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 agentmods add skills/vasilyu1983/ai-agents-public/dev-context-engineeringnpx skills add vasilyu1983/AI-Agents-public --skill dev-context-engineeringgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/dev-context-engineering)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/dev-context-engineering"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/dev-context-engineering.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.04338 |
| Opus 5 | $0.00020 | $0.02169 |
| Sonnet 5 | $0.00008 | $0.00868 |
| Haiku 4.5 | $0.00004 | $0.00434 |
Grade A, and why
dev-context-engineering 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 6d 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 — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering
Use this skill to design repo-native agent context that is portable, high-signal, and maintainable. It owns the context model, migration path, maturity assessment, and context-graph discipline across AGENTS.md, runtime-specific layers, specs, rules, hooks, compiled markdown knowledge bases, and related artifacts.
Quick Reference
| Task | Use |
|---|---|
| Portable repo instructions | the agents-memory skill, references/fast-track-guide.md |
| Spec and plan flow | docs-ai-prd, dev-workflow-planning |
| Context graph design | references/context-graph-guide.md, python3 scripts/scan_context_artifacts.py --help, python3 scripts/validate_context_graph.py --help |
| Hot-tier staleness + supersession integrity checks | scripts/validate_context_graph.py (checks check_stale_tiers, check_supersession_integrity) |
| Query a context graph (PPR, BFS, fan-in, tier budget) | python3 scripts/query_context_graph.py <graph> --help (modes: --node, --impact, --rank, --ppr --seed, --tier-budget) |
| Compiled markdown knowledge layer | the docs-notes-retrieval skill, references/context-graph-guide.md, references/multi-repo-strategy.md |
| Multi-repo strategy | references/multi-repo-strategy.md, dev-context-multi-repo |
| Pick a framework (superpowers, GSD, Spec Kit, OpenSpec, …) | references/framework-selection-matrix.md |
| Regulated environment rollout | references/regulated-environment-patterns.md, assets/ai-agent-governance.md, assets/compliance-fca-emi.md |
When to Use
- Set up or modernize
AGENTS.md,CLAUDE.md, Copilot instructions, hooks, or agent rule layers. - Migrate a repo from ad hoc prompting to durable agent context.
- Decide what belongs in always-on context vs docs, retrieval, or on-demand artifacts.
- Build a context graph and loading-tier model for a repo.
- Define a maturity path for AI-assisted development across one repo or a portfolio.
- Design the operating model for a large compiled repo knowledge base where agents create and refresh repo descriptions, indexes, and reports from structured artifacts.
What ships with it
17 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.
- assets/ai-agent-governance.md 3.5 KB
- assets/compliance-fca-emi.md 2.9 KB
- assets/data-handling-gdpr-pci.md 3.3 KB
- assets/fca-compliance-gate.yml 7.1 KB
- assets/pr-template-ai-disclosure.md 1.9 KB
- data/sources.json 77 KB
- references/context-graph-guide.md 11 KB
- references/context-resolver-pattern.md 4.2 KB
- references/fast-track-guide.md 13 KB
- references/framework-selection-matrix.md 9.1 KB
- references/information-routing-rules.md 4.8 KB
- references/multi-repo-strategy.md 22 KB
- references/regulated-environment-patterns.md 20 KB
- schemas/context-graph.schema.json 5.4 KB
- scripts/query_context_graph.py 13 KB runs code
- scripts/scan_context_artifacts.py 11 KB runs code
- scripts/validate_context_graph.py 7.9 KB runs code
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.
- 6d ago First seen · 294 lines · 41 tokens per session scan A d6a2c6c4e292
dev-context-engineering is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (84 stars, last pushed 4d ago), licensed MIT. It adds 41 tokens to every session and 4,338 once invoked, about $0.0002 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 2026-08-30.
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