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/robcsaszar/scaffold/ai-context-dot-mdnpx skills add robcsaszar/scaffold --skill ai-context-dot-mdgit clone --depth 1 https://github.com/robcsaszar/scaffoldWrote 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/robcsaszar/scaffold/ai-context-dot-md)<a href="https://agentmods.dev/skills/robcsaszar/scaffold/ai-context-dot-md"><img src="https://agentmods.dev/badge/skills/robcsaszar/scaffold/ai-context-dot-md.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 | $0.00148 | $0.02177 |
| Opus 5 | $0.00074 | $0.01089 |
| Sonnet 5 | $0.00030 | $0.00435 |
| Haiku 4.5 | $0.00015 | $0.00218 |
Grade A, and why
ai-context-dot-md 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 2d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Context Dot MD
Create or review CONTEXT.md files that give agents the factual substrate needed to reason correctly about code.
Mode Detection
- User provides a path to an existing CONTEXT.md → Review Mode
- User asks to create/generate/add a CONTEXT.md → Create Mode
- Ambiguous → ask:
(c)reate a new CONTEXT.md or (r)eview an existing one?
Create Mode
Step 1 — Placement
Ask the user: "Where should the CONTEXT.md live?"
(r)oot — broad architecture overview
(p)ath — specific directory (provide path)
(m)ultiple — hierarchical (start with root, then children)
If a CONTEXT.md already exists at the chosen path, ask: File exists. (o)verwrite from scratch / (r)eview and update / (Q)uit? If review-and-update, switch to Review Mode first, then apply improvements.
If the scope has fewer than 5 files or a single obvious purpose, suggest: This scope may be too small for a standalone CONTEXT.md — a section in the parent might suffice. (p)roceed anyway / (Q)uit?
Step 2 — Analyze
MANDATORY READ references/knowledge-base.md for principles and patterns.
Before investigating, ask yourself:
- What would confuse a new agent about this scope?
- What terms mean something different HERE than in general usage?
- What can't the agent find by looking? The code already answers what
ls,package.json, and config files show — write only what looking cannot find: the unwritten convention, the reason behind a boundary, the gotcha no config confesses.
Investigate the target scope:
- Read directory structure, key files, existing docs (README, AGENTS.md)
- Identify the domain model (entities, relationships, naming conventions in code)
- Map architecture (boundaries, data flow, abstractions)
- Note conventions that aren't obvious from code alone
- Find gotchas and known gaps
Step 3 — Draft
MANDATORY READ references/template.md for structure.
Write sections in priority order:
What ships with it
3 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.
- 2d ago Changed · +3 lines 89e88d53c26d
- 5d ago First seen · 195 lines · 148 tokens per session scan A da6b543ac57e
ai-context-dot-md is a skill published in the GitHub repository robcsaszar/scaffold (1 stars, last pushed 3d ago), licensed MIT. It adds 148 tokens to every session and 2,177 once invoked, about $0.0007 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.
Other skills, from other repositories
keep-the-why
Extract and preserve the reasoning code cannot explain - decisions, rejected alternatives, workarounds, incidents, constraints - plus project setup and maintainer interviews. Not for what changed (see Keep a Changelog) - only why.
sw-do
Implement a SpecWeave increment task by task through the ledger, with evidence per task and a verified close. Use for "implement this", "start working", "continue the increment", "keep going".
review
Adversarial fresh-context review of an increment before it ships. Every finding cites path:line and is re-verified. Use when saying "review", "grill this", or "critique the implementation".
done
Close an increment: ledger check, specweave verify, optional review, then specweave complete. Use when all tasks are done and saying "close increment", "we are done", or "finish up".
summarize
Summarize conversations, logs, docs, or investigation notes into action-oriented text with evidence tags. Use for recap, handoff, CI failure digest, or MEMORY. Triggers: 总结, 汇总, summarize, 交接, 复盘, 调试总结.
pre-modification-check
Use before modifying, refactoring, moving, or deleting files in a Repowise-indexed repository, especially shared utilities, core modules, public APIs, or files the user did not explicitly identify.