Skills project-development
📦

project-development

Content revision r2 Medium Risk 📁 Filesystem access⚙️ External commands🌐 Network access

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.

Supports: Claude Codex Code(CC)
⚠️ 50 Poor

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request
Review the Skillstore skill "project-development" from https://skillstore.io/skills/muratcankoylan-project-development.md and its manifest at https://skillstore.io/api/skills/muratcankoylan-project-development/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/muratcankoylan-project-development.md Signed manifest GET /api/skills/muratcankoylan-project-development/manifest Signed lockfile GET /api/skills/muratcankoylan-project-development/lockfile

Test it

Using "project-development". Design a pipeline that summarizes 5,000 support tickets into weekly themes.

Expected outcome:

  • Recommendation: use a single-agent batch pipeline because tickets are independent.
  • Stages: acquire tickets, normalize text, summarize each ticket, aggregate themes, and render the weekly report.
  • Validation: manually test representative tickets and measure theme accuracy before scaling.
  • Cost plan: estimate input and output tokens, then add a retry buffer.

Using "project-development". Should a compliance document checker use multiple agents?

Expected outcome:

  • Start with one agent and deterministic validation rules.
  • Add specialized agents only when benchmark results show measurable gains.
  • Require human review because compliance decisions have low error tolerance.

Using "project-development". Create a parsing contract for model-generated product evaluations.

Expected outcome:

Use fixed section headers, enumerated ratings, explicit missing-value rules, tolerant extraction, and logged validation failures for human review.

Security Audit

Medium Risk
v8 • 8/9/2026 Open versioned report

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.

4
Files scanned
2,102
Lines analyzed
6
Review items
0
False positives ignored

Confirmed security concerns (3)

Medium
scripts/pipeline_template.py:132-153scripts/pipeline_template.py:603-652scripts/pipeline_template.py:767-773
Unvalidated Identifiers Permit Path Traversal
Batch and item identifiers are joined without validation. Absolute or traversal paths can redirect writes and cleanup deletions outside intended directories.
The CLI value reaches Path joins without confinement checks, and cleanup unlinks files beneath the resulting path.
Medium
scripts/pipeline_template.py:571-596scripts/pipeline_template.py:767-770
Generated HTML Includes Unescaped Batch Identifier
The batch identifier is inserted into title and heading markup without escaping. A crafted identifier can inject active content when the report opens.
The CLI-controlled batch_id is interpolated directly into two HTML contexts while other displayed strings are explicitly escaped.
Medium
scripts/pipeline_template.py:256-264scripts/pipeline_template.py:306-315
Untrusted Source Content Can Manipulate Model Instructions
Acquired title and content are inserted directly into the model prompt. Malicious source text can alter requested behavior or corrupt structured output.
The template interpolates source fields without trust-boundary controls, then passes the complete prompt to the configured LLM call.
Capability review items (6)

These are real local capabilities that may be expected for this skill, so they require review but are not counted as confirmed malicious behavior.

Medium
scripts/pipeline_template.py:182
Python file write/append
with open(raw_file, "w") as f:
This executable write uses a path derived from an unvalidated batch identifier. Absolute or traversal values can redirect pipeline output outside the intended directory.
Medium
scripts/pipeline_template.py:246
Python file write/append
with open(prompt_file, "w") as f:
This executable write uses a path derived from an unvalidated batch identifier. Absolute or traversal values can redirect pipeline output outside the intended directory.
Medium
scripts/pipeline_template.py:314
Python file write/append
with open(response_file, "w") as f:
This executable write uses a path derived from an unvalidated batch identifier. Absolute or traversal values can redirect pipeline output outside the intended directory.
Medium
scripts/pipeline_template.py:404
Python file write/append
with open(parsed_file, "w") as f:
This executable write uses a path derived from an unvalidated batch identifier. Absolute or traversal values can redirect pipeline output outside the intended directory.
Medium
scripts/pipeline_template.py:417
Python file write/append
with open(agg_file, "w") as f:
This executable write uses a path derived from an unvalidated batch identifier. Absolute or traversal values can redirect pipeline output outside the intended directory.
Medium
scripts/pipeline_template.py:547
Python file write/append
with open(output_file, "w") as f:
This executable write uses a path derived from an unvalidated batch identifier. Absolute or traversal values can redirect pipeline output outside the intended directory.

