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9 commits

Author SHA1 Message Date
25b36e9ffb wedge: US2 wave converged — T027 publish cmd, T029/T030 seeds (schema-valid, scrubbed); T031 blocked on operator
t031 worker found ZERO real competitor-scan evidence and correctly built
nothing (constitution III). Recommended substitute: Parlobyg email-triage
(real production run 2026-03-15..04-04) — tenant-derived, requires operator
owner-initiation + double scrub (constitution IV). Recorded as open question.
Tenant-named hosts scrubbed from agent-scaffold evidence. 29 tests green.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-02 03:51:27 +02:00
51694acc64 chore: drop remaining tracked pyc
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-02 03:50:35 +02:00
3886ee8480 merge: seed-agent-scaffold (US2) 2026-07-02 03:50:25 +02:00
5e2dd7aac3 merge: seed-mmpdf (US2) 2026-07-02 03:50:25 +02:00
178f373fe1 merge: publish-cmd (US2, pycache conflicts dropped) 2026-07-02 03:50:25 +02:00
91ca883ccb chore: untrack __pycache__/pyc/uv.lock, extend gitignore
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-02 03:50:25 +02:00
362fd2e5b3 T030: seed solution agent-scaffold 2026-07-02 03:47:09 +02:00
430061c0ff T029: seed solution mm-pdf-report 2026-07-02 03:42:12 +02:00
82393ccdc5 T027: publish command (validate -> branch -> release -> PR)
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
2026-07-02 03:38:59 +02:00
45 changed files with 1370 additions and 626 deletions

6
.gitignore vendored
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@ -1 +1,7 @@
.m2herd/
__pycache__/
*.pyc
.venv/
uv.lock
.pytest_cache/

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@ -18,6 +18,7 @@ from m2_market.catalog import CatalogClient
from m2_market.config import Config, ConfigError, load_config
from m2_market.ledger import InsufficientFunds, LedgerClient
from m2_market.output import emit_json, format_price, render_kv, render_table, search_results_table
from m2_market.publish import PublishValidationError, publish_bundle, validate_bundle
from m2_market.registry import RegistryClient, RegistryError
# Exit-code map (contracts/cli.md).
@ -304,8 +305,25 @@ def wallet(ctx: click.Context, operator: str | None) -> None:
@click.argument("bundle_dir")
@click.pass_context
def publish(ctx: click.Context, bundle_dir: str) -> None:
"""Validate a bundle and open a listing PR on the registry."""
_not_implemented(ctx, "publish")
"""Validate a bundle and open a listing PR on the registry (Story 2 step 1-2)."""
config: Config = ctx.obj["config"]
json_output = ctx.obj["json"]
try:
solution, listing = validate_bundle(Path(bundle_dir))
except PublishValidationError as exc:
for line in exc.errors:
click.echo(f"m2-market publish: {line}", err=True)
ctx.exit(EXIT_VALIDATION)
return
with RegistryClient(config.forgejo_url, config.forgejo_token) as registry:
pr_url = publish_bundle(Path(bundle_dir), solution, listing, registry)
if json_output:
emit_json({"status": "ok", "pr_url": pr_url})
else:
click.echo(pr_url)
@main.command()

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@ -0,0 +1,181 @@
"""Publish command implementation (contracts/cli.md publish, contracts/registry-layout.md).
Validates a bundle against the frozen schemas (schemas/*.json), packages
payload/+recipe.yaml+solution.json into a release tarball, and runs the
publish protocol against the registry: branch -> commit -> release -> PR.
"""
from __future__ import annotations
import hashlib
import io
import json
import os
import tarfile
import tempfile
from pathlib import Path
from jsonschema import Draft202012Validator
from m2_market.registry import RegistryClient
class PublishValidationError(Exception):
"""Raised when the bundle fails schema validation (contracts/cli.md exit 5)."""
def __init__(self, errors: list[str]):
super().__init__("; ".join(errors))
self.errors = errors
def schemas_dir() -> Path:
"""Resolve the frozen schemas dir, honoring the M2_MARKET_SCHEMAS_DIR override."""
override = os.environ.get("M2_MARKET_SCHEMAS_DIR")
if override:
return Path(override)
return Path(__file__).resolve().parents[3] / "schemas"
def _load_json(path: Path) -> dict:
with path.open(encoding="utf-8") as f:
return json.load(f)
def _validate(instance: dict, schema_path: Path, label: str) -> list[str]:
schema = _load_json(schema_path)
validator = Draft202012Validator(schema)
errors = []
for err in sorted(validator.iter_errors(instance), key=lambda e: list(map(str, e.path))):
loc = "/".join(str(p) for p in err.path) or "<root>"
errors.append(f"{label}: {loc}: {err.message}")
return errors
def validate_bundle(bundle_dir: Path) -> tuple[dict, dict]:
"""Validate BUNDLE_DIR/solution.json + listing.json against the frozen schemas.
Returns (solution, listing) on success; raises PublishValidationError
(one message per violation, including tenant-firewall rules encoded in
solution.schema.json's if/else) otherwise.
"""
solution_path = bundle_dir / "solution.json"
listing_path = bundle_dir / "listing.json"
errors: list[str] = []
solution: dict | None = None
listing: dict | None = None
try:
solution = _load_json(solution_path)
except (OSError, json.JSONDecodeError) as exc:
errors.append(f"solution.json: {exc}")
try:
listing = _load_json(listing_path)
except (OSError, json.JSONDecodeError) as exc:
errors.append(f"listing.json: {exc}")
if errors:
raise PublishValidationError(errors)
schemas = schemas_dir()
errors += _validate(solution, schemas / "solution.schema.json", "solution.json")
errors += _validate(listing, schemas / "listing.schema.json", "listing.json")
if errors:
raise PublishValidationError(errors)
return solution, listing
def build_bundle_tarball(bundle_dir: Path, solution: dict, dest_path: Path) -> str:
"""Tar payload/+recipe.yaml+solution.json into dest_path; return its sha256:<hex> hash."""
payload_dir = bundle_dir / "payload"
recipe_path = bundle_dir / "recipe.yaml"
with tarfile.open(dest_path, "w:gz") as tar:
if payload_dir.is_dir():
tar.add(payload_dir, arcname="payload")
if recipe_path.is_file():
tar.add(recipe_path, arcname="recipe.yaml")
solution_bytes = json.dumps(solution, indent=2, sort_keys=True).encode("utf-8")
info = tarfile.TarInfo(name="solution.json")
info.size = len(solution_bytes)
tar.addfile(info, io.BytesIO(solution_bytes))
digest = hashlib.sha256()
with dest_path.open("rb") as fh:
for chunk in iter(lambda: fh.read(65536), b""):
digest.update(chunk)
return f"sha256:{digest.hexdigest()}"
def render_pr_body(solution: dict, listing: dict) -> str:
"""PR template body (registry-layout.md publish protocol step 2)."""
permissions = solution.get("permissions") or []
permissions_lines = "\n".join(f"- `{perm}`" for perm in permissions) or "- (none)"
price = solution.get("price") or {}
tenant_scope = solution.get("tenant_scope", "-")
body = (
f"## Evidence summary\n{listing.get('evidence_summary', '-')}\n\n"
f"## Price\n{price.get('amount', '-')} {price.get('currency', '-')}\n\n"
f"## Permissions\n{permissions_lines}\n\n"
f"## Rollback\nRe-run `m2-market install` after uninstall; the apply adapter "
f"restores pre-install backups (contracts/apply-adapter.md).\n"
)
if tenant_scope != "m2-core":
owner_initiated = bool(solution.get("owner_initiated"))
double_scrubbed = bool((solution.get("scrub_status") or {}).get("double_scrubbed"))
body += (
"\n## Tenant-derived attestations\n"
f"- [{'x' if owner_initiated else ' '}] owner_initiated\n"
f"- [{'x' if double_scrubbed else ' '}] double_scrubbed\n"
)
return body
def publish_bundle(bundle_dir: Path, solution: dict, listing: dict, registry: RegistryClient) -> str:
"""Run the publish protocol (branch -> commit -> release -> PR); returns the PR URL."""
listing_id = listing["listing_id"]
branch = f"listing/{listing_id}"
install_ref = listing["install_ref"]
with tempfile.TemporaryDirectory(prefix="m2-market-publish-") as tmp_dir_str:
tarball_path = Path(tmp_dir_str) / f"{install_ref}.tar.gz"
content_hash = build_bundle_tarball(bundle_dir, solution, tarball_path)
solution = dict(solution)
solution["content_hash"] = content_hash
registry.create_branch(branch)
registry.create_file(
f"listings/{listing_id}/listing.json",
json.dumps(listing, indent=2, sort_keys=True).encode("utf-8") + b"\n",
message=f"listing({listing_id}): add listing.json",
branch=branch,
)
registry.create_file(
f"listings/{listing_id}/solution.json",
json.dumps(solution, indent=2, sort_keys=True).encode("utf-8") + b"\n",
message=f"listing({listing_id}): add solution.json",
branch=branch,
)
release = registry.create_release(
tag_name=install_ref,
target_commitish=branch,
name=install_ref,
body=f"Bundle release for {listing_id}",
)
registry.upload_release_asset(release["id"], tarball_path.name, tarball_path.read_bytes())
pr = registry.create_pull_request(
title=f"listing: {listing.get('name', listing_id)} ({listing_id})",
body=render_pr_body(solution, listing),
head=branch,
base="main",
)
return pr["html_url"]

