agentleFS
elfrostelfrost/ai-patchlab/.agents/skills/next/SKILL.md

next

Skill1 starsChanged 30 days agoMITView on GitHub

Read the project state and produce a short ranked recommendation of 1-3 actions. This skill never executes the recommended commands — it only suggests. Produce a short report with two sections:

40 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use quick, project and 8 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • codebycoreySkill

    quick

    Quick start a change with minimal interaction. Use when fast-tracking a straightforward change that needs proposal, design, and tasks in one step.

    Also in 3 projects that use this

  • BugRogerSkill

    project

    Project lifecycle management. Dispatches to Ada.

    Also in 3 projects that use this

  • ArvorCoSkill

    clarify

    Identify and resolve ambiguities in feature specifications. Use when spec has unclear requirements. Triggers on: clarify spec, resolve ambiguities, clarify requirements.

    Also in 3 projects that use this

  • genomewalkerSkill

    explore

    RLM-style recursive memory exploration - dynamically navigate the memory graph

    Also in 3 projects that use this

  • GoogleSkill

    critique

    Expertise in auditing and fixing repository scripts and GitHub Actions workflows to ensure technical robustness and security.

    Also in 3 projects that use this

  • OpenAISkill

    audit

    Audit or critique a product flow, journey, workflow, funnel, onboarding path, checkout path, settings path, screen, or multi-step product experience by capturing screenshots first, then reporting UX, design, and accessibility findings inline from that evidence. Use Figma only when the user explicitly asks for a board. Use when the user asks to audit, review, critique, inspect, assess, analyze, evaluate, or give feedback on a product experience.

    Also in 7 projects that use this

The file

We split this skill into the rules it gives an agent

8 rules. 0 are common, 6 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 36 edited versions we read keep these.

  • 6

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Read the project state and produce a short ranked recommendation of 1-3 actions.

    line 8
  2. Distinctive:

    Read in this order, skipping silently any source that does not exist:

    line 11
  3. Very specific:

    No INITIAL.md, fresh repo, on main → run kickoff to start a new feature interview

    line 27
  4. Distinctive:

    Always print the literal command to run, with exact arguments.

    line 59
  5. Very specific:

    Always remind that execute-prp and retrospective should run in fresh sessions (ADR-013).

    line 61
  6. Distinctive:

    If state is genuinely ambiguous (e.g., 5 PRPs active), ASK the user to pick rather than guess.

    line 62
  7. Distinctive:

    Never fabricate state — if a marker is missing or unreadable, say so and skip that branch.

    line 63
  8. Distinctive:

    Keep output under 30 lines.

    line 64

“Common” is measured: kept by at least 6 in 10 of the 36 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Next-step advisor

Read the project state and produce a short ranked recommendation of 1-3 actions. This skill never executes the recommended commands — it only suggests.

Phase 1: Detect state

Read in this order, skipping silently any source that does not exist:

  1. .ezproject.json for mode and stack
  2. INITIAL.md — exists? when was it last modified? does it still contain placeholders?
  3. PRPs/*.md — active (un-archived) PRPs
  4. PRPs/done/*.md — recently completed (last 30 days)
  5. git status --short — uncommitted files
  6. git rev-parse --abbrev-ref HEAD — current branch
  7. git log --since="2.weeks.ago" --oneline — recent activity
  8. git log -1 --format="%ar — %s" — last commit summary
  9. CLAUDE.md last modification date — drift signal
  10. grep -l "ai-patchlab\|2026-05-12\|\[REMPLIR\]" *.md — unfinished kickoff?

Phase 2: Decision tree (top-down, first match wins)

  1. Placeholders remain in INITIAL/CLAUDE/ROADMAP/README → run kickoff to finish project setup
  2. No INITIAL.md, fresh repo, on main → run kickoff to start a new feature interview
  3. INITIAL.md exists, no PRP for it → run generate-prp INITIAL.md
  4. Active PRP, never executed → run execute-prp PRPs/<name>.md in a NEW conversation (fresh context per ADR-013)
  5. Active PRP with partial progress → resume execute-prp PRPs/<name>.md
  6. PRP moved to PRPs/done/ within last 24h, no retrospective committed since → run retrospective last in a NEW conversation
  7. Uncommitted changes on a feature branch → run review-code before committing
  8. Feature branch ahead of main, tests passing, no active PRP → open a PR or run the release pipeline if available
  9. 3+ weeks since CLAUDE.md modified AND 10+ commits since → run audit-project
  10. 30+ days since pyproject.toml change, never ran a dependency check → run a dependency audit
  11. Many TODO/FIXME comments accumulating → run a cleanup pass
  12. On main, nothing in progress → list top 3 unchecked ROADMAP items and ask the user to pick, then run kickoff

Phase 3: Output

Produce a short report with two sections:

## Where you are
- Mode/Stack: ...
- Branch: ...
- Active PRP: ...
- Uncommitted: ...
- Last commit: ...
- Recent activity: ...

## Recommended next steps
1. [PRIMARY] <command with exact args> — <one-sentence reason tied to detected state>
2. [SECONDARY] <command> — <reason>
3. [LATER] <command> — <reason>

Rules

  • Always print the literal command to run, with exact arguments.
  • Tag each recommendation [PRIMARY] / [SECONDARY] / [LATER].
  • Always remind that execute-prp and retrospective should run in fresh sessions (ADR-013).
  • If state is genuinely ambiguous (e.g., 5 PRPs active), ASK the user to pick rather than guess.
  • Never fabricate state — if a marker is missing or unreadable, say so and skip that branch.
  • Keep output under 30 lines.
  • This skill MUST NOT execute the recommended commands. It only suggests.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.