Risk Factors

📁 Filesystem access (12)
references/pipeline-patterns.md:309 references/pipeline-patterns.md:485 references/pipeline-patterns.md:500 references/pipeline-patterns.md:505 references/pipeline-patterns.md:529 references/pipeline-patterns.md:608 scripts/pipeline_template.py:182 scripts/pipeline_template.py:246 scripts/pipeline_template.py:314 scripts/pipeline_template.py:404 scripts/pipeline_template.py:417 scripts/pipeline_template.py:547
⚙️ External commands (18)
SKILL.md:10 SKILL.md:25 SKILL.md:26 SKILL.md:27 SKILL.md:28 SKILL.md:80-82 SKILL.md:82-96 SKILL.md:96-102 SKILL.md:102-131 SKILL.md:131-133 SKILL.md:133-151 SKILL.md:151-171 SKILL.md:171-273 SKILL.md:273-274 SKILL.md:274-275 SKILL.md:275-276 SKILL.md:276-277 SKILL.md:277-278
🌐 Network access (2)
SKILL.md:292 SKILL.md:293
Audited by: codex View Audit History →
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Security Assessment

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Academic citations (APA · BibTeX · CFF)

APA citation

muratcankoylan. (2026). project-development security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/muratcankoylan-project-development/audits/8

BibTeX citation

@techreport{muratcankoylan-muratcankoylan-project-development-2026, author = {muratcankoylan}, title = {project-development security audit report (audit version 8)}, institution = {Skillstore}, year = {2026}, number = {8}, url = {https://skillstore.io/skills/muratcankoylan-project-development/audits/8}, note = {Author version unspecified} }

CITATION.cff

cff-version: 1.2.0 message: "If you use this Skill, cite its author and this versioned security audit report." title: "project-development security audit report (audit version 8)" version: "unspecified" type: report authors: - name: "muratcankoylan" date-released: "2026-08-09" url: "https://skillstore.io/skills/muratcankoylan-project-development/audits/8" identifiers: - type: other value: "skillstore:muratcankoylan-project-development:audit:8" description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Higher Skillstore usage
muratcankoylan Recommended Current

muratcankoylan-project-development

Skillstore Score 50
Evidence Confidence High
Skillstore usage 14
Updated

2026-08-21

ChakshuGautam

chakshugautam-project-development

Skillstore Score 50
Evidence Confidence High
Skillstore usage 9
Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High
64
Architecture
85
Maintainability
87
Content
71
Community
83
Spec Compliance

What You Can Build

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.

Try These Prompts

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.

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.

Avoid

  • 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.

Frequently Asked Questions

Does this skill call an LLM provider?
No. The Python template returns a mock response until you implement a provider client.
Can it design multi-agent systems?
It supports the project-level choice between single and multiple agents. Detailed topology design belongs to a specialized multi-agent skill.
Does it calculate exact production costs?
No. It provides an estimation method using item counts, token assumptions, pricing inputs, and a retry buffer.
What outputs does the template create?
It creates staged local artifacts, aggregated results, and a static HTML report.
Is the template production ready?
No. Add provider integration, security validation, tests, monitoring, authentication, and deployment controls before production use.
Which tools support this skill?
The skill supports Claude, Codex, and Claude Code.

Developer Details

License

MIT

Skillstore revision

r2

Version notice

The author did not declare a version.

Repository

https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/tree/main/skills/project-development

Ref

02be9409c79ca1183f7844009c14d9df684d0cf9

Maintenance freshness

8/11/2026

Usage

13 downloads · 299 views

File structure

📁 references/

📄 case-studies.md

📄 pipeline-patterns.md

📁 scripts/

📄 pipeline_template.py

📄 SKILL.md