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@ -84,6 +84,56 @@ class RegistryClient:
return dest_path
def create_branch(self, new_branch: str, old_branch: str = "main") -> dict:
resp = self._client.post(
self._repo_api("/branches"),
json={"new_branch_name": new_branch, "old_branch_name": old_branch},
)
resp.raise_for_status()
return resp.json()
def create_file(self, repo_path: str, content: bytes, message: str, branch: str) -> dict:
resp = self._client.post(
self._repo_api(f"/contents/{repo_path}"),
json={
"content": base64.b64encode(content).decode("ascii"),
"message": message,
"branch": branch,
},
)
resp.raise_for_status()
return resp.json()
def create_release(self, tag_name: str, target_commitish: str, name: str, body: str) -> dict:
resp = self._client.post(
self._repo_api("/releases"),
json={
"tag_name": tag_name,
"target_commitish": target_commitish,
"name": name,
"body": body,
},
)
resp.raise_for_status()
return resp.json()
def upload_release_asset(self, release_id: int, filename: str, content: bytes) -> dict:
resp = self._client.post(
self._repo_api(f"/releases/{release_id}/assets"),
params={"name": filename},
files={"attachment": (filename, content, "application/gzip")},
)
resp.raise_for_status()
return resp.json()
def create_pull_request(self, title: str, body: str, head: str, base: str = "main") -> dict:
resp = self._client.post(
self._repo_api("/pulls"),
json={"title": title, "body": body, "head": head, "base": base},
)
resp.raise_for_status()
return resp.json()
@staticmethod
def verify_bundle(path: str | Path, content_hash: str) -> bool:
algo, _, expected = content_hash.partition(":")

