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 TIKAZI/TIKAZ-Codex-Context-Economy --skill context-packgit clone --depth 1 https://github.com/TIKAZI/TIKAZ-Codex-Context-EconomyWrote 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/tikazi/tikaz-codex-context-economy/context-pack)<a href="https://agentmods.dev/skills/tikazi/tikaz-codex-context-economy/context-pack"><img src="https://agentmods.dev/badge/skills/tikazi/tikaz-codex-context-economy/context-pack/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/tikazi/tikaz-codex-context-economy/context-pack"><img src="https://agentmods.dev/badge/skills/tikazi/tikaz-codex-context-economy/context-pack.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 | $0.00052 | $0.00647 |
| Opus 5.5 | $0.00021 | $0.00259 |
| Sonnet 5.5 | $0.00010 | $0.00129 |
| Haiku 4.5 | $0.00005 | $0.00065 |
Grade A, and why
context-pack 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.
This is a copy
100% identical to context-pack — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Pack
Designed, integrated, independently refactored, and continuously maintained by TIKAZ.
Inputs and routing
Accept one or more files, folders, code trees, logs, structured data, or converter-produced Markdown plus a concrete task. Use text for confident text-first material, hybrid for bounded task-relevant visuals or complex tables, and source when extraction cannot preserve important evidence.
Workflow
Own canonical ingestion, fidelity profiling, exact deduplication, evidence selection, and final pack size. First run profile or let pack profile automatically:
text: canonical Markdown is sufficient;hybrid: use Markdown for text and a bounded visual-evidence queue for informative images or complex tables;source: keep the original asset/page path when safe extraction cannot preserve task-relevant information.
Do not trigger vision for a logo, repeated icon, background, or every image merely because it exists. When the queue contains pending-vision items and the host can inspect images, resolve the referenced item, record an anchored observation plus uncertainty, and keep the original reference. When the capability is unavailable, leave it pending or recommend the source file; never invent a description.
Profile first, protect literal facts and anchors, deduplicate only exact or formatting-only repetition, select task-relevant evidence, and assemble one task-ready artifact in this order:
- task and expected output;
- selected mode and estimated budget;
- confirmed constraints and protected facts;
- exact evidence excerpts with source anchors;
- decisions, completed work, and current state;
- conflicts and open questions;
- omitted-anchor inventory and verification limits.
The pack must distinguish exact source text, structured state, and inference. It must remain useful without the surrounding chat. Count the complete artifact against the budget. If essential protected evidence cannot fit, return a visible budget conflict instead of silently exceeding the limit.
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.
- 1mo ago First seen · 51 lines · 52 tokens per session scan A f7ed222e47b8
context-pack is a skill published in the GitHub repository TIKAZI/TIKAZ-Codex-Context-Economy (3 stars, last pushed 4d ago), licensed MIT. It adds 52 tokens to every session and 647 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to context-pack, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
query
Progressive retrieval from the Trove index. Use when the user asks what Trove knows about a subsystem, where code lives, or wants context assembled before editing. Runs architecture -> subsystem -> module -> symbol retrieval with a context budget.
context-engineer
Context window optimizer — analyze, audit, and optimize your agent's context utilization. Know exactly where your tokens go before they're sent.
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
memory-system
A guide to giving an AI agent persistent, user-specific memory across conversations. It covers what to remember, how to store it, how to update or compress it, and when to use memory instead of a shared knowledge base.
[object Object]
Read, search, and open notes in the user's "{{GRAPHNAME}}" Reflect graph via the reflect CLI. Use when the user asks about their notes, daily notes, journal, or anything they may have written down in Reflect.