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 cosai-oasis/secure-ai-tooling --skill mapping-selectiongit clone --depth 1 https://github.com/cosai-oasis/secure-ai-toolingWrote 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/cosai-oasis/secure-ai-tooling/mapping-selection)<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>- NVIDIA SkillSpector pass
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 | $0.00160 | $0.02934 |
| Opus 5 | $0.00080 | $0.01467 |
| Sonnet 5 | $0.00032 | $0.00587 |
| Haiku 4.5 | $0.00016 | $0.00293 |
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.
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 — seereferences/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 — butaddstill 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 ofcontrols.yamlwith a stub row for the not-yet-real id,addcomposes 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-refexactly 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 inrisk-map/docs/contributing/framework-mappings-style-guide.md, then verify it throughaudit-framework-mappings: single-entity mode, naming the real id, ifcontrolXxxalready has a row incontrols.yaml; candidate mode, stating entity typecontrol, 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.
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.
- 4d ago Changed · +3 lines · +5 tokens per session 1ac91a1bf1f6
- 8d ago First seen · 80 lines · 155 tokens per session scan A 8fdcf1eb6a1b
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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