161
cli/tests/test_publish.py Normal file
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@ -0,0 +1,161 @@
"""Inline verification for the publish CLI command (contracts/cli.md, registry-layout.md).
Covers: (1) invalid bundle -> schema errors printed, exit 5, before any registry
call; (2) valid bundle -> happy-path branch/commit/release/PR sequence against
a MockTransport fake of the Forgejo API.
"""
from __future__ import annotations
import json
import httpx
import pytest
from click.testing import CliRunner
import m2_market.cli as cli_mod
LISTING_ID = "lst_mm-pdf-report"
SOLUTION_ID = "sol_mm-pdf-report"
INSTALL_REF = "lst_mm-pdf-report-v1.0.0"
SELLER = "sdjs-operator"
VALID_SOLUTION = {
"schema_version": "m2.solution.v1",
"solution_id": SOLUTION_ID,
"name": "MM PDF Branded Report",
"summary": "Generate branded PDF reports from Markdown",
"intent": "Produce publish-ready branded PDF output without manual design work",
"deployment": {
"recipe_ref": "recipes/mm-pdf-report/recipe.yaml",
"entrypoint": "skills/mm-pdf/render.py",
"verify_command": "python skills/mm-pdf/render.py --self-test",
},
"applicability": {"image_classes": ["desktop-agent"]},
"tenant_scope": "m2-core",
"evidence": [{"source": "session:2026-05-11-mm-pdf-branded-report"}],
"content_hash": "sha256:aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa",
"price": {"amount": 500, "currency": "m2cr", "model": "fixed"},
"seller": SELLER,
"license": {"terms": "internal", "major_version_coverage": True},
"permissions": [{"scope": "filesystem:write", "path": "/tmp/mm-pdf-output"}],
}
VALID_LISTING = {
"schema_version": "m2.listing.v1",
"listing_id": LISTING_ID,
"solution_id": SOLUTION_ID,
"solution_version": "1.0.0",
"inventory_type": "solution",
"name": "MM PDF Branded Report",
"summary": "Generate branded PDF reports from Markdown",
"category": "reporting",
"price": {"amount": 500, "currency": "m2cr", "model": "fixed"},
"seller": SELLER,
"evidence_summary": "Generated branded PDF report; operator confirmed styling matched brand deck.",
"tenant_visibility": ["*"],
"stats": {"installs": 0, "proposals_shown": 0, "proposals_accepted": 0},
"status": "draft",
"install_ref": INSTALL_REF,
}
def _write_bundle(bundle_dir, solution=None, listing=None):
bundle_dir.mkdir(parents=True, exist_ok=True)
(bundle_dir / "solution.json").write_text(json.dumps(solution if solution is not None else VALID_SOLUTION))
(bundle_dir / "listing.json").write_text(json.dumps(listing if listing is not None else VALID_LISTING))
payload_dir = bundle_dir / "payload"
payload_dir.mkdir(exist_ok=True)
(payload_dir / "report.md").write_text("# hello\n")
(bundle_dir / "recipe.yaml").write_text("targets: []\nchecks: []\n")
return bundle_dir
@pytest.fixture
def config_env(tmp_path, monkeypatch):
config_path = tmp_path / "config.toml"
config_path.write_text(
"""
operator_id = "buyer1"
tenant = "test-tenant"
ledger_url = "https://ledger.test"
ledger_api_key = "lk"
memory_api_url = "https://catalog.test"
memory_api_key = "mk"
forgejo_url = "https://registry.test"
forgejo_token = "rt"
"""
)
monkeypatch.setenv("M2_MARKET_CONFIG", str(config_path))
monkeypatch.setenv("M2_MARKET_SCHEMAS_DIR", str(_repo_schemas_dir()))
return config_path
def _repo_schemas_dir():
from pathlib import Path
return Path(__file__).resolve().parents[2] / "schemas"
def test_invalid_bundle_fails_validation_before_any_network_call(tmp_path, config_env, monkeypatch):
def fail_if_called(*args, **kwargs):
raise AssertionError("registry must not be contacted when validation fails")
monkeypatch.setattr(httpx.Client, "post", fail_if_called)
bad_solution = dict(VALID_SOLUTION)
bad_solution["evidence"] = [] # violates evidence minItems (Principle III evidence-backed listings)
bundle_dir = _write_bundle(tmp_path / "bundle", solution=bad_solution)
runner = CliRunner()
result = runner.invoke(cli_mod.main, ["publish", str(bundle_dir)])
assert result.exit_code == cli_mod.EXIT_VALIDATION, result.output
assert "evidence" in result.output
def test_publish_happy_path_opens_pr(tmp_path, config_env, monkeypatch):
calls = {"branch": 0, "files": [], "release": 0, "asset": 0, "pr": 0}
def handler(request: httpx.Request) -> httpx.Response:
path = request.url.path
if path.endswith("/branches") and request.method == "POST":
calls["branch"] += 1
return httpx.Response(201, json={"name": f"listing/{LISTING_ID}"})
if "/contents/" in path and request.method == "POST":
calls["files"].append(path)
return httpx.Response(201, json={"content": {"path": path}})
if path.endswith("/releases") and request.method == "POST":
calls["release"] += 1
return httpx.Response(201, json={"id": 42, "tag_name": INSTALL_REF})
if path.endswith("/releases/42/assets") and request.method == "POST":
calls["asset"] += 1
return httpx.Response(201, json={"name": f"{INSTALL_REF}.tar.gz"})
if path.endswith("/pulls") and request.method == "POST":
calls["pr"] += 1
return httpx.Response(
201,
json={"html_url": f"https://registry.test/m2/market-registry/pulls/7"},
)
raise AssertionError(f"unexpected request: {request.method} {request.url}")
orig_init = httpx.Client.__init__
def patched_init(self, *args, **kwargs):
kwargs["transport"] = httpx.MockTransport(handler)
orig_init(self, *args, **kwargs)
monkeypatch.setattr(httpx.Client, "__init__", patched_init)
bundle_dir = _write_bundle(tmp_path / "bundle")
runner = CliRunner()
result = runner.invoke(cli_mod.main, ["publish", str(bundle_dir)])
assert result.exit_code == 0, result.output
assert "https://registry.test/m2/market-registry/pulls/7" in result.output
assert calls["branch"] == 1
assert len(calls["files"]) == 2
assert calls["release"] == 1
assert calls["asset"] == 1
assert calls["pr"] == 1

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@ -0,0 +1,26 @@
{
"schema_version": "m2.listing.v1",
"listing_id": "lst_agent-scaffold",
"solution_id": "sol_agent-scaffold",
"solution_version": "1.0.0",
"inventory_type": "solution",
"name": "Agent Scaffold Generator",
"summary": "Scaffold OpenClaw/Hermes agent workspaces (PRD.md + SOUL.md + MEMORY.md) from m2-memory context.",
"category": "agent-tooling",
"keywords": ["agent", "scaffold", "openclaw", "hermes", "prd", "soul", "memory"],
"price": {
"amount": 60,
"currency": "m2cr",
"model": "fixed"
},
"seller": "sdjs-operator",
"evidence_summary": "Skill in production use on the m2 host since 2026-04-20, extracted from a real workflow: four PRD.md/SOUL.md agent workspaces at /home/m2/gmi-clinic/agents/ (GMI Clinic fleet deployment) created within a minute of SKILL.md's own mtime, plus three more SOUL.md digital-twin identities at /home/m2/agent-restore-harness/agents/ from 2026-04-22/23 confirming continued production use. Requires buyer-side memory-api access for full function.",
"tenant_visibility": ["*"],
"stats": {
"installs": 0,
"proposals_shown": 0,
"proposals_accepted": 0
},
"status": "draft",
"install_ref": "lst_agent-scaffold-v1.0.0"
}

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@ -0,0 +1,41 @@
---
name: agent-scaffold
description: Scaffolds OpenClaw/Hermes agent workspaces from m2-memory. Query memory for context, generate PRD.md + SOUL.md for a new agent, and optionally wire into the fleet.
---
# agent-scaffold
Generate a complete agent workspace (PRD + SOUL + MEMORY seed) from context in m2-memory.
## Usage
```
/agent-scaffold <agent-name> "<what this agent does>"
```
Examples:
```
/agent-scaffold gesy-rates-fetcher "Fetches and monitors GESY reimbursement rate updates for GMI Clinic"
/agent-scaffold platform-unifier "Maps and unifies the 3-5 GMI Clinic software platforms into Machine.Machine"
/agent-scaffold nasr-research-runner "Runs parallel deep research tasks for Nasr's healthcare domain work"
```
## What it produces
For each agent, the skill:
1. **Queries m2-memory** for relevant context (entity/project history, related agents, decisions)
2. **Generates `PRD.md`** — problem, personas, functional requirements, success metrics, open items
3. **Generates `SOUL.md`** — agent identity, values, communication style, scope boundaries
4. **Generates `MEMORY.md`** — seed memories pre-loaded from m2-memory search results
5. **Prints a deploy snippet**`docker run` or Coolify env vars to spawn the agent
## Output location
`~/agents/<agent-name>/` — commit to `git.machinemachine.ai/machine.machine/specs/` when ready.
## Design principle
The goal is reproducibility: if an agent's context is lost (like Nasr's Apr 8 research agents),
this skill can reconstruct the workspace from the vector store and hand it to OpenClaw or Hermes
to re-execute without starting from scratch.

