ria-client-margin-of-safety

ria-client-margin-of-safety is a skill for Claude Code, Codex from deciqAI/knowledge-skills. It costs 77 tokens per session (684 once invoked), scanned A, original, MIT.

A retirement-planning framework that adds buffers against bad outcomes, especially a market decline early in retirement. It uses cash or bonds, conservative withdrawals, and stress tests to protect a plan from estimation errors.

In plain words
What is it for?
Use it to set spending buffers and withdrawal rates, and to test whether a near-retirement plan can survive an early market crash.
Why use it?
It helps reduce the risk that someone must sell investments after an early loss to fund spending. It is aimed at retirement or other periods when withdrawals are already beginning.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is **Industry front door for [margin-of-safety](../margin-of-safety/SKILL.md).** Adds domain triggers, example, packs only. Parent Process unchanged..

Good fit Use it to set spending buffers and withdrawal rates, and to test whether a near-retirement plan can survive an early market crash.

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Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/deciqAI/knowledge-skills
agentmods
npx agentmods add skills/deciqai/knowledge-skills/ria-client-margin-of-safety

Made for: Claude Code, Codex.

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README.md
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Per session 77 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 684 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 $0.00077 $0.00684
Opus 5 $0.00039 $0.00342
Sonnet 5 $0.00015 $0.00137
Haiku 4.5 $0.00008 $0.00068

Measured 9d ago against content hash ce3bd132f83d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

ria-client-margin-of-safety 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 9d 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.

ria-client-margin-of-safety/SKILL.md · 49 lines

How it starts

The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RIA — Client Plan Margin of Safety

Industry front door for margin-of-safety. Adds domain triggers, example, packs only. Parent Process unchanged. Not investment advice.

Activate when: building a retirement/decumulation plan; setting cash buffers/withdrawal rates; stress-testing a plan; client near retirement; "will they be okay if markets drop early?" Do NOT activate when: pure accumulation with long horizon and no near-term draws.

Why this variant

The parent margin-of-safety builds a buffer so estimation error doesn't cause ruin. A retiree's plan lives or dies on sequence-of-returns risk; the margin of safety is the cash/bond buffer and withdrawal haircut that lets the plan survive an early bad market.

Domain inputs → parent's Process

  • Size a spending buffer (e.g. 1–3 yrs of withdrawals in cash/short bonds) so early drawdowns aren't sold at the bottom.
  • Haircut the withdrawal rate below the "optimistic" number; plan to the conservative case.
  • Stress-test against an early-crash sequence, not just average returns.

Worked example

$1.5M portfolio, planned 5% draw, retiring into a rich market. → Margin of safety: hold 2 years' spending in cash, set draw at 4% with a guardrail to cut in down years. A crash in year 1 is met from the buffer, not by liquidating equities low. Plan survives the sequence.

Packs

  • Solo: buffer + guardrail rule embedded in every decumulation plan.
  • Advisory: dynamic-spending (guardrails) policy documented in the IPS.

Red flags

  • Planning to average returns, ignoring sequence risk.
  • No cash buffer; forced equity sales in downturns.
  • Withdrawal rate set at the optimistic edge.

Verification

  • Spending buffer sized to horizon of concern
  • Withdrawal haircut applied vs optimistic case
  • Early-crash sequence stress-tested
  • Guardrail/dynamic-spending rule documented

Part of deciqAI Knowledge Skills. Core method: margin-of-safety.

Read the full file on GitHub · 49 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. 9d ago First seen · 49 lines · 77 tokens per session scan A ce3bd132f83d

Subscribe to this mod's changes

ria-client-margin-of-safety is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 11d ago), licensed MIT. It adds 77 tokens to every session and 684 once invoked, about $0.0004 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 2026-09-03.

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