New: Earn 20% as an AgentAlley affiliate. Share your link and earn on every sale you refer. Join now New: Earn 20% as an affiliate. Join now

Carry large projects through verifiable tasks, parallel work

Cascade

by Parcha-ai · MIT license · ★ 62 on GitHub

Cascade cover

Carry large projects through verifiable tasks, parallel work, and durable progress. Use when asked for Cascade, work in loops, or sustained project execution.

---
name: cascade
description: Carry large projects through verifiable tasks, parallel work, and durable progress. Use when asked for Cascade, work in loops, or sustained project execution. Skip small edits.
---

# Cascade

Keep one living plan in `.cascade/<project>.md` or the existing project plan:

- Outcome and final acceptance check.
- Context needed to resume: workspace, constraints, decisions, source links.
- Tasks: ID, deliverable, dependencies, exit check, status, evidence.
- Current owners, blockers, and next actions.

**Decompose.** Split by verifiable outcomes or subsystems; detail only near-term work. Expand large
tasks into children while retaining the parent's acceptance check. Convert chains into dependency
graphs by keeping actual prerequisites and removing ordering that serves no dependency.

Overview

Keep one living plan in .cascade/<project>.md or the existing project plan:

Manual install (npx, ZIP)
Claude Codenpx skills add Parcha-ai/parcha-skills --skill cascade -g -a claude-code
Claude Code (degit)npx degit Parcha-ai/parcha-skills/cascade/skills/cascade#main ~/.claude/skills/cascade
Claude.ai / desktopZip the folder and upload it under Settings → Capabilities → Skills.
Codex, Cursor, other agentsPoint them at SKILL.md. The other files are plain text.

Connects with Agent Alley MCP · agentalley.io/mcp

Files included

  • SKILL.md2.1 KB
Read SKILL.md here
Parcha-ai/main1 file shown
SKILL.md
Show the full text37 lines
cascade/SKILL.md37 lines · 2.1 KB
RawView on GitHub

Cascade

Keep one living plan in .cascade/<project>.md or the existing project plan:

  • Outcome and final acceptance check.
  • Context needed to resume: workspace, constraints, decisions, source links.
  • Tasks: ID, deliverable, dependencies, exit check, status, evidence.
  • Current owners, blockers, and next actions.

Decompose. Split by verifiable outcomes or subsystems; detail only near-term work. Expand large tasks into children while retaining the parent's acceptance check. Convert chains into dependency graphs by keeping actual prerequisites and removing ordering that serves no dependency.

Execute. Pick tasks whose prerequisites passed. Build, verify, update the plan, repeat while authorized work remains. Use todo, doing, done, blocked. Failed checks remain unfinished; change approach when attempts stop producing information. Respect session limits and continue independent work when a task is blocked.

Parallelize. Use available subagents for independent ready tasks. Give each worker context, owned files or worktree, deliverable, and exit check. Serialize overlapping edits. Workers return changes, evidence, and unresolved issues; the lead owns integration and the plan. Mirror the graph into native tasks when useful; otherwise the file suffices.

Verify. Mark done only when the exit check passes on the actual candidate. Record check, result, and revision or working-tree identity. Test relevant failure cases; establish a baseline for measured improvements. Never weaken acceptance to hide failure. Verify the assembled system against the original outcome after its required tasks pass.

Resume. Save progress at meaningful boundaries and before handoff. Compare the plan with actual files and workers; recheck stale evidence. Continue until the overall acceptance passes or no authorized work can advance. Leave concrete blockers and next actions when stopping.

1---
2name: cascade
3description: Carry large projects through verifiable tasks, parallel work, and durable progress. Use when asked for Cascade, work in loops, or sustained project execution. Skip small edits.
4---
5 
6# Cascade
7 
8Keep one living plan in `.cascade/<project>.md` or the existing project plan:
9 
10- Outcome and final acceptance check.
11- Context needed to resume: workspace, constraints, decisions, source links.
12- Tasks: ID, deliverable, dependencies, exit check, status, evidence.
13- Current owners, blockers, and next actions.
14 
15**Decompose.** Split by verifiable outcomes or subsystems; detail only near-term work. Expand large
16tasks into children while retaining the parent's acceptance check. Convert chains into dependency
17graphs by keeping actual prerequisites and removing ordering that serves no dependency.
18 
19**Execute.** Pick tasks whose prerequisites passed. Build, verify, update the plan, repeat while
20authorized work remains. Use `todo`, `doing`, `done`, `blocked`. Failed checks remain unfinished;
21change approach when attempts stop producing information. Respect session limits and continue
22independent work when a task is blocked.
23 
24**Parallelize.** Use available subagents for independent ready tasks. Give each worker context,
25owned files or worktree, deliverable, and exit check. Serialize overlapping edits. Workers return
26changes, evidence, and unresolved issues; the lead owns integration and the plan. Mirror the graph
27into native tasks when useful; otherwise the file suffices.
28 
29**Verify.** Mark done only when the exit check passes on the actual candidate. Record check, result,
30and revision or working-tree identity. Test relevant failure cases; establish a baseline for measured
31improvements. Never weaken acceptance to hide failure. Verify the assembled system against the
32original outcome after its required tasks pass.
33 
34**Resume.** Save progress at meaningful boundaries and before handoff. Compare the plan with actual
35files and workers; recheck stale evidence. Continue until the overall acceptance passes or no
36authorized work can advance. Leave concrete blockers and next actions when stopping.
37 
View all files on GitHub →

