# Design Reliable LLM Project Pipelines

LLM projects often fail because task fit, architecture, costs, and output contracts remain unclear. This skill provides a staged method and reusable pipeline template.

## Install

```bash
npx skillstore add muratcankoylan/project-development
```

## Metadata

- Status: approved
- Slug: muratcankoylan-project-development
- Skillstore revision: r2
- Version status: missing
- Tree hash: f621cb6c830c6a2b424e8fb65911329b9338ae6da65e619dd634891bd930125d
- Author: muratcankoylan
- GitHub username: muratcankoylan
- License: MIT
- Repository: https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/tree/main/skills/project-development
- Ref: 02be9409c79ca1183f7844009c14d9df684d0cf9
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: filesystem, external\_commands, network
- Quality score: 50
- Quality tier: warning
- Public page: https://skillstore.pages.dev/skills/muratcankoylan-project-development
- Manifest: https://skillstore.pages.dev/api/skills/muratcankoylan-project-development/manifest

## Capabilities

- Evaluates whether an LLM matches a task using explicit proceed and stop criteria.
- Structures projects into acquire, prepare, process, parse, and render stages.
- Estimates token usage and processing costs with a retry buffer.
- Guides project-level choices between single-agent and multi-agent architectures.
- Defines structured output contracts and tolerant parsing practices.
- Provides a Python batch pipeline template with caching, cleanup, rendering, and cost estimation.

## Use Cases

- Plan a Batch Analysis Product: Define stages, intermediate artifacts, parsing contracts, and cost estimates before implementation.
- Review an Agent Architecture: Compare single-agent and multi-agent options against complexity, context, quality, and cost requirements.
- Prototype a Research Pipeline: Adapt the staged Python template for source acquisition, model processing, result parsing, and report rendering.

## Prompt Templates

### Check Task Fit

```
Assess whether an LLM suits this task: [task]. List proceed signals, stop signals, one manual test, and a clear recommendation.
```

### Design Pipeline Stages

```
Design acquire, prepare, process, parse, and render stages for [project]. Define each input, output, cache boundary, failure mode, and rerun behavior.
```

### Estimate Cost and Scale

```
Estimate costs for [item count], [input tokens], [output tokens], and [model prices]. Include retries, concurrency assumptions, uncertainty, and reduction options.
```

### Review a Complete Architecture

```
Review this LLM project architecture: [design]. Challenge task fit, agent topology, trust boundaries, structured outputs, evaluation gates, costs, recovery, and operational risks.
```

## Limitations

- The included LLM call is a mock and requires provider integration.
- The template does not provide deployment, authentication, monitoring, or production infrastructure.
- Path identifiers and generated HTML require security hardening before production use.
- Cost estimates use configurable assumptions and do not query current provider pricing.

## Best Practices

- Run one representative manual test before automating the workflow.
- Persist stage outputs so expensive model calls remain cacheable and independently rerunnable.
- Measure quality and actual token costs throughout development.

## Anti Patterns

- Do not choose multiple agents without evidence that simpler designs fail.
- Do not combine acquisition, model calls, parsing, and rendering into one opaque step.
- Do not assume model output always follows the requested format.

## Security Audit

- Audited at: 2026-08-09T09:56:16.232\+00:00
- Summary: Most static findings are false positives caused by Markdown formatting, documentation examples, benign citations, and mislabeled text. The executable template has medium-risk path traversal, HTML injection, and indirect prompt injection weaknesses. These issues should be fixed before marketplace publication.

## Stats

- Views: 299
- Downloads: 14
- Favorites: 0
- Popularity score: 0