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Guide · Agent skill

How to use the Project Development Methodology agent skill

Updated · written with AI from the project's own documentation and checked against our catalogue data

The Project Development Methodology skill gives an agent a structured way to make project-level decisions about LLM-powered work: whether an LLM is the right tool for a task, how a multi-stage pipeline should be shaped, and how to estimate tokens, cost and timelines. It is aimed at developers planning batch jobs and teams deciding whether an LLM is worth building around. After this guide you will know what the skill does, how to install it, how to trigger it and what it needs to run.

Project Development Methodologymuratcankoylan/Agent-Skills-for-Context-Engineering · MIT

Quick start

  1. 1Download and unzip. Download the ZIP from this page and unzip the project-development folder.
  2. 2Place the folder. Move it to ~/.claude/skills/ for personal use or to .claude/skills/ inside a project. Alternatively, upload the ZIP under Settings, Capabilities, Skills in the Claude apps.
  3. 3Start a new session. Open a fresh session so the agent loads the skill.
  4. 4Describe the project. State the task, the input volume and what output you need, in plain language.
  5. 5Run a manual prototype. Test one representative input by hand to check whether an LLM can do the task.
  6. 6Run the pipeline template. If you need a batch job, review scripts/pipeline_template.py, install its Python and API requirements, then run it on a few items first.

What an agent skill is and what this one makes the agent do

An agent skill is a folder with a SKILL.md file that tells an agent when to use a method and how to follow it. This skill is the project-level one: its unit of work is a whole project or a multi-stage pipeline, not a single tool or prompt.

Once installed, the agent checks task-model fit before any code is written. It uses a two-table test: tasks with synthesis, subjective judgment, natural language output and batch processing are suited to an LLM, while tasks needing precise computation, real-time responses, perfect accuracy or deterministic output are not. It also suggests a staged pipeline and helps reason about tokens, cost and timelines.

  • Decide whether an LLM is the right primitive for a task before building
  • Shape a pipeline as acquire, prepare, process, parse, render
  • Keep intermediate results as files so stages can be re-run
  • Estimate tokens, dollar cost and timelines for LLM-heavy work
  • Choose between single-agent and multi-agent designs at the project level
  • Design structured outputs that a later stage can parse

Install the project-development skill

Download the project-development ZIP from this page and unzip it. For a personal install, place the folder in ~/.claude/skills/. For a single project, place it in that project's .claude/skills/ folder. Alternatively, in the Claude apps, go to Settings, then Capabilities, then Skills, and upload the ZIP there.

After installing, start a new session so the agent can see the skill. The folder should contain SKILL.md, scripts/pipeline_template.py, and the references folder with case-studies.md and pipeline-patterns.md.

  • Personal install: ~/.claude/skills/
  • Project install: .claude/skills/ inside your project
  • Claude apps: Settings, then Capabilities, then Skills, then upload the ZIP

How to trigger the skill with natural requests

You do not need a special command. Describe a project-level question in plain language. The skill is meant for work where the unit is a whole project or pipeline, so mention the batch, pipeline, cost or architecture question directly.

If your request is about one tool's schema or error messages, or about trimming context inside a single run, the skill routes you elsewhere: tool-design for tool work, and context-optimization for per-skill loading or context-budget tactics.

  • Should I use an LLM for this: I have 5,000 support tickets to tag by topic?
  • Plan a pipeline that summarises 2,000 PDFs with Claude, and estimate the token cost.
  • Do I need a multi-agent design for this research project, or is one agent enough?
  • Help me design a JSON output format my parser can read reliably from the model.

Use the pipeline template script

The skill includes scripts/pipeline_template.py, a template for a file-based pipeline. The process stage calls a Claude model, for example claude-sonnet-4-6. Each item gets its own folder, and each stage writes a file, so a finished stage is skipped on re-run.

The skill's file-system pattern uses files such as raw.json, prompt.md, response.md and parsed.json inside data/{id}/. To re-run a stage, delete its output file and the downstream files, then run again.

  • Requires Python to run scripts/pipeline_template.py
  • Requires an LLM API key and SDK for the process stage; the exact client dependency is not confirmed in the source excerpt
  • Read the intermediate files directly to debug a stage
data/{id}/ raw.json # acquire stage complete prompt.md # prepare stage complete response.md # process stage complete parsed.json # parse stage complete

Safety notes: scripts and network access

This skill runs scripts and uses the network. The template script calls an LLM API, so it sends your prompts and data to that service and uses your API key. Review scripts/pipeline_template.py before running it, and confirm which data it reads and where it writes.

Keep the process stage in mind for cost: it is the expensive, non-deterministic step, so test on a few items before running a full batch.

  • Runs scripts: yes
  • Uses network: yes
  • Check what data leaves your machine before processing sensitive material

Tips for getting useful output

Run a manual prototype first: paste one representative input into the model and judge the result. A failed prototype usually predicts a failed automated system. The skill recommends this before any automation.

When you need structured output, ask for section markers, a format example, a note that the output will be parsed programmatically, and constrained values. Expect the model not to follow format perfectly, so build parsers that tolerate variation and log failures rather than crashing.

  • Test one item by hand before scaling
  • Use fixed section markers and enumerated values
  • Make stages idempotent so re-runs do not duplicate work
  • Read the case studies in references/case-studies.md for worked examples

What people use it for

  • Deciding whether a text classification job is worth building with an LLM before writing code
  • Estimating token counts and dollar cost for a batch of documents before committing budget
  • Planning a pipeline that separates the costly LLM stage from quick parsing and rendering
  • Choosing between one agent and several agents for a research project
  • Designing a structured output format that a downstream script can parse

Frequently asked questions

How do I install the project-development skill?

Download the ZIP from this page and unzip it into ~/.claude/skills/ for personal use, or into a project's .claude/skills/ folder. In the Claude apps you can instead upload the ZIP under Settings, Capabilities, Skills.

What do I need to run it?

The skill itself is methodology and a template script. The template needs Python to run scripts/pipeline_template.py, and an LLM API key and SDK for the process stage. The template calls a Claude model, such as claude-sonnet-4-6.

Is the project-development skill free?

The source is licensed under MIT. This page does not state pricing, so check the repository for licence details. Note that the LLM API calls in the process stage are billed by your API provider.

Can it guarantee exact or deterministic results?

No. The skill itself says LLMs are unreliable for exact math, real-time responses, perfect accuracy and deterministic output, so it advises against using an LLM for those tasks.

How is this different from the tool-design skill?

project-development covers whole projects and pipelines. Tool-design covers the description, schema, naming, response format and error messages of an individual tool, and the skill routes those requests there.

Does it work for a single small script?

It is designed for multi-stage work where the unit is a whole project or pipeline. For a single tool or per-skill context tactic, the skill points you to other skills instead.

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Our verdict

Choose this skill if you are planning a batch job or pipeline that runs many items through an LLM and want a reusable, file-based template with clear stages. Skip it if you need exact math, real-time responses or deterministic output, if you are designing a single tool, or if you want a finished app rather than a methodology and template script.

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