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 navendubrajesh/context-management-for-agents --skill project-developmentgit clone --depth 1 https://github.com/navendubrajesh/context-management-for-agentsWrote 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/navendubrajesh/context-management-for-agents/project-development)<a href="https://agentmods.dev/skills/navendubrajesh/context-management-for-agents/project-development"><img src="https://agentmods.dev/badge/skills/navendubrajesh/context-management-for-agents/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/navendubrajesh/context-management-for-agents/project-development"><img src="https://agentmods.dev/badge/skills/navendubrajesh/context-management-for-agents/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 | $0.00064 | $0.01462 |
| Opus 5 | $0.00032 | $0.00731 |
| Sonnet 5 | $0.00013 | $0.00292 |
| Haiku 4.5 | $0.00006 | $0.00146 |
Grade A, and why
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 10d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Project Development
Design and build LLM-powered projects from ideation through deployment. This skill covers the meta-level decisions that shape a project before any code is written: whether LLMs are appropriate for the task, how to structure the pipeline, what output formats to use, how to handle batch processing, and how to plan for cost and scale.
When to Activate
Activate this skill when:
- Starting a new LLM-powered project from scratch
- Analyzing whether a task is a good fit for language models
- Designing pipeline architecture (single-pass, chain, DAG, agent loop)
- Choosing structured output schemas and validation strategies
- Planning batch processing for throughput optimization
- Estimating costs and planning resource allocation
Do not activate this skill for adjacent work owned by other skills:
- Designing individual tool schemas and descriptions:
tool-design. - Choosing multi-agent topology (supervisor vs. swarm):
multi-agent-patterns. - Building evaluation pipelines and metrics:
evaluation. - Designing agent operating loops:
harness-engineering.
Core Concepts
Start every project with task-model fit analysis. Not all tasks benefit from LLMs — some are better served by traditional software, rules engines, or simpler ML models. LLMs excel at tasks requiring language understanding, generation, reasoning across unstructured data, and flexible decision-making. They struggle with tasks requiring exact arithmetic, deterministic logic, real-time performance, or guaranteed correctness.
Design pipelines around the complexity of the task, not the sophistication of the technology:
| Pipeline Type | When to Use | Complexity |
|---|---|---|
| Single-pass | Task has one clear input → output | Lowest |
| Chain | Task decomposes into sequential steps | Low |
| DAG | Steps have conditional branches or parallel paths | Medium |
| Agent loop | Task requires exploration, tool use, or self-correction | Highest |
Choose the simplest pipeline that solves the problem. Over-engineering adds cost, latency, and debugging difficulty without proportional quality gains.
What ships with it
1 file 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.
- 10d ago First seen · 136 lines · 64 tokens per session scan A 832c14ec4bbb
project-development is a skill published in the GitHub repository navendubrajesh/context-management-for-agents (2 stars, last pushed 2mo ago), licensed MIT. It adds 64 tokens to every session and 1,462 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
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