advise-project-approach is an agent skill for project planning, course correction, and review. Its portable SKILL.md can be loaded by Claude, Codex, pi, and other Agent Skills-compatible harnesses.

Before recommending a stack, architecture, vendor, refactor, or shipping plan, it checks:

  • your actual constraints
  • comparable real-world projects
  • tradeoffs and failure conditions
  • cost and lock-in realities
  • when the recommendation becomes wrong

Use It When

  • you have a rough project idea and need a build plan
  • your repo is getting messy and you need course correction
  • you are choosing between stacks or vendors
  • you want a review before shipping
  • you want the agent to explain what not to build yet

One-Line Install

npx skills@latest add AaravKashyap12/advise-project-approach --skill advise-project-approach

This uses the open skills installer to fetch the repo from GitHub and install only this skill. It requires Node.js/npm. Review installed skills before use; skills run with your agent's normal permissions.

Source of Truth

The runtime skill spec lives in skills/advise-project-approach/SKILL.md. That file is the source of truth for the workflow agents actually run.

Everything else in this repo exists to package, explain, test, or distribute that skill.

What's New in v0.6.0

v0.6 makes the skill portable across agent harnesses without removing the compatibility layers existing users rely on.

  • Adds AGENTS.md as the shared repository guidance source, with a small CLAUDE.md import bridge for Claude Code.
  • Documents installation for pi, Claude Code, Codex, and generic Agent Skills-compatible harnesses.
  • Keeps the packaged .skill, Claude plugin manifest, and Codex agents/openai.yaml as additive compatibility layers.
  • Adds a portability evidence matrix that separates structural compatibility from runtime claims.
  • Adds release-version validation so VERSION, plugin metadata, and the README cannot silently drift apart.

See the full changelog for earlier versions.

Where This Fits

Use recent-signal tools to discover what changed.

Use advise-project-approach to decide what to build, change, defer, or avoid.

The skill is not trying to be a general search engine. It is a project-judgment workflow for turning evidence into engineering decisions.

Try These Prompts

"What's the best way to build a self-hosted bookmark manager?"
"Research comparable projects before I start this."
"I'm halfway through building a Node/Express API. Is my approach right?"
"Review my finished project at github.com/owner/repo."
"Should I use Postgres or SQLite for this?"
"What stack should I use given I know Python and want to self-host?"
"Should I use Supabase/Firebase/Neon/Vercel, or will pricing hurt later?"

What It Does

Drop it into your agent and it will:

  • Pre-build: Research your stack, find comparable real projects, compare architecture options, and hand you a build plan before you commit to anything you will regret in month three.
  • Mid-build: Inspect your repo, identify what is actually wrong, not just what is fashionable to fix, and give you a prioritized list of changes ordered by impact.
  • Post-build: Review your finished project against mature comparables, call out the gaps, and tell you what to harden before you ship.

It does the research loop a good engineer would do manually: understand the goal, inspect the evidence, study credible comparables, evaluate the tradeoffs, and recommend the highest-leverage path.

No vibes. Evidence first.

Works Across Agent Harnesses

The workflow is self-contained in its runtime skill file:

skills/advise-project-approach/SKILL.md

From a local clone, install the same skill folder into the location your harness scans:

Harness Local skill location
pi ~/.agents/skills/ or ~/.pi/agent/skills/
Claude Code ~/.claude/skills/
Codex ~/.codex/skills/
Other compatible agents Point the loader at the skill folder or SKILL.md 
cp -r skills/advise-project-approach <your-agent-skill-directory>/

Pi documents recursive discovery of directories containing SKILL.md and permits freeform additional files. See pi's skill documentation.

The packaged .skill archive, .claude-plugin/plugin.json, and agents/openai.yaml are additive compatibility layers for installers and host UIs. They do not change the portable SKILL.md workflow, and harnesses that do not use them can ignore them.

Other Install Methods

To inspect the available skill before installing:

npx skills@latest add AaravKashyap12/advise-project-approach --list

Manual Download

Download the packaged skill:

dist/advise-project-approach.skill

Or install from the GitHub release:

Download the v0.6.0 release asset

Skill UI

  1. Download dist/advise-project-approach.skill.
  2. Open your agent's skill settings.
  3. Upload the .skill file.
  4. Start a new conversation.

Plugin Metadata

This repo also includes .claude-plugin/plugin.json for plugin-aware installers that can read skill collections from GitHub repositories.

Demo

You: I want to build a self-hosted bookmark manager. Solo dev, Python background, want tags and full-text search.

Agent, with skill: researches linkding, Linkwarden, LinkAce, official framework docs, and relevant search/storage options.

## Project Approach: Self-Hosted Bookmark Manager

### TL;DR
Go with Django + SQLite FTS5 or Postgres full-text search, depending on your hosting target and expected scale. Keep the main UI server-rendered with HTMX, Turbo, or light JavaScript unless the UI needs true SPA complexity. This matches your Python skills, keeps deployment simple, and is backed by nearby real projects like linkding.

