greybeard

greybeard is a skill for Claude Code, Codex from ManojLingala/greybeard. It costs 109 tokens per session (1,650 once invoked), scanned A, original, MIT.

A set of backend coding rules that makes an AI agent consider failures involving money, repeated requests, outside services, and simultaneous updates.

In plain words
What is it for?
It is for guiding server-side code involving payments, database changes, external calls, retries, time limits, and concurrency.
Why use it?
It helps prevent common production problems such as duplicate charges, lost data, slow failed requests, and incorrect money calculations.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for guiding server-side code involving payments, database changes, external calls, retries, time limits, and concurrency.

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Install with agentmods
npx agentmods add skills/manojlingala/greybeard/greybeard
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.

Any agent
npx skills add ManojLingala/greybeard --skill greybeard
Clone the repo
git clone --depth 1 https://github.com/ManojLingala/greybeard

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 greybeard

README.md
[![agentmods](https://agentmods.dev/badge/skills/manojlingala/greybeard/greybeard/github.svg)](https://agentmods.dev/skills/manojlingala/greybeard/greybeard)
Your own site
<a href="https://agentmods.dev/skills/manojlingala/greybeard/greybeard"><img src="https://agentmods.dev/badge/skills/manojlingala/greybeard/greybeard/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.

agentmods 80×15 button for greybeard

Your own site · 80×15
<a href="https://agentmods.dev/skills/manojlingala/greybeard/greybeard"><img src="https://agentmods.dev/badge/skills/manojlingala/greybeard/greybeard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 109 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,650 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00109 $0.01650
Opus 5.5 $0.00044 $0.00660
Sonnet 5.5 $0.00022 $0.00330
Haiku 4.5 $0.00011 $0.00165

Measured 1mo ago against content hash 944950eccbee, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

greybeard 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.

The scan reads SKILL.md. This mod also ships 1 executable file (assets/make_hero.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.

SKILL.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.

greybeard

He has shipped payment systems that move billions. He does not trust your happy path.

You know him. Grey beard, sharp eyes, the on-call pager scars to prove it. You hand him a tidy little endpoint that works on your machine. He reads it for ten seconds and asks: "What happens when this runs twice? When the bank times out? When two requests hit the same row? Where did the half-cent go?"

greybeard puts him inside your AI agent. Before the agent writes backend code, it walks the ladder below and stops at the first rung that applies.


The ladder

The agent must consider these in order for any server-side code that touches money, state, external systems, or concurrency. Each rung is a question the agent answers in a one-line greybeard: comment in the code, naming what it did.

1. Money?            → integer minor-units, never float. Explicit rounding. Currency code travels with the amount.
2. Mutation?         → idempotency key. Safe to retry. Exactly-once effect, at-least-once delivery.
3. External call?    → timeout (always). Retry with jittered backoff. Circuit breaker on repeated failure.
4. Concurrency?      → explicit transaction boundary. Optimistic concurrency / row lock. No lost updates.
5. Reads a list?     → pagination. Bounded result set. No unbounded fan-out, no N+1.
6. Can it fail half-way? → graceful degradation. Compensating action or saga. Partial failure is a first-class path.
7. Then, and only then: write the minimum correct code — and make it observable.

If a rung does not apply, the agent skips it silently. It does not add machinery for problems the code does not have. greybeard is paranoid, not ceremonial — it is the opposite of cargo-cult enterprise code.


Inbound webhooks (the 3am classic)

Webhooks arrive across a trust boundary, are best-effort, and are delivered more than once. greybeard never trusts them on faith. For any inbound webhook the agent enforces, in order:

W1. Verify signature      → HMAC the RAW request bytes against the endpoint secret. Reject if invalid.
W2. Hash the raw body     → never the framework-parsed/re-serialized body. (The #1 reason verification "mysteriously" fails — and gets disabled.)
W3. Reject replays        → check the signed timestamp against a tolerance window; a captured request must not work later.
W4. Don't trust the payload→ treat amounts/state as a claim, not truth. Confirm against the provider or your own record before acting.
W5. Idempotent processing → dedupe on the provider event id (see rung 2). A redelivered event is a no-op.
W6. Reconcile out-of-band → webhooks WILL be missed (your endpoint 500s, the retry window lapses). A periodic sweep pulls events from the provider API and repairs the diff. Delivery is best-effort; reconciliation is the source of truth.
W7. Ack fast, work async   → return 2xx quickly, do slow work on a queue, so the provider doesn't time out and retry-storm you.

Read the full file on GitHub · 133 lines

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. 1mo ago First seen · 133 lines · 109 tokens per session scan A 944950eccbee

Subscribe to this mod's changes

greybeard is a skill published in the GitHub repository ManojLingala/greybeard (3 stars, last pushed 12d ago), licensed MIT. It adds 109 tokens to every session and 1,650 once invoked, about $0.0004 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.

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