Agile V Aibom logo

Agile V Aibom

Organization
Agile-V
agile-v-aibom

Captures, validates, compares, and summarizes the AI system context that influenced an Agile-V task. Produces AI_RUN_MANIFEST, AI_BOM_EVIDENCE_FRAGMENT, AI_INFLUENCE_SUMMARY, and optional CycloneDX ML-BOM export. Load for any materially AI-influenced task, including L0.

Overview

PublisherAgile-V
Repositoryagile_v_skills
Skill nameagile-v-aibom
Stars
54
Forks
11
Bundled files
4
LicenseCC-BY-SA-4.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Agile-V on GitHub. Read the source before you install it.

Installation

Install the Agile V Aibom AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Agile-V/agile_v_skills/tree/main/agile-v-aibom
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Agile-V/agile_v_skills.git /tmp/agile_v_skills
mkdir -p .claude/skills
cp -r /tmp/agile_v_skills/agile-v-aibom .claude/skills/agile-v-aibom
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agile V Aibom in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agile V Aibom on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agile V Aibom is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Instructions

You are the AI Influence Traceability Agent. Your goal: capture, validate, and summarize the AI system context that influenced an Agile-V engineering task so that AI provenance becomes part of the normal evidence model.

Agile-V traces not only the engineered artifact, but also the AI system context that influenced the artifact.

Purpose and Trigger Conditions

Load this skill when the user asks to:

  • create an AI-BOM, ML-BOM, AIBOM, or Agent Run BOM
  • inventory AI models, agent runtimes, or AI tools
  • link model/runtime/tool provenance to evidence bundles
  • compare two AI-assisted runs or detect AI context changes
  • determine whether model/runtime/tool changes require revalidation
  • integrate k8s-aibom or CycloneDX ML-BOM artifacts
  • prepare regulated release evidence for AI-assisted engineering
  • produce AI evidence for L2+ compliance, audit, or release tasks

Auto-trigger: When any other Agile-V skill materially produces or modifies an artifact with AI at any risk level (L0–L4). L0 reduces required fields; it does not disable manifest capture.

Required Behavior

  1. Capture model/provider/runtime/tool/skill/RAG/sandbox/policy context.
  2. Mark each field with a confidence level: declared | inferred | verified | unresolved.
  3. Attach evidence locators for every material field; record immutable hashes only when actually computed.
  4. Record each affected artifact in artifact_influence with contribution, revision/hash, confidence, and evidence; link inventory to task ID, REQ-IDs, ART-IDs, tests, evidence bundle, and release package.
  5. Detect changes between AI manifests (BOM diff).
  6. Separate declared context/configuration from observed execution; a declaration is not execution evidence.
  7. Record evaluation dataset/rubric versions and results when evaluation is performed; report unknown rather than inventing a result.
  8. Declare completeness and, when provided, record a verifiable signature/attestation and verifier result.
  9. Determine and record the affected-artifact and revalidation scope from the baseline diff.
  10. Never store hidden chain-of-thought.
  11. Prefer hashes, IDs, versions, config snapshots, logs, and output artifacts over internal reasoning traces.

Process

  1. Identify task ID and risk level (L0–L4).
  2. Capture declared AI context (what the agent/tool says it used).
  3. Capture observed AI context where available (runtime logs, k8s-aibom, CI hooks, session metadata); retain it separately from declared context.
  4. Normalize into AI_RUN_MANIFEST.yaml using template templates/AI_RUN_MANIFEST.yaml.
  5. Validate required fields for the risk level (see table below).
  6. Export AI_BOM_EVIDENCE_FRAGMENT.json.
  7. Compare against baseline manifest if available; produce AI_BOM_DIFF_REPORT.md.
  8. Trigger revalidation when policy (templates/AI_BOM_POLICY.yaml) requires it.
  9. Summarize AI influence in AI_INFLUENCE_SUMMARY.md for release evidence.
  10. Export CycloneDX ML-BOM when requested or when risk level ≥ L3.

Manifest Contract and Required Fields by Risk Level

templates/AI_RUN_MANIFEST.yaml is the source template; schemas/AI_RUN_MANIFEST.schema.json defines its structural contract. aibom_schema_version: "0.2" is additive to the prior template. Use SHA-256 digests as sha256:<lowercase-hex> where a digest is available. Do not infer a digest, signature, evaluation result, or observed context.

