Install AKF (pip install akf, or pipx install akf) and set it up: run `akf init --git-hooks`.
From now on:
- After you create or modify a file, stamp it:
    akf stamp <file> --agent <your-id> --evidence "<what you verified, e.g. 42/42 tests passed>"
- Before building on any existing file, check it first:
    akf check <file>
  OK = fresh stamp with verified evidence, skip re-verification. STALE/LOW/UNSTAMPED = verify before trusting.
If you support MCP, also register the server: {"mcpServers":{"akf":{"command":"python","args":["-m","mcp_server_akf"]}}}

# Session 1 — agent fixes auth, tests pass
$ akf stamp auth.py --agent claude-code --evidence "42/42 tests passed"

# Session 2 — tomorrow, any agent, any tool
$ akf check auth.py
OK trust=0.65 agent=claude-code evidence=test_pass age=1d claims=1
# → build on it, skip re-verification

# Someone edits auth.py without re-testing
$ akf check auth.py
STALE trust=0.65 agent=claude-code evidence=test_pass age=1d claims=1 reason=modified_after_stamp
# → re-verify before trusting (exit code 1 — gate CI or hooks on it)

import akf

# Check before you trust — can I build on this file without re-verifying?
result = akf.check_file("auth.py")
if result.status == "OK":      # fresh stamp, verified evidence
    ...                        # skip re-verification, save the tokens
# LOW / STALE / UNSTAMPED → verify before trusting

# Stamp with evidence (auto-detected: test_pass, type_check, human_review, 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

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

akf agent create --name "Bot" --platform claude-code --capabilities search,summarize
akf agent list
akf agent verify <id>
akf agent export-a2a <id> --output card.json   # A2A protocol bridge
akf agent import-a2a card.json
akf certify src/ --team                         # Per-agent breakdown

# Install from the repo
pip install ./packages/mcp-server-akf

{
  "mcpServers": {
    "akf": {
      "command": "python",
      "args": ["-m", "mcp_server_akf"]
    }
  }
}

Agent writes code → Git commit stamped → CI runs akf certify → Team reviews with context

# 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)"

{"v":"1.0","claims":[{"c":"Revenue was $4.2B","t":0.98,"src":"SEC 10-Q"}]}

{"version":"1.0","claims":[{"content":"Revenue was $4.2B","confidence":0.98,"source":"SEC 10-Q"}]}

{"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"}
 ]}

# One API for all formats
akf.embed("report.docx", claims=[...], classification="confidential")
meta = akf.extract("report.docx")
akf.scan("report.docx")

# Install the background watcher
akf install

# Or run in foreground
akf watch ~/Downloads ~/Desktop ~/Documents

# Add to ~/.zshrc or ~/.bashrc
eval "$(akf shell-hook)"

{
  "rules": [
    {"pattern": "*/finance/*", "classification": "confidential", "tier": 2},
    {"pattern": "*/public/*", "classification": "public", "tier": 3}
  ]
}

# ── 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

# ── Check before you trust ──
akf check auth.py            # One line: OK / LOW / STALE / UNSTAMPED
akf check auth.py --json     # Structured output; exit codes 0/1/2 for gating

# ── 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
akf scan . --badge badge.json     # shields.io endpoint: "14% stamped · trust 0.76"

# ── 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 <file> --agent claude-code --evidence "tests pass"

# ── Agent identity & teams ──
akf agent create --name "Bot" --platform claude-code
akf agent list
akf agent verify <agent_id>
akf agent export-a2a <id> --output card.json  # A2A protocol bridge
akf agent import-a2a card.json

# ── Knowledge Base ──
akf kb stats ./kb
akf kb query ./kb --topic finance

from akf import run_all_detections
report = run_all_detections(unit)
for finding in report.findings:
    print(f"[{finding.severity}] {finding.detection}: {finding.message}")

effective_trust = confidence × authority_weight × temporal_decay × (1 + penalty)

Output knowledge as AKF:
{"v":"1.0","claims":[{"c":"<claim>","t":<0-1>,"src":"<source>","tier":<1-5>,"ai":true}]}

akf audit report.docx --regulation eu_ai_act

pip install akf    # Python
npm install akf-format    # TypeScript / Node.js

akf doctor         # Check your install — detects PATH issues and guides setup

# The core loop — stamp what you verified, check before you trust
akf stamp auth.py --agent claude-code --evidence "42/42 tests passed"
akf check auth.py        # OK trust=0.65 agent=claude-code evidence=test_pass age=0d

import akf

# Same loop from Python
akf.stamp_file("auth.py", agent="claude-code", evidence=["42/42 tests passed"])
result = akf.check_file("auth.py")
print(result.summary_line())   # OK trust=0.65 agent=claude-code evidence=test_pass age=0d claims=1

# 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}")

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' });