context-engineering

context-engineering is a skill for Claude Code, Codex from liza-mas/liza. It costs 123 tokens per session (3,726 once invoked), scanned A, original, Apache-2.0.

A method for examining the prompts and outputs used by coding agents. It looks at how context, instructions, tool results, and token limits are arranged.

In plain words
What is it for?
Use it to analyze an agent project's prompt and output files, including context size, tool usage, caching, and how well the supplied information fits each role.
Why use it?
It helps identify missing or repeated information, wasted context, unclear role-specific instructions, and other prompt-design problems that can affect agent results.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/liza-mas/liza/context-engineering
Any agent
npx skills add liza-mas/liza --skill context-engineering
Clone the repo
git clone --depth 1 https://github.com/liza-mas/liza

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for context-engineering

README.md
[![agentmods](https://agentmods.dev/badge/skills/liza-mas/liza/context-engineering.svg)](https://agentmods.dev/skills/liza-mas/liza/context-engineering)
Your own site
<a href="https://agentmods.dev/skills/liza-mas/liza/context-engineering"><img src="https://agentmods.dev/badge/skills/liza-mas/liza/context-engineering.svg" alt="Measured on agentmods" height="20"></a>
Per session 123 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,726 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00123 $0.03726
Opus 5 $0.00062 $0.01863
Sonnet 5 $0.00025 $0.00745
Haiku 4.5 $0.00012 $0.00373

Measured 6d ago against content hash b4bedb34cd84, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/context-corpus-index.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/context-engineering/SKILL.md · 266 lines

How it starts

The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Context Engineering

Scope

Analyze only §BRAND_PROJECT_DIRNAME§/agent-prompts/ and §BRAND_PROJECT_DIRNAME§/agent-outputs/ unless the user names other artifacts.

This skill is complementary to §BRAND_BINARY_NAME§-logs: §BRAND_BINARY_NAME§-logs finds operational failures and token/tool patterns; this skill explains whether the prompt and context design caused or amplified those patterns.

Protocol

1. Inventory Before Reading

Run the corpus indexer first:

python3 skills/context-engineering/scripts/context-corpus-index.py §BRAND_PROJECT_DIRNAME§

Use the generated index as the primary source for mechanical discovery: inventory, prompt/output pairing, size and pressure signals, outcome signals, common tools, MCP usage, and sample selection. The index is not evidence of causality by itself.

The indexer supports both Claude rich stream-json logs and Codex sparse item.completed logs. Check the reported format counts before assuming which fields are available.

Use indexer options deliberately:

python3 skills/context-engineering/scripts/context-corpus-index.py §BRAND_PROJECT_DIRNAME§ --json
python3 skills/context-engineering/scripts/context-corpus-index.py §BRAND_PROJECT_DIRNAME§ --max-pair-minutes 30
python3 skills/context-engineering/scripts/context-corpus-index.py §BRAND_PROJECT_DIRNAME§ --sample-limit 25
  • Use --json when exact pair metadata, token fields, or full metrics are needed.
  • Use --max-pair-minutes to control how strict same-role timestamp pairing should be.
  • Use --sample-limit to expand or shrink top lists and the sampling plan.

If a §BRAND_BINARY_NAME§-logs report or analyzer output is available, use it as the first sampling guide. Prioritize roles, runs, or timestamps with repeated tool failures, broad tool-result volume, duplicated task-local material, growing prompts, low cache reuse for expected-stable prefixes, or blocked/rejected task outcomes.

If §BRAND_BINARY_NAME§-logs and context-engineering evidence disagree, report the disagreement explicitly and keep the narrower claim supported by direct prompt/output evidence. Example: §BRAND_BINARY_NAME§-logs may correctly flag token pressure while prompt shape is not the cause.

Read the full file on GitHub · 266 lines

Files

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.

Changes

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.

  1. 6d ago First seen · 266 lines · 123 tokens per session scan A b4bedb34cd84

Subscribe to this mod's changes

context-engineering is a skill published in the GitHub repository liza-mas/liza (364 stars, last pushed 4d ago), licensed Apache-2.0. It adds 123 tokens to every session and 3,726 once invoked, about $0.0006 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.

Related

Other skills, from other repositories

media-ingest

Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.

garrytan/gbrain · 52 tokens

mem0-oss-to-platform

Plan and then execute a migration of a project from the mem0 open-source / self-hosted SDK (the local Memory class) to the mem0 Platform / hosted / managed SDK (the MemoryClient class). Use this whenever a developer wants to move, switch, or migrate their mem0 usage off OSS/self-hosted to the hosted API — e.g.…

mem0ai/mem0 · 273 tokens

Cortex

Operate Cortex, the LifeOS memory system — the typed Knowledge Archive (People, Companies, Ideas, Research with typed related: links) plus recall of prior work sessions, ISAs, and conversations. Search, add, harvest, develop, ingest, distill, graph-navigate, recall. USE WHEN cortex, knowledge, knowledge base, search…

danielmiessler/LifeOS · 196 tokens

agent-memory

../../../engineering/agent-memory/skills/agent-memory/SKILL.md.

alirezarezvani/claude-skills · 0 tokens

memory

Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.

opensquilla/opensquilla · 44 tokens

ha-data-stores

Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…

shiwenwen/hope-agent · 115 tokens