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@ -0,0 +1,162 @@
#!/usr/bin/env python3
"""
agent-scaffold generate PRD.md + SOUL.md + MEMORY.md for a new agent
from m2-memory context.
Usage:
python3 scaffold.py <agent-name> "<description>"
python3 scaffold.py gesy-rates-fetcher "Monitors GESY rates for GMI Clinic DRG billing"
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import urllib.request
from pathlib import Path
MEMORY_API = os.environ.get("M2_MEMORY_API_URL", "http://172.18.0.20:8000")
AGENT_ID = os.environ.get("M2_MEMORY_AGENT_ID", "m2")
API_KEY = os.environ.get("M2_MEMORY_API_KEY")
OUT_BASE = Path.home() / "agents"
def search_memory(query: str, limit: int = 10) -> list[dict]:
url = f"{MEMORY_API}/memory/search"
body = json.dumps({
"query": query,
"agent_id": AGENT_ID,
"routing_strategy": "deep",
"limit": limit,
}).encode()
headers = {"Content-Type": "application/json"}
if API_KEY:
headers["X-API-Key"] = API_KEY
req = urllib.request.Request(url, data=body, headers=headers, method="POST")
with urllib.request.urlopen(req, timeout=30) as r:
return json.loads(r.read()).get("results", [])
def fmt_memory_block(results: list[dict]) -> str:
lines = []
for r in results:
ts = (r.get("timestamp") or "")[:10]
score = r.get("score", 0)
content = (r.get("content") or "").replace("\n", " ")[:200]
lines.append(f"- [{ts} score={score:.2f}] {content}")
return "\n".join(lines)
def generate_prd(name: str, description: str, memories: list[dict]) -> str:
mem_block = fmt_memory_block(memories)
return f"""# {name.replace("-", " ").title()} — PRD
**Agent ID:** {name}
**Description:** {description}
**Generated from:** m2-memory search ({len(memories)} relevant memories)
---
## Problem
> TODO: Refine based on memory context below
{description}
## Relevant Memory Context
{mem_block}
## Personas
| Persona | Goal |
|---------|------|
| TODO | TODO |
## Functional Requirements
1. TODO derived from memory context above
2.
3.
## Success Metrics
- TODO
## Open Items
- TODO check memory for unresolved gaps
## Technical Notes
- Agent runtime: OpenClaw / Hermes
- Memory: m2-memory (agent_id={name})
- Deploy: Coolify stack on Machine.Machine fleet
"""
def generate_soul(name: str, description: str) -> str:
title = name.replace("-", " ").title()
return f"""# {title} — Agent Identity
You are **{title}**, part of the Machine.Machine agent fleet.
{description}
## Values
- TODO: define 3 core values for this agent
## Communication style
- TODO: define tone and output format
## Scope boundaries
- You do not TODO
- You do not TODO
"""
def generate_memory_seed(memories: list[dict]) -> str:
lines = ["# Seed Memories\n",
"These memories are pre-loaded from m2-memory at agent creation time.\n"]
for r in memories[:10]:
ts = (r.get("timestamp") or "")[:19]
mtype = r.get("memory_type", "semantic")
content = (r.get("content") or "").strip()[:500]
lines.append(f"## [{mtype} {ts}]\n{content}\n")
return "\n".join(lines)
def main():
p = argparse.ArgumentParser()
p.add_argument("name", help="agent slug (e.g. gesy-rates-fetcher)")
p.add_argument("description", help="one-line description of what the agent does")
p.add_argument("--out", default=None, help="output directory (default: ~/agents/<name>)")
p.add_argument("--limit", type=int, default=10, help="memory search limit")
args = p.parse_args()
out_dir = Path(args.out) if args.out else OUT_BASE / args.name
out_dir.mkdir(parents=True, exist_ok=True)
print(f"Searching m2-memory for: {args.description}", file=sys.stderr)
try:
memories = search_memory(args.description, args.limit)
print(f" Found {len(memories)} relevant memories", file=sys.stderr)
except Exception as e:
print(f" Memory search failed ({e}) — generating without context", file=sys.stderr)
memories = []
(out_dir / "PRD.md").write_text(generate_prd(args.name, args.description, memories))
(out_dir / "SOUL.md").write_text(generate_soul(args.name, args.description))
(out_dir / "MEMORY.md").write_text(generate_memory_seed(memories))
print(f"\nScaffolded: {out_dir}")
print(f" PRD.md — {(out_dir / 'PRD.md').stat().st_size} bytes")
print(f" SOUL.md — {(out_dir / 'SOUL.md').stat().st_size} bytes")
print(f" MEMORY.md — {(out_dir / 'MEMORY.md').stat().st_size} bytes")
print(f"\nNext: review + fill TODOs, then commit to git.machinemachine.ai/machine.machine/specs/")
if __name__ == "__main__":
main()

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# agent-scaffold install recipe (apply-adapter.md bundle layout, local adapter v1)
# Places the agent-scaffold skill into the buyer's Claude Code skills directory.
targets:
- src: SKILL.md
dest: ~/.claude/skills/agent-scaffold/SKILL.md
- src: scripts/scaffold.py
dest: ~/.claude/skills/agent-scaffold/scripts/scaffold.py
checks:
- cmd: test -f ~/.claude/skills/agent-scaffold/SKILL.md
expect_exit: 0
- cmd: test -x ~/.claude/skills/agent-scaffold/scripts/scaffold.py
expect_exit: 0
entrypoint: "Ask your agent to use the agent-scaffold skill, or run: python3 ~/.claude/skills/agent-scaffold/scripts/scaffold.py <agent-name> \"<description>\""

