mapping-selection

mapping-selection is a skill for Claude Code, Codex from cosai-oasis/secure-ai-tooling. It costs 160 tokens per session (2,934 once invoked), scanned A, original, Apache-2.0.

A selection guide for linking a CoSAI Risk Map risk or control to the components, risks, controls, and external framework references that directly apply. These references can include MITRE ATLAS, NIST AI RMF, and OWASP LLM guidance.

In plain words
What is it for?
Use it to choose and justify mappings for a new or updated risk-map control or risk.
Why use it?
It reduces incorrect or excessive links between related entries. The guide focuses on where a defense operates and which risks or framework categories it genuinely addresses.

Skill for Claude CodeCodex

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

Good fit Use it to choose and justify mappings for a new or updated risk-map control or risk.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/cosai-oasis/secure-ai-tooling/mapping-selection
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 cosai-oasis/secure-ai-tooling --skill mapping-selection
Clone the repo
git clone --depth 1 https://github.com/cosai-oasis/secure-ai-tooling

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 mapping-selection

README.md
[![agentmods](https://agentmods.dev/badge/skills/cosai-oasis/secure-ai-tooling/mapping-selection.svg)](https://agentmods.dev/skills/cosai-oasis/secure-ai-tooling/mapping-selection)
Your own site
<a href="https://agentmods.dev/skills/cosai-oasis/secure-ai-tooling/mapping-selection"><img src="https://agentmods.dev/badge/skills/cosai-oasis/secure-ai-tooling/mapping-selection.svg" alt="Measured on agentmods" height="20"></a>
Per session 160 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,934 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00160 $0.02934
Opus 5 $0.00080 $0.01467
Sonnet 5 $0.00032 $0.00587
Haiku 4.5 $0.00016 $0.00293

Measured 4d ago against content hash 1ac91a1bf1f6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

mapping-selection 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 4d 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.

scripts/skills/mapping-selection/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Mapping Selection

Choose a control's structured references — components, risks, and framework mappings — and justify each. The failure this skill prevents is confident-but-wrong selection: mapping to "related" rather than directly-relevant items, over-mapping, mixing MITRE techniques with mitigations, or picking the wrong NIST AI RMF function.

Scope: both the control direction (a control's components/risks/mappings) and the risk direction (a risk's components/controls/mappings) — see the two sections below.

Procedure

1. Components — where the defense lives

Read risk-map/yaml/components.yaml. Select the components where the control's mechanism actually operates — the locus of the defense. Guidance:

  • Prefer the specific components. Each one you list should be a place the control genuinely acts, not merely a place the risk appears.
  • Use "all" only for a universal/governance/assurance control that genuinely applies framework-wide.
  • Use "none" only when the control applies to no specific component.
  • Do not over-select. If you are tempted to list five components, check whether the control is really one control or several.

2. Risks — what the control addresses

Read risk-map/yaml/risks.yaml. Select the risks this control directly mitigates. A mapping should be defensible in one sentence ("this control reduces the likelihood/impact of risk X because…"). Flag any that are merely "related." Be selective.

If you set risks: "all", the control is universal — and those risks must not list it back (application is implicit). "none" is not valid for risks.

3. Framework mappings — the discipline

Read references/frameworks-applicability.md for the rules. In brief:

  • Applicability: map only to frameworks that apply to controls.
  • MITRE ATLAS: controls map to mitigations (AML.M####), never techniques (AML.T####). If no mitigation fits cleanly, omit ATLAS rather than forcing a technique.
  • NIST AI RMF: use the subcategory id (e.g., MEASURE-2.7), never the category alone, and pick the right function — this is the most common mistake:
    • GOVERN — policy, roles, responsibilities, oversight, culture, risk tolerance. Most preventive design and human-oversight controls land here.
    • MAP — establishing context, framing intended use, identifying impacts.
    • MEASURE — assessment, testing, metrics, evaluation, tracking.
    • MANAGE — responding to, prioritizing, treating, and recovering from identified risks (reactive/operational). Do not use MANAGE for a preventive design control.
  • OWASP Top 10 for LLM: LLMxx:2025.
  • EU AI Act: Article N@2024, only when the control implements a specific regulatory obligation (e.g. human oversight → Article 14). Do not force it onto a generic technical control — see references/frameworks-applicability.md's non-US counterbalance note (D3b).
  • Selective: soft cap of 4 per framework; one-sentence rationale each.
  • Generate, don't hand-spell: mapping values are version-pinned. For an entity that already has a row in the corpus, produce the value with scripts/framework_mapping_maintainer.py (ADR-027). For a control being drafted pre-PR (no row yet), the tool's composition step runs before entity lookup, meaning it will compose a value structurally even for a fabricated identifier — but add still requires a corpus row (real or a stub) to complete the command; without one, the command exits 1 at the lookup step. Pointed at a scratch copy of controls.yaml with a stub row for the not-yet-real id, add composes correctly. The real reason to hand-compose here isn't that the tool can't run (with a stub row, it can): it's that the tool gives no fabrication protection either way — it composes a plausible-but-nonexistent --framework-specific-ref exactly as readily as a real one, exit 0, no existence check, and a correctly hand-composed value is byte-identical in structure to a tool-generated one, so it passes the round-trip purity check just as cleanly. So compose the value directly against the pinned pattern in risk-map/docs/contributing/framework-mappings-style-guide.md, then verify it through audit-framework-mappings: single-entity mode, naming the real id, if controlXxx already has a row in controls.yaml; candidate mode, stating entity type control, if it's a pre-PR draft with no row yet. If the entity has no row yet (goes through candidate mode) and you are deliberately deferring any other value for that same framework to a later pass, say so — see Output format for the full scope-selection and deferred-value rules (rejecting a value as not directly relevant is not deferral and needs no declaration). This declaration only matters for candidate mode; single-entity mode has no drip-feed mechanism to route it to.

Read the full file on GitHub · 83 lines

Files

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.

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. 4d ago Changed · +3 lines · +5 tokens per session 1ac91a1bf1f6
  2. 8d ago First seen · 80 lines · 155 tokens per session scan A 8fdcf1eb6a1b

Subscribe to this mod's changes

mapping-selection is a skill published in the GitHub repository cosai-oasis/secure-ai-tooling (98 stars, last pushed yesterday), licensed Apache-2.0. It adds 160 tokens to every session and 2,934 once invoked, about $0.0008 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.

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