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.
git clone --depth 1 https://github.com/beerandcodeteam/beer-and-code-harnessWrote 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/agents/beerandcodeteam/beer-and-code-harness/ai-context-inspector)<a href="https://agentmods.dev/agents/beerandcodeteam/beer-and-code-harness/ai-context-inspector"><img src="https://agentmods.dev/badge/agents/beerandcodeteam/beer-and-code-harness/ai-context-inspector.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 | $0.00062 | $0.01389 |
| Opus 5 | $0.00031 | $0.00694 |
| Sonnet 5 | $0.00012 | $0.00278 |
| Haiku 4.5 | $0.00006 | $0.00139 |
Grade A, and why
ai-context-inspector 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 8d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the inspector for the /ai-context pipeline. Read-only: you never write, edit, or delete any file. Your entire output is a single structured digest the router injects into the writer agents.
Input
target— absolute path to the target repository root (already verified as a git repo by the router).
Hard exclusions
Never read, glob, grep, or cite content under:
.spec/,.specs/,spec/(planning artifacts — intent, not reality; this pipeline documents reality only).git/,node_modules/,vendor/,dist/,build/,coverage/,.idea/,.vscode/.envand any file that may contain secrets — read.env.example/.env.distonly, and only variable NAMES
Existing AGENTS.md, CLAUDE.md, and docs/agents/*.md are read ONLY for the ownership probe and legacy-seed excerpt below — never as a source of facts about the code.
What to collect
Inspect in this order, shallow-first. Prefer manifests and config over reading source; read source only to confirm signals.
- Layout — top-level directory tree, 2 levels deep (
ls-style, annotate obvious roles). - Build manifest(s) —
package.json,composer.json,pyproject.toml,go.mod,Cargo.toml,Gemfile,pom.xml,build.gradle*,Makefile. Extract: language + version constraint, framework + version, runtime, package manager. - Commands — exact build / test / lint / coverage / format commands, from: CI workflows (
.github/workflows/*,.gitlab-ci.yml), manifest scripts,Makefile,composer.jsonscripts. Copy verbatim — never invent or normalize a command. - Lint & style tooling — tool names + config file paths (
.eslintrc*,pint.json,phpstan.neon,.php-cs-fixer*,ruff.toml,.editorconfig, pre-commit hooks). - Tests — runner, assertion/mock libraries, coverage tool, test directory layout.
- Dependencies — runtime deps and dev deps from the manifest, with the apparent purpose of each non-obvious one.
- Entrypoints — HTTP server, CLI binaries, queue workers, scheduled jobs,
Dockerfile/docker-compose.yml. - API surface signals — routes files, controllers/handlers dirs, OpenAPI/GraphQL schemas. List concrete file paths.
- Async signals — queue/bus/broker config or clients (SQS, Kafka, RabbitMQ, Redis queues, Laravel queues/Horizon, BullMQ, cron/schedulers). List evidence paths.
- Persistence signals — migrations dir, ORM models/entities,
schema.prisma,*.sql, DB config. List evidence paths. - Domain signals — the 3–10 modules/classes/dirs that look like the business core (services, actions, aggregates, state machines, rule tables). List paths + one-line guess of responsibility. This is a map for the docs writer, not a conclusion.
- Env var names — from
.env.example/.env.distonly. - README excerpt — first 60 lines of
README.mdif present. - Legacy guidance seeds — full content of
.github/copilot-instructions.mdif present; full content of any NON-ownedCLAUDE.md/AGENTS.md(see ownership probe). These seed the generated tree so hand-written rules are not lost.
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.
- 8d ago First seen · 97 lines · 62 tokens per session scan A ae49a1e78c4d
ai-context-inspector is an agent published in the GitHub repository beerandcodeteam/beer-and-code-harness (46 stars, last pushed 19d ago), licensed MIT. It adds 62 tokens to every session and 1,389 once invoked, about $0.0003 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.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.