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{
"schema_version": "m2.solution.v1",
"solution_id": "sol_agent-scaffold",
"name": "Agent Scaffold Generator",
"summary": "Scaffold OpenClaw/Hermes agent workspaces (PRD.md + SOUL.md + MEMORY.md) from m2-memory context.",
"description": "Installs the agent-scaffold skill: given an agent name and a one-line description, it queries m2-memory (agent.memory.system / memory-api) for relevant context and generates a complete agent workspace — PRD.md (problem, personas, functional requirements, success metrics), SOUL.md (identity, values, communication style, scope boundaries), and MEMORY.md (seed memories pre-loaded from the search results) — plus a deploy snippet for OpenClaw or Hermes. Ships two artifacts: SKILL.md and scripts/scaffold.py (a single-file Python script using only the stdlib, talking HTTP to memory-api). Requirement: the buyer machine needs memory-api (m2-memory) network access for full function — scripts/scaffold.py calls M2_MEMORY_API_URL (default http://172.18.0.20:8000, override via env) at /memory/search. Without reachable memory-api the script still runs and produces PRD.md/SOUL.md, but with an empty 'Relevant Memory Context' section and no MEMORY.md content — degraded, not blocked. content_hash is computed over payload/ + recipe.yaml only (not solution.json itself, to avoid the self-referential hash problem of hashing a file that contains its own hash): sha256 of `tar --sort=name --mtime='@0' --owner=0 --group=0 --numeric-owner -czf - payload recipe.yaml` run from this bundle's root.",
"intent": "Give an operator a reproducible way to stand up (or reconstruct) a new agent's working context — PRD + identity + seed memory — from what the fleet's memory system already knows, instead of starting from a blank page.",
"behavior": {
"skill_ref": "payload/SKILL.md"
},
"tools": [
{
"name": "memory-api",
"kind": "http-api",
"required": true
},
{
"name": "python3",
"kind": "cli",
"required": true
}
],
"runtime": {
"surfaces": [
"claude-code-skill"
]
},
"permissions": [
{
"action": "write",
"target": "~/.claude/skills/agent-scaffold/"
},
{
"action": "write",
"target": "~/agents/<agent-name>/ (PRD.md, SOUL.md, MEMORY.md — output of running the skill, not part of the install)"
},
{
"action": "net",
"target": "HTTP POST to the configured M2_MEMORY_API_URL /memory/search endpoint"
}
],
"deployment": {
"recipe_ref": "recipe.yaml",
"entrypoint": "Ask your agent to use the agent-scaffold skill, or run: python3 ~/.claude/skills/agent-scaffold/scripts/scaffold.py <agent-name> \"<description>\"",
"verify_command": "test -f ~/.claude/skills/agent-scaffold/SKILL.md"
},
"applicability": {
"image_classes": [
"primus",
"agent-latest"
],
"roles": [
"operator",
"desktop-agent"
],
"tool_requirements": [
"python3",
"memory-api"
]
},
"tenant_scope": "m2-core",
"evidence": [
{
"source": "/home/m2/.claude/skills/agent-scaffold/ (SKILL.md, scripts/scaffold.py) — the proven skill this bundle packages, present on the m2 host since 2026-04-20 (SKILL.md mtime 2026-04-20T22:48:00+02:00, scripts/scaffold.py mtime 2026-04-28T00:33:10+02:00 — a later revision of the same script)",
"machine": "m2",
"excerpt": "SKILL.md frontmatter: 'Scaffolds OpenClaw/Hermes agent workspaces from m2-memory. Query memory for context, generate PRD.md + SOUL.md for a new agent, and optionally wire into the fleet.'"
},
{
"source": "/home/m2/gmi-clinic/agents/{admin-data-bridge,clinical-entity-extraction,drg-los-optimizer}/{PRD.md,SOUL.md} — three real PRD+SOUL agent-workspace pairs on disk, all created 2026-04-20 22:46:2722:47:19 (mtimes), roughly one minute before this skill's own SKILL.md mtime (22:48:00) — the exact PRD.md/SOUL.md workspace shape this skill formalizes and automates, produced for the GMI Clinic AI fleet deployment immediately prior to the skill being written",
"machine": "m2",
"excerpt": "admin-data-bridge/SOUL.md: 'You are the Admin Data Bridge for GMI Clinic... ## Values ## Communication style ## Scope boundaries' — same section structure generate_soul() in scaffold.py emits"
},
{
"source": "/home/m2/gmi-clinic/agents/gesy-research/PRD.md — a fourth real workspace, mtime 2026-04-20T22:47:48+02:00, whose own text names the exact incident SKILL.md's 'Design principle' section cites as the skill's reason for existing: 'On 2026-04-08, Nasr ran 4 parallel research agents on his machine... The output was intended to be stored in m2-memory, Planka, and Forgejo but the storage step failed (m2 errored). This PRD reconstructs the agent's purpose and gaps so the research can be completed and properly stored.'",
"machine": "m2",
"excerpt": "SKILL.md: 'The goal is reproducibility: if an agent's context is lost (like Nasr's Apr 8 research agents), this skill can reconstruct the workspace from the vector store and hand it to OpenClaw or Hermes to re-execute without starting from scratch.'"
},
{
"source": "/home/m2/agent-restore-harness/agents/*/SOUL.md — three further real SOUL.md agent-identity files for actual named fleet agents (machine names withheld from shared catalog per tenant-firewall hygiene), mtimes 2026-04-22T23:412026-04-23T00:48, confirming the PRD+SOUL workspace pattern stayed in production use after the skill was written, not a one-off",
"machine": "m2",
"excerpt": "nasr/SOUL.md: 'You are Nasr's digital twin — his AI counterpart in the Machine.Machine fleet... ## Core Capabilities'"
}
],
"content_hash": "sha256:202aaff5519e774e2669bd2ca6220fc67284a506775a90ff7710fb2361cc58ee",
"price": {
"amount": 60,
"currency": "m2cr",
"model": "fixed"
},
"seller": "sdjs-operator",
"license": {
"terms": "Perpetual use on operator-owned machines for the buyer's own agent-workspace generation; no redistribution of the skill files as a standalone product.",
"major_version_coverage": true
},
"revenue_split": {
"platform_pct": 10
}
}

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{
"schema_version": "m2.listing.v1",
"listing_id": "lst_mm-pdf-report",
"solution_id": "sol_mm-pdf-report",
"solution_version": "1.0.0",
"inventory_type": "solution",
"name": "MM PDF Report Generator",
"summary": "Generate Machine.Machine branded PDF reports and slide decks from Markdown.",
"category": "document-generation",
"keywords": ["pdf", "branding", "markdown", "reports", "decks"],
"price": {
"amount": 40,
"currency": "m2cr",
"model": "fixed"
},
"seller": "sdjs-operator",
"evidence_summary": "Skill in production use on the m2 host since 2026-04-21; two real generated PDF/HTML pairs on disk (session-2026-04-20.pdf, spark-cluster-old/fabric-setup.pdf) confirm the pipeline works end-to-end.",
"tenant_visibility": ["*"],
"stats": {
"installs": 0,
"proposals_shown": 0,
"proposals_accepted": 0
},
"status": "draft",
"install_ref": "lst_mm-pdf-report-v1.0.0"
}

