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 paruff/uFawkesAI --skill context-engineeringgit clone --depth 1 https://github.com/paruff/uFawkesAIWrote 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/paruff/ufawkesai/context-engineering)<a href="https://agentmods.dev/skills/paruff/ufawkesai/context-engineering"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/context-engineering/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/paruff/ufawkesai/context-engineering"><img src="https://agentmods.dev/badge/skills/paruff/ufawkesai/context-engineering.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 | $0.00054 | $0.01551 |
| Opus 5.5 | $0.00022 | $0.00620 |
| Sonnet 5.5 | $0.00011 | $0.00310 |
| Haiku 4.5 | $0.00005 | $0.00155 |
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 20d 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Context Engineering
Load trigger:
"load context-engineering skill"> DORA: AI Capability 3: AI-accessible internal data Token cost: Low
Purpose
Move AI tools from generic assistants to specialized experts by connecting them to internal documentation and codebase structure. "Context engineering" is the practice of deliberately managing what internal knowledge is available to AI tools at session start — not just what prompt you give them.
DORA AI Capabilities Model v2025.1: "Connecting AI to your internal documentation and codebases moves it from a generic assistant to a specialized expert."
This skill eliminates the "re-discover context every session" tax — particularly costly at 2hrs/day across an 8-repo portfolio.
Minimum Corpus per Repo
Every uFawkes* repo must maintain these files for AI-accessible context. Missing files are flagged as corpus gaps, not silently ignored.
| File | Required | Purpose |
|---|---|---|
README.md |
✅ Mandatory | What this is, quick start, current status |
AGENTS.md |
✅ Mandatory | Agent suite, skill list, handoff protocol |
ARCHITECTURE.md or docs/ARCHITECTURE.md |
✅ Mandatory | System design, component relationships |
CONTRIBUTING.md |
✅ Mandatory | How to contribute — must not be a placeholder |
AI_STANCE.md |
✅ Mandatory | AI policy (see ai-stance skill) |
tests/README.md or equivalent |
✅ Mandatory | How to run tests, what they cover |
CHANGELOG.md |
✅ Mandatory | Release history |
DORA-event-format.md |
⚠ Required when uFawkesObs wired | Deployment event schema for DORA metrics |
docs/PROMPT_LIBRARY.md |
Optional | Tested prompt templates (see uFawkesObs) |
What ships with it
2 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.
- 20d ago Changed ed883a4ac22a
- 24d ago Changed · -24 lines · +6 tokens per session e31c49c221d9
- 1mo ago First seen · 153 lines · 48 tokens per session scan A 7ed570103cca
context-engineering is a skill published in the GitHub repository paruff/uFawkesAI (2 stars, last pushed yesterday), licensed MIT. It adds 54 tokens to every session and 1,551 once invoked, about $0.0002 per session on Opus 5.5. 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
memory-curator
Curate this tool's auto memory. Use when the memory index is near its load limit or the stored notes look duplicated or stale.
shared-memory
Recall or curate the project memory that every AI coding tool shares.
brain:init
Use when setting up project memory in a new folder or on the first brain:init in a project. Creates projectbrain.db, registers the project, and bootstraps it from CLAUDE.local.md, memory files, docs, and emails; safe to re-run.
cognee:recall
Use when the user says 'cognee recall', 'search memory', 'what do we know about', 'find related', or 'graph search'. Runs semantic, graph, and lexical search across the Cognee knowledge graph and injects results as agent context.
cognee:store
Use when the user says 'cognee store', 'push to cognee', 'save to graph', 'remember this' or 'log this decision', or when session decisions must be stored. Writes entities, decisions and events over POST /api/v1/remember.
brain:update
End-of-session brain flush. Scans the full conversation, extracts all new entities, decisions, tasks, events, relationships, and updates, presents a summary, and writes to projectbrain.db after confirmation. Use at end of every session or when you want to capture everything discussed.