### Comparable Projects
1. linkding - github.com/sissbruecker/linkding; Django, DRF, Huey, Turbo/Lit, Docker, optional Postgres; nearest domain match; limits: current details must be verified at review time.
2. Linkwarden - github.com/linkwarden/linkwarden; heavier collaborative bookmark manager; useful contrast for when archiving/collaboration matter more than simplicity.
3. LinkAce - linkace.org; mature self-hosted bookmark manager in a different stack; useful for feature comparison, less useful for implementation fit.

The demo avoids hard-coded star counts and "latest" dates because those decay. The skill requires the agent to verify those values at review time.

See more examples:

Why This Is Different From Just Asking

Without the skill, an agent will usually give you an answer. This skill makes it give you an accountable answer:

  • Every "active" or "maintained" claim needs an exact date or adoption signal.
  • Comparable projects are verified against real repos, docs, or other primary sources.
  • Comparables must be separated into what transfers and what should not be copied.
  • Pricing claims must distinguish "free to start" from "cheap to operate."
  • Vendor choices must consider storage, bandwidth, usage limits, add-ons, migration cost, and lock-in.
  • If no repo was provided, it says "advisory from description" instead of pretending it inspected files.
  • Large repos are mapped first, then sampled by relevance instead of read blindly.
  • The recommendation includes what you gain, what you give up, what becomes harder later, and when it becomes wrong.
  • A self-check runs before output: is this grounded in actual project constraints, or is it generic?

What It Produces

Pre-Build

## Project Approach: <name>
TL;DR / Project Frame / Comparable Projects / Recommended Stack /
Cost and Vendor Reality / Architecture Direction / Alternatives Considered / Build Plan /
Risks and Unknowns / References

Mid-Build or Post-Build

## Project Approach Review: <name>
TL;DR / Project Summary / Evidence Reviewed, including evidence status /
What Is Working / Comparable Projects / Gap Analysis /
Recommended Changes, grouped High / Medium / Low /
Stack and Architecture Verdict / Cost and Vendor Reality / Risks and References

What It Will Not Do

  • Invent star counts, last-commit dates, benchmark numbers, or production adoption claims.
  • Treat "free to start" as proof that a vendor is cheap to operate.
  • Invent prices, quotas, usage limits, or cost estimates without sources.
  • Pretend it reviewed files when you only gave it a description.
  • Tell you to add auth, tests, or Docker if you already have them.
  • Recommend something because it is trending instead of because it fits your constraints.
  • Give a production-grade review to a weekend prototype without calibrating the advice.

Repo Structure

.
|-- README.md
|-- LICENSE
|-- CHANGELOG.md
|-- ROADMAP.md
|-- CONTRIBUTING.md
|-- SECURITY.md
|-- AGENTS.md
|-- CLAUDE.md
|-- assets/
|   `-- brand/
|       |-- lockup-dark.svg
|       `-- lockup-light.svg
|-- .claude-plugin/
|   `-- plugin.json
|-- .github/
|   `-- workflows/
|       `-- validate.yml
|-- dist/
|   `-- advise-project-approach.skill
|-- skills/
|   `-- advise-project-approach/
|       |-- SKILL.md
|       `-- agents/
|           `-- openai.yaml
|-- examples/
|   |-- ab-comparisons.md
|   |-- pricing-operating-cost.md
|   |-- prebuild-bookmark-manager.md
|   |-- midbuild-express-api.md
|   `-- postbuild-fastapi-template.md
|-- evals/
|   |-- README.md
|   |-- cases.json
|   |-- portability.md
|   `-- results/
|-- scripts/
|   |-- package_skill.py
|   `-- validate_skill.py

The packaged .skill file is a zip archive containing the advise-project-approach/ skill folder.

Development

Validate and rebuild the package:

python scripts/validate_skill.py
python scripts/package_skill.py
python scripts/validate_skill.py

The GitHub Actions workflow runs the same checks and fails if the generated package differs from what is committed.

Evaluation

The skill is forward-tested across vague and detailed pre-build requests, vendor-cost decisions, mid-build repository reviews, large-repository sampling, and post-build launch reviews.

The first exploratory six-case run surfaced risks in intake enforcement, research completeness, repository permission boundaries, and stopping behavior. A stricter rerun explicitly invoked the skill by name and path. Both records are preserved rather than hiding the rough first pass.

Methodology and rubric | Reusable cases | Portability audit | Initial failure run | v0.5.0 focused rerun

These are exploratory forward tests, not yet a controlled same-model baseline benchmark. No improvement percentage is claimed.

Contributing

Issues and PRs are welcome. The most useful contributions are:

  • New repo test cases: a repo, what the skill got wrong, and what it should have said.
  • Evidence discipline failures: cases where a claim was made without a verifiable source.
  • Mode selection bugs: cases where the skill picked the wrong operating mode.

License

MIT

Portfolio

See more of my work at https://www.aaravkashyap.live/.