Details

License
MIT
Stars on the repo
★ 62
Checked
Source
Parcha-ai/parcha-skills ↗
Creator
Parcha-ai · 8 listings
View creator

Similar skills

See all →
SkillReverse engineer native, managed, Electron/JavaScriptREAmorluto/rea · by morlutoReverse engineer native, managed, Electron/JavaScript, packaged, firmware, and browser targets with REA. Use shipped-artifact or requested runtime evidence to explain features, compare versions, decompile code, or guide a reconstruction. Skip REA for ordinary source-repository architecture analysis.★ 316k GitHub starsSkillBrainstorms ideas into designs before any creative workBrainstorming Ideas Into Designsobra/superpowers · by obraYou MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.★ 290k GitHub starsSkillRequires technical rigor and verificationCode Review Receptionobra/superpowers · by obraUse when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation★ 290k GitHub starsSkillDiagnosis loop for hard bugs and performance regressionsDiagnosing bugsmattpocock/skills · by mattpocockDiagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.★ 267k GitHub starsSkillImplement the result of /to-spec and /to-tickets in codeImplement specmattpocock/skills · by mattpocockImplement the result of /to-spec and /to-tickets in code.★ 267k GitHub starsSkillWriting a PR bodymattpocock/skills · by mattpocockUse when writing a PR body.★ 267k GitHub stars

More from Parcha-ai

SkillFind prior Claude Code and Codex sessions with an indexed/recall: session memory engineParcha-ai/parcha-skills · by Parcha-aiFind prior Claude Code and Codex sessions with an indexed local search engine, then continue the work, repeat it with fresh inputs, or distill it into a skill. Runs from Claude Code, Codex, or pi; the current index covers Claude Code and Codex transcripts. Use when the user names Recall, says "find that conversation where…", "what did we do last time about…", "continue what we did yesterday on X", "what did codex do on this branch", "turn what we did about Y into a skill", or "remember when you…". Not for searching code (use grep on the repo) or for facts already in MEMORY.md.★ 62 GitHub starsSkillSelf-QA a repo's running application end-to-endautoqa — QA any repo against its own running appParcha-ai/parcha-skills · by Parcha-aiSelf-QA a repo's running application end-to-end — discover how it runs, build a feature×modality test plan, execute it against a live instance, and produce a witnessed pass/fail report. Use when the user says "autoqa", "QA this repo/branch", "verify every feature works", "release readiness check", "test this like a QA engineer would", or another skill needs an automated QA pass before sign-off.★ 62 GitHub starsSkillFind what a change could break somewhere else beforeBlast radiusParcha-ai/parcha-skills · by Parcha-aiFind what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', or reviewing a small diff you don't trust.★ 62 GitHub starsSkillInstall, onboard, and run multi-model coding orchestrationparable — divide and conquerParcha-ai/parcha-skills · by Parcha-aiInstall, onboard, and run multi-model coding orchestration or solo single-model mode. Invoke when the user says install Parable, install parable.sh, set up or onboard Parable, connect coding subscriptions, names Parable, hands over a backlog, or asks to route work across models. Solo mode (`parable --solo <model>`) runs with a single model. For first-time install intent, collect subscription choices with the harness's native structured-question tool and then run the bundled parable.sh. Not for a single isolated bugfix or standalone code review.★ 62 GitHub starsSkillDrive every reviewer thread on a GitHub pull request to zeroReview loopParcha-ai/parcha-skills · by Parcha-aiDrive every reviewer thread on a GitHub pull request to zero unresolved in bounded iterations. Covers bot reviewers such as Greptile and Devin and human reviewers. Fetches every inline comment, review, and issue comment, fixes what is actionable, replies, resolves bot threads through GraphQL, re-requests review once per push, and ends with a fixed-format report. Use when the user says "review loop", "address the review comments", "get this PR to zero unresolved", "greploop", or "make Greptile happy".★ 62 GitHub starsSkillTest-Driven DevelopmentParcha-ai/parcha-skills · by Parcha-aiTest-driven development. Use when the user wants to build features or fix bugs test-first, mentions "red-green-refactor", or wants integration tests.★ 62 GitHub stars