Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add viktorbezdek/skillstack --skill agent-project-developmentgit clone --depth 1 https://github.com/viktorbezdek/skillstackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/viktorbezdek/skillstack/agent-project-development)<a href="https://agentmods.dev/skills/viktorbezdek/skillstack/agent-project-development"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/agent-project-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/viktorbezdek/skillstack/agent-project-development"><img src="https://agentmods.dev/badge/skills/viktorbezdek/skillstack/agent-project-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00090 | $0.02284 |
| Opus 5.5 | $0.00036 | $0.00914 |
| Sonnet 5.5 | $0.00018 | $0.00457 |
| Haiku 4.5 | $0.00009 | $0.00228 |
Grade A, and why
agent-project-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Development Methodology
Principles for identifying tasks suited to LLM processing, designing effective project architectures, and iterating rapidly using agent-assisted development. Applies whether building a batch processing pipeline, a multi-agent research system, or an interactive agent application.
When to Activate
- Starting a new project that might benefit from LLM processing
- Evaluating whether a task is well-suited for agents versus traditional code
- Designing the architecture for an LLM-powered application
- Planning a batch processing pipeline with structured outputs
- Choosing between single-agent and multi-agent approaches
- Estimating costs and timelines for LLM-heavy projects
Decision Tree: Task-Model Fit
Should you use LLM processing for this task?
+-- Does it require synthesis across sources? --> Likely YES
+-- Does it involve subjective judgment with rubrics? --> Likely YES
+-- Is natural language the desired output? --> Likely YES
+-- Is there tolerance for individual errors? --> Likely YES
+-- Is the domain knowledge in the model's training? --> Likely YES
|
+-- Does it require precise computation? --> Likely NO (use traditional code)
+-- Does it need real-time sub-second responses? --> Likely NO
+-- Does it require perfect accuracy? --> Likely NO (hallucination risk)
+-- Does it depend on proprietary data the model lacks? --> Likely NO
+-- Must same input produce identical output? --> Likely NO
|
+-- Mixed? --> Manual prototype first (5 minutes saves weeks)
Core Concepts
The Manual Prototype Step
Before investing in automation, validate task-model fit with a manual test. Copy one representative input into the model interface. Evaluate the output quality. This takes minutes and prevents hours of wasted development.
If the manual prototype fails, the automated system will fail. If it succeeds, you have a baseline and a template for prompt design.
Pipeline Architecture
LLM projects benefit from staged pipeline architectures where each stage is:
- Discrete: Clear boundaries between stages
- Idempotent: Re-running produces the same result
- Cacheable: Intermediate results persist to disk
- Independent: Each stage can run separately
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 231 lines · 90 tokens per session scan A 1687b893a93f
agent-project-development is a skill published in the GitHub repository viktorbezdek/skillstack (14 stars, last pushed 4mo ago), licensed MIT. It adds 90 tokens to every session and 2,284 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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