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---
name: mm-pdf
description: Generate beautifully styled Machine.Machine branded PDFs and slide decks from Markdown. Dark navy aesthetic, cyan accents, professional A4 layout. Uses a headless-Chrome render container (any container with Node + Chrome, e.g. a "*-m2o" agent-desktop image) or a local WeasyPrint fallback. Produces PDF + HTML output.
---
# mm-pdf
Generate MM-branded documents from Markdown content.
## Design system
| Token | Value | Use |
|-------|-------|-----|
| Cover background | `#0a0e1a` | Dark navy |
| Primary accent | `#4bb8d4` | Cyan — headings, kickers, borders |
| Section banners | `#3d4550` | Slate grey |
| Purple sidebar | `#6b46c1` | Alt cover variant |
| Body text | `#e2e8f0` | Light grey on dark bg |
| Body background | `#111827` | Dark page bg |
| Code/data blocks | `#1e2533` | Slightly lighter navy |
## Usage
```bash
# Generate PDF from markdown
mm-pdf generate input.md [--out output.pdf] [--style dark|purple|light]
# Generate slide deck (Marp)
mm-pdf slides input.md [--out output.pdf]
# With explicit container (auto-discovery picks the first "*m2o*" container if omitted)
mm-pdf generate input.md --container <your-render-container-name>
```
## Pipeline
1. **MD → HTML** — a small Node script (via `marked`) renders Markdown into full MM-styled
HTML. Extracts YAML frontmatter (`title`, `subtitle`, `date`, `client`) for the cover
page. Strips the first `# H1` to use as title if no frontmatter is present.
2. **HTML → PDF** — Chrome headless renders the HTML to PDF (`--print-to-pdf`).
Both outputs are saved alongside the input file.
## Output
- `<name>.html` — standalone styled HTML (preview, shareable)
- `<name>.pdf` — print-ready PDF
## Styles
| Style | Cover | Use case |
|-------|-------|----------|
| `dark` | Navy `#0a0e1a` + cyan accent | Technical specs, deployment docs |
| `purple` | Purple sidebar `#6b46c1` | Partner decks |
| `light` | White + navy header | Client-facing docs |
`dark.css` and `purple.css` ship in `templates/`. `light` is documented for forward
compatibility but has no template in this bundle yet — add `templates/light.css` locally
if you need it (it will be picked up automatically; `generate.sh` falls back to `dark.css`
for any missing style file).
## Components (in Markdown)
```markdown
<!-- KPI card row -->
<div class="kpi-row">
<div class="kpi">**€5K** setup</div>
<div class="kpi">**€2K**/mo</div>
<div class="kpi">**8 weeks** pilot</div>
</div>
<!-- Section kicker -->
<span class="kicker">DEPLOYMENT ARCHITECTURE</span>
<!-- Warning/callout -->
<div class="callout">⚠️ Open item: integration path TBD</div>
```
## Requirements
- Docker, with a running container that has Node.js + Google Chrome (headless) installed
and reachable via `docker exec`/`docker cp` from the host running this skill. Any
container name is fine — `scripts/generate.sh` auto-discovers the first container whose
name matches `nasr-m2o|m2o`, or accepts `--container <name>` to target any other
container that meets the Node+Chrome requirement.
- `docker` CLI available on the host invoking `mm-pdf`.
No absolute host paths, hostnames, or client-identifying data are baked into this skill —
`--container` and the input Markdown path are the only machine-specific inputs at run time.

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#!/usr/bin/env bash
# mm-pdf generate — render Markdown → HTML → PDF using nasr-m2o container
set -euo pipefail
SKILL_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
STYLE="${MM_PDF_STYLE:-dark}"
OUT=""
OUT_HTML=""
CONTAINER=""
INPUT=""
usage() {
echo "Usage: mm-pdf generate <input.md> [--out output.pdf] [--style dark|purple|light] [--container NAME]"
exit 1
}
while [[ $# -gt 0 ]]; do
case $1 in
--out) OUT="$2"; shift 2 ;;
--style) STYLE="$2"; shift 2 ;;
--container) CONTAINER="$2"; shift 2 ;;
--help|-h) usage ;;
*) INPUT="$1"; shift ;;
esac
done
[[ -z "$INPUT" ]] && usage
[[ ! -f "$INPUT" ]] && { echo "Error: $INPUT not found"; exit 1; }
INPUT_ABS="$(realpath "$INPUT")"
BASENAME="${INPUT_ABS%.md}"
OUT="${OUT:-${BASENAME}.pdf}"
OUT_ABS="$(realpath -m "$OUT")"
OUT_HTML_ABS="${OUT_ABS%.pdf}.html"
# Auto-discover container
if [[ -z "$CONTAINER" ]]; then
CONTAINER=$(docker ps --format "{{.Names}}" | grep -E "nasr-m2o|m2o" | head -1)
[[ -z "$CONTAINER" ]] && { echo "Error: no m2o container running. Start nasr-m2o or pass --container"; exit 1; }
fi
echo "Using container: $CONTAINER"
# Copy files into container
TMP="/tmp/mm-pdf-$$"
docker exec "$CONTAINER" mkdir -p "$TMP"
docker cp "$INPUT_ABS" "$CONTAINER:$TMP/input.md"
if [[ -f "$SKILL_DIR/templates/${STYLE}.css" ]]; then
docker cp "$SKILL_DIR/templates/${STYLE}.css" "$CONTAINER:$TMP/style.css"
else
docker cp "$SKILL_DIR/templates/dark.css" "$CONTAINER:$TMP/style.css"
fi
# Step 1: Markdown → styled HTML (via Node/marked)
echo "Step 1: Converting Markdown → HTML..."
docker exec "$CONTAINER" bash -c "
cd $TMP
# Install marked locally in tmp dir so require() can find it
[ -d node_modules/marked ] || npm install marked --prefix . -q 2>/dev/null
node - <<'JSEOF'
const fs = require('fs');
const path = require('path');
const { marked } = require('marked');
const mdRaw = fs.readFileSync('input.md', 'utf8');
const css = fs.readFileSync('style.css', 'utf8');
// Strip YAML frontmatter
let body = mdRaw;
const fm = {};
const fmMatch = mdRaw.match(/^---\s*\n([\s\S]*?)\n---\s*\n/);
if (fmMatch) {
fmMatch[1].split('\n').forEach(line => {
const i = line.indexOf(':');
if (i > 0) fm[line.slice(0,i).trim().toLowerCase()] = line.slice(i+1).trim();
});
body = mdRaw.slice(fmMatch[0].length);
}
// Extract first H1 as title
let title = fm.title || '';
if (!title) {
const h1 = body.match(/^#\s+(.+)$/m);
if (h1) { title = h1[1].trim(); body = body.replace(h1[0], ''); }
}
title = title || 'Machine.Machine';
const subtitle = fm.subtitle || fm.description || '';
const dateLine = [fm.client, fm.date].filter(Boolean).join(' \u00a0·\u00a0 ');
marked.setOptions({ gfm: true, breaks: false });
const contentHtml = marked.parse(body);
const coverHtml = \`<div class=\"cover\">
<div class=\"logo\">MACHINE.MACHINE</div>
<div>
<h1 class=\"title\">\${title}</h1>
\${subtitle ? '<div class=\"subtitle\">' + subtitle + '</div>' : ''}
</div>
\${dateLine ? '<div class=\"meta\">' + dateLine + '</div>' : ''}
</div>\`;
const html = \`<!DOCTYPE html>
<html lang=\"en\">
<head>
<meta charset=\"utf-8\">
<title>\${title}</title>
<style>\${css}</style>
</head>
<body>
\${coverHtml}
<div class=\"body-content\">
\${contentHtml}
</div>
</body>
</html>\`;
fs.writeFileSync('output.html', html);
console.log('HTML written:', html.length, 'bytes');
JSEOF
"
echo "Step 2: Rendering HTML → PDF..."
docker exec "$CONTAINER" bash -c "
cd $TMP
google-chrome \
--headless \
--no-sandbox \
--disable-gpu \
--disable-web-security \
--print-to-pdf=output.pdf \
--print-to-pdf-no-header \
--run-all-compositor-stages-before-draw \
--virtual-time-budget=5000 \
'file://$TMP/output.html'
"
# Copy both outputs back
docker cp "$CONTAINER:$TMP/output.html" "$OUT_HTML_ABS"
docker cp "$CONTAINER:$TMP/output.pdf" "$OUT_ABS"
docker exec "$CONTAINER" rm -rf "$TMP"
echo ""
echo "HTML: $OUT_HTML_ABS ($(du -h "$OUT_HTML_ABS" | cut -f1))"
echo "PDF: $OUT_ABS ($(du -h "$OUT_ABS" | cut -f1))"