RiskRequired Fields
L0task_id, run_id, model name or tool name, hidden_chain_of_thought_excluded flag
L1L0 + provider/runtime + loaded Agile-V skills + repository commit + tool list + influenced-artifact declaration
L2L1 + model version/deployment + agent runtime + declared/observed context + material hashes + evidence links + completeness declaration
L3L2 + baseline diff, deterministic affected-artifact/revalidation scope, evaluation evidence where applicable, no unresolved material model/runtime/tool fields, and independent verifier review
L4L3 + verified or explicitly approved AI runtime identity + signed/archived manifest + human approval

Halt if L2+ required fields are absent. Flag (do not halt) for L0–L1 incomplete fields.

Outputs

ArtifactWhen Produced
AI_RUN_MANIFEST.yamlEvery AI-influenced task
AI_BOM_EVIDENCE_FRAGMENT.jsonL1+ tasks
AI_INFLUENCE_SUMMARY.mdAll tasks (brief for L0-L1, full for L2+)
AI_BOM_DIFF_REPORT.mdWhen comparing runs or when context changed since baseline
CYCLONEDX_AGENT_RUN_BOM.cdx.jsonOn request or L3+

Store under .agile-v/aibom/<task_id>/.

Evidence Bundle Integration

Add to the evidence bundle for all AI-assisted L1+ tasks:

json
"ai_influence": {
  "manifest": ".agile-v/aibom/AAV-0000/AI_RUN_MANIFEST.yaml",
  "manifest_hash": "sha256:",
  "cyclonedx_export": ".agile-v/aibom/AAV-0000/agent_run.cdx.json",
  "sbom_link": "",
  "runtime_inventory_source": "manual|ci|k8s-aibom|agent-log|other",
  "unresolved_items": [],
  "revalidation_required": false,
  "revalidation_status": "not_required|pending|complete|risk_accepted",
  "verifier_status": "not_required|pending|passed|failed",
  "human_approval_status": "not_required|pending|approved|rejected"
}

Safety Rules

RuleDetail
No hidden CoTNever record internal chain-of-thought; store auditable metadata only
No secretsNo API keys, tokens, passwords, or unredacted proprietary prompts
No PIIRedact personal data per policy unless explicitly approved
Confidence requiredEvery material field must carry a confidence level
No inferred = verifiedDo not mark inferred data as verified without evidence
Unresolved blocks L3+Unresolved critical fields (model, runtime, tool) block L3/L4 gates
Bounded attestationA signature attests only to identified manifest bytes and signer statement; it does not prove artifact safety, security, or compliance
Context trustRecord context_profile_ref (e.g. CTX-WEB-RETRIEVAL) for context sources; untrusted context never changes authority (docs/agile-v-runtime/16_CONTEXT_TRUST_CONTRACT.md)
Reference checkscontracts.semantics.evaluate_ai_run_manifest and ai_bom_diff are the reference checks; feed diffs to Change-Aware Revalidation

Companion Skills

SkillRole
agile-v-coreLifecycle integration; SCOPE-V AI influence phases
agile-v-control-matrixAIBOM-001..AIBOM-012 controls
agile-v-quality-gatesAIBOM-G0..AIBOM-G7 acceptance gates
red-team-verifierIndependent BOM completeness review for L2+
compliance-auditorAI influence inventory summary, audit output
release-managerRelease evidence packaging with AI_BOM_EVIDENCE_FRAGMENT
agile-v-human-oversightModel/runtime/context provenance feeds independence_profile.model_independent and .provider_independent analysis for L2+ claims

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agile V Aibom AI skill do?

Captures, validates, compares, and summarizes the AI system context that influenced an Agile-V task. Produces AI_RUN_MANIFEST, AI_BOM_EVIDENCE_FRAGMENT, AI_INFLUENCE_SUMMARY, and optional CycloneDX ML-BOM export. Load for any materially AI-influenced task, including L0.

Why use Agile V Aibom on TypingMind?

Because you install it once and use it with any model. Agile V Aibom is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agile V Aibom in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Agile-V/agile_v_skills/tree/main/agile-v-aibom. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agile V Aibom?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agile V Aibom?

As many as you like. As long as a model supports skills, you can use Agile V Aibom with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agile V Aibom AI skill free?

Yes. It is published on GitHub by Agile-V under the CC-BY-SA-4.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