AgentMD Runtime

AgentMD compiles scattered AI/tool runs into one verified current-state packet.

It compiles scattered AI/tool runs into one verified current-state packet, with schema validation, deterministic hashes, and proof receipts.

Proof

• Current public release: v0.3.0-alpha.2
• v0.2.0: AI Work Lead checkpoint
• v0.3.0-alpha.2: CI-verified alpha with gated skill edit primitive
• Published alpha CI status predates the September 2026 security audit
• Local security-hardening branch tests: 36 passed
• Public main remediation and fresh CI proof are required before the next release
• Deterministic demo script: scripts/demo-lead-compile.ps1
• Lead Artifact schema validation is enforced (schemas/lead-artifact.schema.json)
• Lead receipts include chained hashes, source artifact hashes, replay resistance, and advisory trust-weight.v1 metadata
• Source identity is asserted, not authenticated; AgentMD does not convert self-reported status into an authorization score
• Technical proof note: docs/proof_note.md

Trust & adoption docs

• docs/adoption_quickstart.md
• docs/compatibility_policy.md
• docs/cli_exit_codes.md
• docs/determinism.md
• docs/receipt_integrity.md
• docs/threat_model.md
• SECURITY.md

Problem

AI work is fragmented across chats, CLIs, CI logs, scripts, and partial artifacts. Teams lose truth-state when outputs are stale, contradictory, or unverifiable.

AgentMD creates one reproducible trace:

task
  -> validate context
  -> compile current-state packet
  -> enforce schema gates
  -> write proof receipts
  -> preserve audit trail

AI Work Lead

agentmd lead compile turns scattered run artifacts into one verified operating picture with deterministic hashing and proof receipts.

The compiled packet includes:

• what changed
• what is true
• what is unverified
• contradictions
• human approval items
• next clean action

Runtime outputs:

• .sticky/current-state.json
• .sticky/current-state.md
• .sticky/receipts/*.jsonl

Lead Artifact v1

Lead Artifact is the portable input contract for any tool (Codex, Claude, Gemini, ChatGPT, Cursor, CI jobs, scripts).

• Schema: schemas/lead-artifact.schema.json
• Required version field: schema_version: "lead-artifact.v1"
• Strict validation dependency: jsonschema
• Invalid artifacts fail compile with explicit schema errors
• Artifact paths stay inside the workspace and hostile inputs are size/depth bounded
• High-confidence credential patterns fail closed before outputs are written
• Imported text is treated as untrusted data and escaped in Markdown output

Use the portable emitter prompt and template:

• prompts/emit-lead-artifact.md
• examples/lead-artifacts/template.lead-artifact.json

Skill Edit Gate

AgentMD includes a gated SkillOpt-style primitive for controlled skill evolution without runtime optimizer calls.

• Command: agentmd skill apply-edit --edit <path>
• Input schema: schemas/skill-edit.schema.json (skill-edit.v1)
• Bounded edit types only: add, delete, replace
• Skill edits are restricted to workspace-local skills/*/SKILL.md files
• Acceptance policy: validation_score > baseline_score plus a matching hash-verified skill-validation.v1 receipt
• Validation schema: schemas/skill-validation.schema.json
• Accepted edits: .sticky/skill-receipts/*.jsonl
• Rejected edits: .sticky/rejected-skill-edits/*.jsonl

Scope intentionally not implemented yet:

• optimizer model that proposes edits
• multi-epoch benchmark loop
• autonomous self-editing runtime

Core Commands

.\agentmd.cmd doctor
.\agentmd.cmd resolve --task "review this repo for context drift"
.\agentmd.cmd receipt
.\agentmd.cmd run --adapter codex --task "review this repo for context drift"
.\agentmd.cmd lead compile --task "compile ai work lead state" --artifact examples/lead-artifacts/run-codex.jsonl --artifact examples/lead-artifacts/run-claude.json --artifact examples/lead-artifacts/run-gemini.jsonl
.\agentmd.cmd skill apply-edit --edit edits/validated-skill-edit.json

Quickstart

git clone https://github.com/electricwolfemarshmallowhypertext/agentmd-runtime.git
cd agentmd-runtime

python -m pip install -r requirements-cli.txt
python -m pytest -q tests/agentmd/test_agentmd_cli.py
.\scripts\demo-lead-compile.ps1

CI installs from requirements-ci.lock with package hashes and runs dependency and repository security scans. Trust scoring is advisory metadata; it never grants execution permission.

License

AgentMD Runtime is source-available under BUSL-1.1.

Commercial production use, hosted service use, resale, embedding into commercial products, or offering substantially similar functionality as a service requires a commercial license from the Licensor.

See LICENSE, LICENSE.md, and NOTICE.