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/* MM Dark Design System — WeasyPrint / Marp compatible */
:root {
--cover-bg: #0a0e1a;
--accent: #4bb8d4;
--section-bg: #3d4550;
--body-bg: #111827;
--body-text: #e2e8f0;
--muted: #94a3b8;
--code-bg: #1e2533;
--border: #2d3748;
--white: #ffffff;
}
/* --- PAGE --- */
@page {
size: A4;
margin: 15mm 18mm 15mm 18mm;
}
body {
font-family: 'Inter', 'Helvetica Neue', Arial, sans-serif;
font-size: 10pt;
line-height: 1.6;
color: var(--body-text);
background: var(--body-bg);
margin: 0;
padding: 0;
}
.body-content {
padding: 0;
}
/* --- COVER PAGE --- */
.cover {
background: var(--cover-bg);
min-height: 100vh;
padding: 52px 52px 40px 52px;
display: flex;
flex-direction: column;
justify-content: space-between;
page-break-after: always;
}
.cover .logo {
font-size: 11pt;
font-weight: 700;
letter-spacing: 0.15em;
text-transform: uppercase;
color: var(--accent);
}
.cover .title {
font-size: 28pt;
font-weight: 800;
color: var(--white);
line-height: 1.15;
margin: 0;
}
.cover .subtitle {
font-size: 13pt;
color: var(--accent);
margin-top: 8px;
font-weight: 400;
}
.cover .meta {
font-size: 9pt;
color: var(--muted);
margin-top: 32px;
}
.cover .version-badge {
display: inline-block;
background: var(--accent);
color: var(--cover-bg);
font-size: 8pt;
font-weight: 700;
padding: 3px 10px;
border-radius: 3px;
letter-spacing: 0.08em;
}
/* --- SECTION BANNERS --- */
.section-banner, h2 {
background: var(--section-bg);
color: var(--white);
padding: 8px 16px;
margin: 24px -18mm 16px -18mm;
font-size: 10pt;
font-weight: 700;
letter-spacing: 0.12em;
text-transform: uppercase;
}
/* --- KICKER --- */
.kicker {
display: block;
font-size: 8pt;
font-weight: 700;
letter-spacing: 0.18em;
text-transform: uppercase;
color: var(--accent);
margin-bottom: 4px;
}
/* --- HEADINGS --- */
h1 {
font-size: 20pt;
font-weight: 800;
color: var(--white);
margin: 0 0 8px 0;
}
h3 {
font-size: 11pt;
font-weight: 700;
color: var(--accent);
margin: 16px 0 6px 0;
}
h4 {
font-size: 10pt;
font-weight: 600;
color: var(--white);
margin: 12px 0 4px 0;
}
/* --- KPI CARDS --- */
.kpi-row {
display: flex;
gap: 12px;
margin: 16px 0;
}
.kpi {
flex: 1;
background: var(--code-bg);
border: 1px solid var(--border);
border-top: 3px solid var(--accent);
padding: 12px 14px;
border-radius: 4px;
font-size: 9pt;
color: var(--muted);
}
.kpi strong {
display: block;
font-size: 16pt;
font-weight: 800;
color: var(--white);
margin-bottom: 2px;
}
/* --- TABLES --- */
table {
width: 100%;
border-collapse: collapse;
margin: 12px 0;
font-size: 9pt;
}
th {
background: var(--section-bg);
color: var(--white);
padding: 7px 10px;
text-align: left;
font-weight: 600;
letter-spacing: 0.05em;
text-transform: uppercase;
font-size: 8pt;
}
td {
padding: 6px 10px;
border-bottom: 1px solid var(--border);
color: var(--body-text);
}
tr:nth-child(even) td {
background: rgba(255,255,255,0.02);
}
/* --- CODE BLOCKS --- */
pre, code {
background: var(--code-bg);
border: 1px solid var(--border);
border-radius: 3px;
font-family: 'JetBrains Mono', 'Fira Code', monospace;
font-size: 8.5pt;
color: #a5d6a7;
}
pre {
padding: 12px 14px;
overflow-x: auto;
}
code { padding: 1px 5px; }
/* --- CALLOUTS --- */
.callout {
background: rgba(75, 184, 212, 0.08);
border-left: 3px solid var(--accent);
padding: 10px 14px;
margin: 12px 0;
border-radius: 0 4px 4px 0;
font-size: 9pt;
color: var(--body-text);
}
.callout.warn {
border-left-color: #f6a623;
background: rgba(246, 166, 35, 0.06);
}
.callout.danger {
border-left-color: #e53e3e;
background: rgba(229, 62, 62, 0.06);
}
/* --- FOOTER --- */
@page {
@bottom-center {
content: "Machine.Machine — Confidential";
font-size: 7.5pt;
color: #4a5568;
}
@bottom-right {
content: counter(page);
font-size: 7.5pt;
color: #4a5568;
}
}

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/* MM Purple Sidebar — Nasr reference style */
@import url("dark.css");
:root {
--cover-bg: #1a0533;
--accent: #9f7aea;
--sidebar-bg: #6b46c1;
}
.cover {
background: var(--cover-bg);
padding-left: 0;
display: grid;
grid-template-columns: 6px 1fr;
}
.cover::before {
content: '';
background: var(--sidebar-bg);
grid-column: 1;
}
.cover-content {
grid-column: 2;
padding: 52px 52px 40px 36px;
}
.cover .logo { color: var(--accent); }
.cover .subtitle { color: var(--accent); }
h3 { color: var(--accent); }
.kpi { border-top-color: var(--accent); }
.callout { border-left-color: var(--accent); }

