# AKF > The AI native file format — trust scores, provenance, and compliance metadata that embed into every file your AI touches. pip install akf - **Type:** MCP server - **Install:** `agentstack add mcp-hmakt99-akf` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [HMAKT99](https://agentstack.voostack.com/s/hmakt99) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [HMAKT99](https://github.com/HMAKT99) - **Source:** https://github.com/HMAKT99/AKF - **Website:** https://akf.dev ## Install ```sh agentstack add mcp-hmakt99-akf ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About --- _akf: '{"v":"1.0","claims":[{"c":"Trust metadata for README.md","t":0.7,"id":"1979cbeb","src":"unspecified","tier":5,"ver":false,"ai":true,"evidence":[{"type":"other","detail":"updated certify and github action references","at":"2026-03-18T04:21:48.869226+00:00"}]}],"id":"akf-c33254656fc5","agent":"claude-code","at":"2026-03-18T04:21:48.870623+00:00","label":"public","inherit":true,"ext":false,"sv":"1.1"}' --- AKF — The AI Native File Format Trust scores · Source provenance · Security classification · Compliance readiness Embeds natively into DOCX, PDF, XLSX, images, code, and all major formats. Think EXIF for AI — ~15 tokens of JSON that travel with your files. ⭐ Star on GitHub · Website · 🛡️ Is This AI-Generated? · 🇪🇺 Compliance Check · 📊 Repo Scanner Quickstart · AI Agents · MCP Server · Agent Skills · CLI · Website --- ## The Problem AI generates reports, code, docs, images — and **none of it carries provenance.** Your team opens a file with no idea: Who made it? AI or human? Can we trust it? Is it compliant? **EU AI Act Article 50 takes effect August 2, 2026.** After that, AI-generated content must carry transparency metadata. Penalties: up to EUR 35M / 7% of global turnover. ## The Solution AKF is the **AI native file format** — ~15 tokens of JSON that embed into any file: | What | How | |------|-----| | **Trust score** | 0–1 confidence based on source tier | | **Source provenance** | SEC filing → analyst → AI agent chain | | **Compliance** | One command: `akf audit file --regulation eu_ai_act` | ``` AI generates content → AKF stamps trust metadata → Anyone can verify it ``` ### vs Alternatives | | AKF | C2PA | Watermarking | Manual tracking | |---|:---:|:---:|:---:|:---:| | Works on documents/code | ✅ | ❌ (media only) | ❌ | ⚠️ | | No Certificate Authority needed | ✅ | ❌ | ✅ | ✅ | | Trust scores | ✅ | ❌ | ❌ | ❌ | | Source provenance chain | ✅ | ✅ | ❌ | ⚠️ | | Compliance auditing | ✅ | ❌ | ❌ | ❌ | | ~15 tokens (LLM-friendly) | ✅ | ❌ | N/A | N/A | | 20+ file formats | ✅ | ⚠️ (media) | ⚠️ (text) | ❌ | | Free & open source | ✅ | ⚠️ | Varies | ✅ | ## Quickstart ```bash pip install akf # Python npm install akf-format # TypeScript / Node.js akf doctor # Check your install — detects PATH issues and guides setup ``` > **`akf` command not found?** Run `akf doctor` to auto-detect your setup, or use `python3 -m akf` (always works). > - Install with pipx: `pipx install akf` (recommended — auto-handles PATH) > - **Windows:** use `python3 -m akf` or install via `pipx` ```python import akf # Stamp trust metadata onto any AI output akf.stamp("Revenue was $4.2B, up 12% YoY", confidence=0.98, source="SEC 10-Q", agent="claude-code", model="claude-sonnet-4-20250514") # Embed into Office docs, PDFs, images — any format akf.embed("report.docx", claims=[...], classification="confidential") # Audit for compliance (EU AI Act, HIPAA, SOX, GDPR, NIST AI, ISO 42001) result = akf.audit("report.akf", regulation="eu_ai_act") print(f"Compliant: {result.compliant}") ``` **TypeScript / Node.js** (`akf-format`): ```typescript import { create, validate, effectiveTrust, stampFile } from 'akf-format'; // Create a trust-stamped unit from any AI output const unit = create('Revenue was $4.2B, up 12% YoY', 0.98, { source: 'SEC 10-Q', agent: 'claude-code', }); // Validate against the AKF schema const { valid } = validate(unit); // Compute effective trust for a claim const trust = effectiveTrust(unit.claims[0]); console.log(`valid: ${valid}, score: ${trust.score}, decision: ${trust.decision}`); // Stamp trust metadata directly into a file (markdown, json, code, …) stampFile('report.md', { agent: 'claude-code', evidence: 'tests pass' }); ``` > Full TypeScript API and more examples: [`typescript/README.md`](typescript/README.md). ## For AI Agents AKF is designed **agent-first**. One-line APIs for stamping, streaming, and auditing. ```python import akf # Stamp with evidence (auto-detected: test_pass, type_check, etc.) akf.stamp("Fixed auth bypass", kind="code_change", evidence=["42/42 tests passed", "mypy: 0 errors"], agent="claude-code", model="claude-sonnet-4-20250514") # Stream trust metadata in real-time with akf.stream("output.md", model="gpt-4o") as s: for chunk in llm_response: s.write(chunk) # Trust-annotated git commits (uses git notes) akf.stamp_commit(content="Refactored auth module", kind="code_change", evidence=["all tests pass"], agent="claude-code") print(akf.trust_log(n=10)) # + ACCEPT ~ LOW - REJECT ? none ``` ## Multi-Agent Teams AKF supports multi-agent orchestration — Claude Agent Teams, Copilot Cowork, Codex multi-agent, and any A2A-compatible platform. ```python import akf # Agent-to-agent delegation with trust ceiling policy = akf.DelegationPolicy( delegator="lead-agent", delegate="research-bot", trust_ceiling=0.7, allowed_actions=["search", "summarize"] ) result = akf.delegate(parent_unit, policy) # Multi-agent streaming session with akf.TeamStream(["research", "writer", "reviewer"]) as ts: ts.write("research", "Found 3 sources", confidence=0.8) ts.write("writer", "Drafted summary", confidence=0.75) ts.write("reviewer", "Approved with edits", confidence=0.9) scores = ts.aggregate() # per-agent + team trust # Cross-platform agent identity card = akf.create_agent_card(name="Research Bot", platform="claude-code", capabilities=["search", "summarize"]) akf.verify_agent_card(card) # SHA-256 hash verification # Team certification (per-agent breakdown) report = akf.certify_team("src/", min_trust=0.7) # report.all_agents_certified — each agent must individually pass ``` **CLI:** ```bash akf agent create --name "Bot" --platform claude-code --capabilities search,summarize akf agent list akf agent verify akf agent export-a2a --output card.json # A2A protocol bridge akf agent import-a2a card.json akf certify src/ --team # Per-agent breakdown ``` ## MCP Server AKF ships an [MCP](https://modelcontextprotocol.io) server so any AI agent can create, validate, scan, and audit trust metadata. ```bash # Install from the repo pip install ./packages/mcp-server-akf ``` ```json { "mcpServers": { "akf": { "command": "python", "args": ["-m", "mcp_server_akf"] } } } ``` **9 MCP tools:** `create_claim` · `validate_file` · `scan_file` · `trust_score` · `stamp_file` · `audit_file` · `embed_file` · `extract_file` · `detect_threats` ## Ambient Trust AKF works where AI agents work. Drop a config file, and every AI-generated file carries trust metadata automatically. | Agent | How it works | |-------|-------------| | **Claude Code** | Reads `CLAUDE.md` — stamps every file it creates with confidence and evidence | | **Cursor** | Reads `.cursorrules` — stamps AI edits before you review | | **Windsurf** | Reads `.windsurfrules` — stamps AI edits with trust metadata | | **GitHub Copilot** | Reads `.github/copilot-instructions.md` (native) + shell hook for CLI | | **OpenAI Codex** | Reads `AGENTS.md` — stamps files in cloud sandbox and local | | **Manus / Other Agents** | MCP server + shell hook — works with any agent that supports MCP or CLI | | **Any MCP agent** | 9 MCP tools — stamp, audit, embed, extract, detect, validate, scan, trust, create | | **Any CLI tool** | `eval "$(akf shell-hook)"` — intercepts `claude`, `chatgpt`, `aider`, `openclaw`, `ollama`, `manus` | **The trust pipeline:** ``` Agent writes code → Git commit stamped → CI runs akf certify → Team reviews with context ``` Set up in 60 seconds: ```bash # 1. Agent stamps its own work (already in this repo) cat CLAUDE.md # or .cursorrules / .windsurfrules / AGENTS.md / .github/copilot-instructions.md # 2. Git hooks stamp every commit akf init --git-hooks # 3. CI certifies trust on every PR # uses: HMAKT99/AKF/extensions/github-action@main # 4. Shell hook intercepts AI CLI tools eval "$(akf shell-hook)" ``` ## Skills AKF provides [agent skill files](skills/) that AI agents can discover and use. Drop these into your agent's context: | Skill | What it does | |-------|-------------| | [`stamp.md`](skills/stamp.md) | Stamp trust metadata onto AI outputs | | [`audit.md`](skills/audit.md) | Audit files for regulatory compliance | | [`scan.md`](skills/scan.md) | Security scan files and directories | | [`embed.md`](skills/embed.md) | Embed trust metadata into Office/PDF/images | | [`detect.md`](skills/detect.md) | Run 10 security detection classes | | [`stream.md`](skills/stream.md) | Stream trust metadata in real-time | | [`git.md`](skills/git.md) | Trust-annotated git workflows | | [`convert.md`](skills/convert.md) | Convert between formats | | `delegate` | Agent-to-agent trust delegation | | `team` | Multi-agent streaming sessions | ## Format at a Glance **Compact** (~15 tokens — optimized for AI): ```json {"v":"1.0","claims":[{"c":"Revenue was $4.2B","t":0.98,"src":"SEC 10-Q"}]} ``` **Descriptive** (human-readable — same data): ```json {"version":"1.0","claims":[{"content":"Revenue was $4.2B","confidence":0.98,"source":"SEC 10-Q"}]} ``` **Full** (with provenance, decay, AI flags, security): ```json {"v":"1.0","by":"sarah@acme.com","label":"confidential","inherit":true, "claims":[ {"c":"Revenue $4.2B","t":0.98,"src":"SEC 10-Q","tier":1,"ver":true,"decay":90}, {"c":"H2 will accelerate","t":0.63,"tier":5,"ai":true,"risk":"AI inference"} ], "prov":[ {"hop":0,"by":"sarah@acme.com","do":"created","at":"2025-07-15T09:30:00Z"}, {"hop":1,"by":"copilot-agent","do":"enriched","at":"2025-07-15T10:15:00Z"} ]} ``` ## Works With Every Format AKF embeds natively — no sidecars needed for most formats: | Format | How It Works | |--------|-------------| | `.akf` | Native standalone knowledge file | | `.docx` `.xlsx` `.pptx` | OOXML custom XML part | | `.pdf` | PDF metadata stream | | `.html` | JSON-LD `` | | `.md` | YAML frontmatter | | `.png` `.jpg` | EXIF/XMP metadata | | `.json` | Reserved `_akf` key | | `.mp4` `.mov` `.webm` `.mkv` | Sidecar `.akf.json` companion | | `.mp3` `.wav` `.flac` `.ogg` | Sidecar `.akf.json` companion | | Everything else | Sidecar `.akf.json` companion | ```python # One API for all formats akf.embed("report.docx", claims=[...], classification="confidential") meta = akf.extract("report.docx") akf.scan("report.docx") ``` ## Zero-Touch Auto-Stamping AKF can automatically stamp every file AI touches — no manual intervention needed. ```bash # Install the background watcher akf install # Or run in foreground akf watch ~/Downloads ~/Desktop ~/Documents ``` The background watcher monitors directories for new and modified files and stamps them with trust metadata. **Smart context detection** automatically infers: - **Git author** — from `git log` history - **Download source** — from macOS extended attributes - **Classification** — from project `.akf/config.json` rules - **AI-generated flag** — from LLM tracking timestamps + content heuristics - **Confidence score** — dynamically adjusted based on available evidence ### Shell Hook (intercept AI CLI tools) ```bash # Add to ~/.zshrc or ~/.bashrc eval "$(akf shell-hook)" ``` Automatically detects when you run `claude`, `chatgpt`, `aider`, `openclaw`, `ollama`, or other AI CLI tools, and stamps any files they create or modify. Also pre-stamps files before upload to content platforms (`gws`, `box`, `m365`, `dbxcli`, `rclone`) so trust metadata travels with the file. Use `--no-upload-hooks` to disable. ### Project Rules Create `.akf/config.json` in your project root: ```json { "rules": [ {"pattern": "*/finance/*", "classification": "confidential", "tier": 2}, {"pattern": "*/public/*", "classification": "public", "tier": 3} ] } ``` Files matching these patterns are automatically classified when stamped. ## CLI ```bash # ── Quick start ── akf # Welcome + quick start akf quickstart # Interactive demo akf doctor # Check installation health # ── Stamp & create ── akf create report.akf \ --claim "Revenue $4.2B" --trust 0.98 --src "SEC 10-Q" \ --by sarah@acme.com --label confidential # ── Validate & inspect ── akf validate report.akf akf inspect report.akf akf trust report.akf # ── Certify (aggregate pass/fail gate) ── akf certify report.akf # Trust + detection + compliance akf certify src/ --min-trust 0.8 # Custom threshold akf certify . --evidence-file results.xml # Attach test evidence akf certify . --format json --fail-on-untrusted # CI-friendly output akf certify src/ --team # Per-agent trust breakdown # ── Compliance ── akf audit report.akf # Compliance readiness check akf audit report.akf --regulation eu_ai_act # EU AI Act akf audit report.akf --trail # Audit trail # ── Universal format commands ── akf embed report.docx --classification confidential \ --claim "Revenue $4.2B" --trust 0.98 akf extract report.docx akf scan report.docx akf scan ./docs/ --recursive # ── Auto-stamping ── akf install # Install background watcher akf watch ~/Downloads ~/Documents # Watch directories akf shell-hook # Print shell hook code akf shell-hook --no-upload-hooks # Without content platform hooks akf uploads # View upload stamp log # ── Git integration ── akf stamp --agent claude-code --evidence "tests pass" # ── Agent identity & teams ── akf agent create --name "Bot" --platform claude-code akf agent list akf agent verify akf agent export-a2a --output card.json # A2A protocol bridge akf agent import-a2a card.json # ── Knowledge Base ── akf kb stats ./kb akf kb query ./kb --topic finance ``` ## Security Detections 10 built-in detection classes: AI content without review, trust below threshold, hallucination risk, knowledge laundering, classification downgrade, stale claims, ungrounded AI claims, trust degradation chain, excessive AI concentration, provenance gap. ```python from akf import run_all_detections report = run_all_detections(unit) for finding in report.findings: print(f"[{finding.severity}] {finding.detection}: {finding.message}") ``` ## Trust Computation ``` effective_trust = confidence × authority_weight × temporal_decay × (1 + penalty) ``` | Tier | Weight | Example | |------|--------|---------| | 1 | 1.00 | SEC filings, official records | | 2 | 0.85 | Analyst reports, peer-reviewed | | 3 | 0.70 | News, industry reports | | 4 | 0.50 | Internal estimates, CRM data | | 5 | 0.30 | AI inference, extrapolations | **Decision:** score ≥ 0.7 → ACCEPT · ≥ 0.4 → LOW · `): | Package | Description | |---------|-------------| | [`mcp-server-akf`](packages/mcp-server-akf/) | MCP server — create, validate, scan, trust | | [`langchain-akf`](packages/langchain-akf/) | LangChain callback handler + document loader (experimental) | | [`llama-index-akf`](packages/llama-index-akf/) | LlamaIndex node parser + trust filter (experimental) | | [`crewai-akf`](packages/crewai-akf/) | CrewAI tool for trust-aware agents (experimental) | **Editor & CI extensions** (source in repo): | Extension | Description | |-----------|-------------| | [VS Code](extensions/vscode/) | Syntax highlighting, hover info, validation for `.akf` files | | [VS Code AI Monitor](editors/vscode/) | Auto-stamp files edited by Copilot, Cursor, and other AI tools | | [GitHub Action](extensions/github-action/) | CI trust gate — runs `akf certify` on PRs with optional PR comments | | [Google Workspace](extensions/google-workspace/) | Add-on for Docs, Sheets, Slides (preview) | | [Office Add-in](extensions/office-addin/) | Add-in for Word, Excel, PowerPoint (preview) | ## For LLMs Prompt with one example and LLMs produce … ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [HMAKT99](https://github.com/HMAKT99) - **Source:** [HMAKT99/AKF](https://github.com/HMAKT99/AKF) - **License:** MIT - **Homepage:** https://akf.dev Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-hmakt99-akf - Seller: https://agentstack.voostack.com/s/hmakt99 - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.