_NUMBER --body "..."

d. Commit message — draft a concise new commit message for the fixes made in this cycle. The message should describe only the newly applied changes, not the entire PR.

Do not make the sync audit a "wall of changes" — only fix what is actually wrong.

8. Create a new commit and push

Modes: all (only if fixes were applied this pass). Applies to local and remote too — fixes have to land to be useful.

bash
git add -p   # stage only changed files explicitly
git commit -m "fix: address PR review feedback"

Before pushing, run outside the sandbox to show the push preamble:

bash
.agents/skills/pr-cycle/pr.py PR_NUMBER pushpreview

Then add a one-sentence summary of what the push contains (e.g. "Pushing 1 commit: doc fix for option+expires constraint and CHANGELOG update"). Then push:

bash
git push origin BRANCH

Create a new commit for every PR-cycle pass that changes files. Do not amend previous commits and do not force push unless the user explicitly requests history rewriting.

Do not add a Co-Authored-By line.

9. Resolve all open threads and re-request Copilot review

Modes: full, remote. Skip entirely in local mode (no PR-conversation mutations, no Copilot re-request).

After the push, run outside the sandbox — combining both steps in one call:

bash
.agents/skills/pr-cycle/pr.py PR_NUMBER resolve rerequest

resolve re-fetches all unresolved threads and resolves each one via GraphQL mutation. Goal: zero open threads after this step.

rerequest triggers a fresh Copilot review on the PR.

10. Minimize all comments from before this cycle

Modes: full, remote. Skip entirely in local mode.

After threads are resolved, hide all inline review comments and top-level PR comments so the PR conversation is clean. Run outside the sandbox:

bash
.agents/skills/pr-cycle/pr.py PR_NUMBER minimize

This fetches all inline review comments and top-level PR comments from all authors and calls the GitHub minimizeComment GraphQL mutation with classifier RESOLVED on each one. Only comments created before the last push timestamp are minimized — comments posted after the push (i.e., responses to the new round of changes) are left visible. Use --dry-run first to preview which comments would be minimized.

11. Report

Modes: all. State the mode that ran, and report only the lines relevant to it.

  • The mode that ran (full / local / remote).
  • (full / remote) How many inline PR comments were found total; how many were new (post-last-push) vs. pre-existing unresolved; how many were valid/already-fixed/invalid; how many were fixed this cycle.
  • (full / local) How many code-reviewer findings were found, how many were valid, how many were fixed.
  • (full / local) How many consumer-reviewer findings were found, how many were valid, how many were fixed.
  • Which findings were ruled invalid and why.
  • Sync audit result: what was corrected in CHANGELOG, the new commit message, and — in full / remote — the PR description (or "all in sync").
  • (full / remote) Confirm threads resolved (state total resolved count) and Copilot re-requested.
  • The resulting new commit SHA and push status (or "no changes to commit this pass").

© jaemk, 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 1 other file in .agents/skills/pr-cycle of jaemk/cached.

  • SKILL.md
  • pr.py

Open the folder on GitHubat commit 9da0ec2

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in jaemk/cached, which our catalogue first saw on October 7, 2026.

Compare with similar skills

PR Cycle 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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Cherry Studio PR ReviewCherryHQ/cherry-studio52k—~3.9kAutomated safety check: PassAGPL-3.0
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Works with

Questions about PR Cycle

What does PR Cycle do?

PR review-and-update cycle — the orchestrator that takes a PR from review to resolved. PR Cycle is an agent skill from jaemk/cached. PR review-and-update cycle — the orchestrator that takes a PR from review to resolved.

When should I use PR Cycle?

PR Cycle fits situations like: asked to run the pr cycle; address pr comments; resolve comments and re-request review; review and fix the branch.

How do I install PR Cycle in Claude Code?

Run `npx skills add jaemk/cached --skill pr-cycle -a claude-code`. Or copy the skill folder (.agents/skills/pr-cycle in jaemk/cached) into .claude/skills/pr-cycle in your project. Claude Code loads it when a task matches its description.

How do I install PR Cycle in Codex?

Run `npx skills add jaemk/cached --skill pr-cycle -a codex`. Or copy the skill folder (.agents/skills/pr-cycle in jaemk/cached) into .agents/skills/pr-cycle in your project. Codex loads it when a task matches its description.

Can I use PR Cycle 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 jaemk/cached --skill pr-cycle -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pr-cycle, .gemini/skills/pr-cycle, .github/skills/pr-cycle and .opencode/skills/pr-cycle in your project.

What does PR Cycle need to run?

Going by SKILL.md and its folder, PR Cycle needs Python for the scripts in its folder and the command-line tools its instructions call (git, gh, cargo and make). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash, Read, Edit, Write, Agent.

Does PR Cycle 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 PR Cycle safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does PR Cycle use?

PR Cycle 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 PR Cycle use?

About 4.8k tokens (SKILL.md is roughly 19k 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 PR Cycle?

Skills that share tags, products or a category with PR Cycle: PR Review (jaemk/self_update, 961 stars), Firewood Review (ava-labs/firewood, 153 stars), GitHub Review Iteration (prisma/orm, 48k stars) and Cherry Studio PR Review (CherryHQ/cherry-studio, 52k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains PR Cycle?

jaemk (a GitHub user) maintains it in jaemk/cached, which has 2,098 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on October 1, 2026.

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