agentleFS
Kashif-RezwiKashif-Rezwi/ai-dev-workflow/SKILL.md

ai-dev-workflow

Skill1 starsChanged 3 months agoMITView on GitHub

Bootstrap or audit a pragmatic AI-assisted development workflow in any software repository: generates AGENTS.md, a two-gear development workflow, spec and ADR templates, a verification contract, and thin harness adapters. Use when the user asks to initialize, set up, inspect, audit, or improve a repo's AI/agent development workflow, AGENTS.md, or AI instruction files, or to scaffold a feature spec with this workflow. Subcommands: init, inspect, doctor, spec, verify. Not for everyday coding tasks, bug fixes, or debugging failing tests, builds, or CI.

2 other projects use a version of this.

Adopted by

Most recently taken up by

  • Other versions2 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 2

    1. andremoreira73/Useful-Skills-from-lyfX★ 8same name
    2. mdrmuhaimin/agentic-skills★ 3same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

The file

We split this skill into the rules it gives an agent

11 rules. About 0 are common, 4 are distinctive, and 7 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 7

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If the repo already has a good AGENTS.md and coherent AI setup: report "already AI-ready", list any doctor-style observations, and stop.

    line 45
  2. Very specific:

    Never generate: duplicated .cursor/rules, nested AGENTS.md in small repos, .ai//.agent/ directory trees, status/evidence docs, or tooling …

    line 65
  3. Very specific:

    If a verify command exists or was created, run it.

    line 70
  4. Very specific:

    Run inspect, then check for problems:

    line 83
  5. Very specific:

    If docs/specs/ has no template, copy templates/docs/specs/feature-spec.md.tmpl there.

    line 95
  6. Very specific:

    Only now create docs/specs/<slug>.md from the template, filled with the approved results — every {{SLOT}} resolved, no half-filled file on …

    line 96
  7. Distinctive:

    Run what exists, in this order, stopping at the first failure: format check → lint → typecheck → unit tests → integration tests → build → …

    line 102
  8. Very specific:

    Never overwrite human-owned files (see references/merge-rules.md).

    line 111
  9. Distinctive:

    Never install dependencies, add tooling, or change CI without explicit approval.

    line 112
  10. Distinctive:

    Never commit or push without explicit approval.

    line 113
  11. Distinctive:

    If the repo has its own AGENTS.md rules, they outrank this skill's defaults.

    line 115

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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

AI Development Workflow

Bootstrap a pragmatic, harness-agnostic AI development workflow into any repository — or audit an existing one. The goal: maximum practical leverage with minimum generated structure.

Core principles (apply to every command)

  1. Preserve, never fight. Existing tooling, docs, scripts, CI, and AI configuration are the user's prior intent. Detect → analyze → preserve → extend → warn → ask. Never blindly overwrite.
  2. Single source of truth. AGENTS.md is the canonical agent-instruction file. Harness-specific files are one-line pointers, never duplicated content.
  3. Deterministic verification is the definition of done. An agent never declares work "done" on reasoning alone — the repo's checks must be green.
  4. Two gears, not bureaucracy. Small changes use the quick loop; features/architecture use the full loop. See references/workflow.md.
  5. Minimum files. Generate only what will be read. No empty directories, no placeholder docs, no process for its own sake.
  6. Idempotent. Re-running init on an initialized repo reports "already AI-ready" and changes nothing.

Commands

The user's request determines the command. When ambiguous, ask.

CommandTrigger examplesWhat it does
init"initialize", "set up", "bootstrap", "make this repo AI-ready"Inspect → plan → generate baseline → validate → report
inspect"inspect", "analyze", "what's the workflow here"Read-only report of stack, tooling, AI config, gaps
doctor"doctor", "check", "audit", "what's missing/stale"Detect missing/stale/contradictory config; propose fixes, change nothing
spec <feature>"create a spec for X", "spec out X"Scaffold docs/specs/<feature>.md from template
verify"verify", "run the checks"Discover and run the repo's deterministic checks in order

Procedure: init

Work through these steps in order. Load reference files only when you reach the step that needs them.

1. INSPECT

Load references/detection.md and follow it to detect: git state, languages, frameworks, package manager, test/build/lint/format tooling, CI, Docker, existing docs, existing AI configuration (AGENTS.md, CLAUDE.md, GEMINI.md, .cursor/, .github/, .codex/, .gemini/).

Present a concise detection report to the user.

2. PLAN

Decide the action for every candidate file using references/merge-rules.md (load it now). The matrix is: CREATE (absent) / REFRESH (skill-managed, human-unmodified) / PRESERVE + report (human-owned) / WARN (contradicts detected reality).

