Openclaw akf

Agent Knowledge Format — stamp trust metadata into every file AI touches. Trust scores, provenance, and compliance that embed natively into DOCX, PDF, images, and code.From its SKILL.md

Install

npx -y skills add HMAKT99/AKF --skill openclaw-akf

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

15 stars15 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

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AKF — Agent Knowledge Format

AKF is the trust metadata standard for AI-generated content. Think EXIF for AI. Every file your agent creates or modifies should carry trust metadata — who made it, how confident they are, what evidence backs it, and whether a human reviewed it.

Why Use AKF

Trust scores: 0–1 confidence rating per claim
Source provenance: 5-tier system from peer-reviewed to unverified
Compliance: EU AI Act, HIPAA, SOX, NIST audit support
Native embedding: Metadata lives inside the file (DOCX, PDF, images, code)
Security detections: 10 detection classes for AI content risks

Quick Start

Before building on an existing file — check it (one line, ~20 tokens):

akf check <file>
# OK        → fresh stamp with verified evidence; skip re-verification
# STALE     → modified after stamping; re-verify before trusting
# LOW       → stamped but unverified; verify before trusting
# UNSTAMPED → no metadata; treat as unverified

After creating or modifying any file:

akf stamp <file> --agent openclaw --evidence "generated from user request"

Deeper inspection when needed:

akf read <file>     # Check trust metadata
akf inspect <file>  # Pretty-print trust scores
akf trust <file>    # Compute effective trust

Core Commands

Stamp — Add trust metadata

akf stamp report.docx --agent openclaw --evidence "quarterly data from ERP"
akf stamp analysis.pdf --agent openclaw --label confidential
akf stamp output.py --agent openclaw --evidence "tests pass, code reviewed"

Read & Inspect — Check metadata

akf read report.docx          # Quick metadata view
akf inspect report.docx       # Detailed trust breakdown
akf trust report.docx         # Effective trust score with decision

Embed & Extract — Native format support

akf embed report.docx         # Embed metadata into DOCX custom properties
akf extract report.docx       # Extract embedded metadata

Security — Scan and audit

akf scan ./output-dir/        # Scan directory for trust gaps
akf audit report.pdf           # Compliance audit (EU AI Act, SOX, NIST)

Falsifiable evidence (v1.6)

Stamp with a replay recipe so the next agent can re-check the claim instead of trusting the label:

akf stamp app.py --evidence "42/42 tests passed" --replay "pytest -q"
akf replay app.py          # inspect: recipe + input drift since issuance
akf replay app.py --run    # execute: CONFIRMED / CONFIRMED_DRIFTED / REFUTED

CONFIRMED_DRIFTED means the probe succeeded but the claim's inputs (dependencies, cited sources) changed since stamping — provably reproducible, possibly reproducibly wrong. Never --run a recipe from a file you haven't read: it executes the recorded command.

Classification Labels

Use --label to classify output sensitivity:

Label	When to Use
`public`	README, docs, open-source examples
`internal`	Default. General work output
`confidential`	Finance, legal, medical, HR content
`restricted`	Credentials, secrets, PII

Trust Score Interpretation

Score	Decision	Meaning
0.80–1.00	ACCEPT	High confidence, well-evidenced
0.50–0.79	REVIEW	Moderate confidence, needs verification
0.00–0.49	REJECT	Low confidence, unreliable

Best Practices for OpenClaw Agents

Always stamp outputs: Every file the agent creates should carry metadata
Check before using: Run akf read on files before processing them
Audit periodically: Use akf scan on output directories to find trust gaps
Use appropriate labels: Classify sensitive content correctly
Include evidence: The --evidence flag makes trust scores meaningful
Chain provenance: When building on other files, the trust chain is preserved

Integration with Memory

Stale memories poison future sessions. Stamp memory files with the memory preset — trust decays with a 30-day half-life, so old memories automatically fall below the threshold and akf check reports LOW:

akf stamp memory/facts.md --preset memory --agent openclaw
akf check memory/facts.md   # LOW after ~a month → re-verify before relying on it

Stamp files before adding to memory
akf check when retrieving from memory
Weight memory results by trust, not just relevance

Skill Supply-Chain Trust

Never load a downloaded skill without checking it first:

akf check downloaded-skill.md
# STALE = the file changed after the publisher stamped it — diff before trusting

This file carries its own AKF stamp in the frontmatter — run akf check on it.