If the repo already has a good AGENTS.md and coherent AI setup: report "already AI-ready", list any doctor-style observations, and stop. That is a successful run.

Present the plan as a table (file → action → reason) and ask for confirmation before writing anything, unless the user already approved generation.

3. GENERATE

Load references/instructions-guide.md and the relevant templates from templates/. Fill every {{SLOT}} with detected facts — never leave generic filler.

Mandatory baseline (create if absent):

  • AGENTS.md — from templates/AGENTS.md.tmpl, filled with detected stack/commands
  • docs/development.md — from templates/docs/development.md.tmpl (the two-gear workflow + verification contract, so the repo works without this skill installed)
  • docs/architecture.md — from templates/docs/architecture.md.tmpl, seeded with detected structure

Conditional:

  • docs/specs/feature-spec.md — always create the template file so specs are one step away
  • docs/decisions/ — do not create at init; on the first real decision, create docs/decisions/NNNN-<slug>.md from templates/docs/decisions/adr.md.tmpl
  • CI workflow — from templates/ci/github-actions.yml.tmpl, only if no CI exists and a GitHub remote is present; otherwise note it in the report
  • Harness adapters — load references/harness-adapters.md; generate thin pointers only for harnesses the user confirms they use or that are already present in the repo
  • verify script — if a package manager exists, propose composing existing scripts into one verify script and ask before editing the manifest. For non-JS repos, document the equivalent command sequence in docs/development.md instead.

Never generate: duplicated .cursor/rules, nested AGENTS.md in small repos, .ai//.agent/ directory trees, status/evidence docs, or tooling the repo doesn't have (recommend it in the report instead).

4. VALIDATE

  • Re-read every file you created: all {{SLOTS}} filled, no contradictions with detection.
  • If a verify command exists or was created, run it. Report the result honestly — if it fails on pre-existing issues, say so and do not fix unrelated failures without asking.
  • git status --short — show exactly what changed.

5. REPORT

Close with: what was detected, what was created/preserved/warned, what needs the user's attention, and the suggested next step (usually: commit, then start the first feature with spec).

Procedure: inspect

Load references/detection.md, run detection, and present the report. Also list: existing AI config files found, doc coverage, and which init actions would apply. Write nothing.

Procedure: doctor

Run inspect, then check for problems:

  • AGENTS.md contradictions (wrong commands, wrong package manager, dead file paths)
  • missing verification path (no way to run checks in one step)
  • duplicated/conflicting instruction files (CLAUDE.md copy-pasted from AGENTS.md, etc.)
  • stale docs vs git reality (docs claim work lives on a branch that was merged)

Propose a numbered fix list. Change nothing without explicit approval.

Procedure: spec <feature>

  1. Slugify the feature name (<feature> → kebab-case).
  2. Interview the user first: goal, non-goals, acceptance criteria, constraints. Then draft at least two approaches with a trade-off table and recommend one. The spec is not complete until the user approves the approach.
  3. If docs/specs/ has no template, copy templates/docs/specs/feature-spec.md.tmpl there.
  4. Only now create docs/specs/<slug>.md from the template, filled with the approved results — every {{SLOT}} resolved, no half-filled file on disk.
  5. Remind the user: the spec is the source of truth for implementation; the full gear in docs/development.md applies.

Procedure: verify

  1. Discover available checks from the repo's manifests/config (see references/detection.md §verification-discovery).
  2. Run what exists, in this order, stopping at the first failure: format check → lint → typecheck → unit tests → integration tests → build → e2e.
  3. Report each layer as pass/fail/skipped-not-present. Never claim success for a layer that didn't run.

Working on changes (any command)

When implementing or modifying code in a repo that uses this workflow, follow the two-gear loop in references/workflow.md: quick gear for small changes, full gear for features/architecture. Gear rule: touching >3 files, public API, data model, or infra — or not trivially reversible — means full gear. Close quick-gear work against checklists/quick-change.md; track full-gear work with checklists/full-change.md, and complete checklists/pre-merge-review.md before requesting merge approval.

Boundaries

  • Never overwrite human-owned files (see references/merge-rules.md).
  • Never install dependencies, add tooling, or change CI without explicit approval.
  • Never commit or push without explicit approval; draft the message, let the human decide.
  • Stay in scope: no drive-by refactors, no unsolicited "cleanups".
  • If the repo has its own AGENTS.md rules, they outrank this skill's defaults.

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.