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# mm-pdf-report install recipe (apply-adapter.md bundle layout, local adapter v1)
# Places the mm-pdf skill into the buyer's Claude Code skills directory.
targets:
- src: SKILL.md
dest: ~/.claude/skills/mm-pdf/SKILL.md
- src: scripts/generate.sh
dest: ~/.claude/skills/mm-pdf/scripts/generate.sh
- src: templates/dark.css
dest: ~/.claude/skills/mm-pdf/templates/dark.css
- src: templates/purple.css
dest: ~/.claude/skills/mm-pdf/templates/purple.css
checks:
- cmd: test -f ~/.claude/skills/mm-pdf/SKILL.md
expect_exit: 0
- cmd: test -x ~/.claude/skills/mm-pdf/scripts/generate.sh
expect_exit: 0
entrypoint: "Ask your agent to use the mm-pdf skill, or run: ~/.claude/skills/mm-pdf/scripts/generate.sh <input.md> --style dark"

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{
"schema_version": "m2.solution.v1",
"solution_id": "sol_mm-pdf-report",
"name": "MM PDF Report Generator",
"summary": "Generate Machine.Machine branded PDF reports and slide decks from Markdown.",
"description": "Installs the mm-pdf skill: a Markdown-to-branded-PDF/HTML pipeline (dark navy aesthetic, cyan accents, A4 layout) built on a Node+marked HTML render step and headless-Chrome print-to-pdf inside a Docker container. Ships three artifacts: SKILL.md, scripts/generate.sh, and templates/{dark,purple}.css. content_hash is computed over payload/ + recipe.yaml only (not solution.json itself, to avoid the self-referential hash problem of hashing a file that contains its own hash): sha256 of `tar --sort=name --mtime='@0' --owner=0 --group=0 --numeric-owner -czf - payload recipe.yaml` run from this bundle's root.",
"intent": "Give an operator a working branded-document pipeline (proposal PDFs, spec sheets, decks) without building one from scratch.",
"behavior": {
"skill_ref": "payload/SKILL.md"
},
"tools": [
{ "name": "docker", "kind": "cli", "required": true }
],
"runtime": {
"surfaces": ["claude-code-skill"]
},
"permissions": [
{ "action": "write", "target": "~/.claude/skills/mm-pdf/" },
{ "action": "exec", "target": "docker exec/docker cp against a caller-selected container" }
],
"deployment": {
"recipe_ref": "recipe.yaml",
"entrypoint": "Ask your agent to use the mm-pdf skill, or run: ~/.claude/skills/mm-pdf/scripts/generate.sh <input.md> --style dark",
"verify_command": "test -f ~/.claude/skills/mm-pdf/SKILL.md"
},
"applicability": {
"image_classes": ["primus", "agent-latest"],
"roles": ["operator", "desktop-agent"],
"tool_requirements": ["docker"]
},
"tenant_scope": "m2-core",
"evidence": [
{
"source": "/home/m2/.claude/skills/mm-pdf/ (SKILL.md, scripts/generate.sh, templates/dark.css, templates/purple.css) — the proven skill this bundle packages, present on the m2 host since 2026-04-21 (SKILL.md mtime 2026-04-21T00:05:01+02:00, templates/dark.css mtime 2026-04-21T00:04, scripts/generate.sh mtime 2026-04-21T00:09)",
"machine": "m2",
"excerpt": "SKILL.md frontmatter: 'Generate beautifully styled Machine.Machine branded PDFs and slide decks from Markdown. Dark navy aesthetic, cyan accents, professional A4 layout.'"
},
{
"source": "/home/m2/session-2026-04-20.pdf — real output of this pipeline: 4-page A4 PDF, PDF metadata Title 'output.html' (the pipeline's fixed intermediate filename), Creator 'HeadlessChrome/145.0.0.0' (Chrome print-to-pdf, matches scripts/generate.sh Step 2), CreationDate 2026-04-20T22:56:51+02:00; pdftotext shows the 'MACHINE.MACHINE' cover logo and MM design-system markup",
"machine": "m2",
"excerpt": "pdfinfo: Title=output.html; Creator=Mozilla/5.0 ... HeadlessChrome/145.0.0.0 ...; Producer=Skia/PDF m145; Pages=4; page size 594.96x841.92pts (A4)"
},
{
"source": "/home/m2/spark-cluster-old/fabric-setup.pdf + fabric-setup.html — second real generated pair, 4-page A4 PDF titled 'DGX Spark Cluster — Fabric Setup Reference', same HeadlessChrome/145.0.0.0 print-to-pdf signature, CreationDate 2026-04-21T00:09:40+02:00 (12 seconds before this SKILL.md's own mtime, i.e. produced by the exact skill version in this bundle)",
"machine": "m2",
"excerpt": "pdftotext footer: 'Machine.Machine — Confidential' page-number footer matches templates/dark.css `@bottom-center`/`@bottom-right` rules"
}
],
"content_hash": "sha256:aaee0b1a894efafabddd1bd95c5023c915a6fcd05007776ad921d420f38ea916",
"price": {
"amount": 40,
"currency": "m2cr",
"model": "fixed"
},
"seller": "sdjs-operator",
"license": {
"terms": "Perpetual use on operator-owned machines for the buyer's own document generation; no redistribution of the skill files as a standalone product.",
"major_version_coverage": true
},
"revenue_split": {
"platform_pct": 10
}
}

View file

@ -79,11 +79,11 @@ published listing
**Independent test**: quickstart.md Scenario A
- [ ] T027 [US2] `publish` command in `cli/src/m2_market/cli.py` + `cli/src/m2_market/publish.py`: validate bundle against schemas (exit 5 on violation incl. tenant firewall), push `listing/<id>` branch, open PR via Forgejo API with template
- [x] T027 [US2] `publish` command in `cli/src/m2_market/cli.py` + `cli/src/m2_market/publish.py`: validate bundle against schemas (exit 5 on violation incl. tenant firewall), push `listing/<id>` branch, open PR via Forgejo API with template
- [ ] T028 [US2] Registry CI hardening in `m2/market-registry` CI config: evidence-required, owner-initiated + double-scrub attestations for tenant-scoped submissions, content_hash-vs-release check (Story 2 AC-2/AC-4 mechanical rejection)
- [ ] T029 [P] [US2] Package seed Solution `solutions/mm-pdf-report/` (from the proven mm-pdf outcome): solution.json, payload/, recipe.yaml, real evidence refs
- [ ] T030 [P] [US2] Package seed Solution `solutions/agent-scaffold/` (from the agent-scaffold skill outcome): solution.json, payload/, recipe.yaml, real evidence
- [ ] T031 [P] [US2] Package seed Solution `solutions/competitor-scan/` (from the competitor-scan report outcome): solution.json, payload/, recipe.yaml, real evidence
- [x] T029 [P] [US2] Package seed Solution `solutions/mm-pdf-report/` (from the proven mm-pdf outcome): solution.json, payload/, recipe.yaml, real evidence refs
- [x] T030 [P] [US2] Package seed Solution `solutions/agent-scaffold/` (from the agent-scaffold skill outcome): solution.json, payload/, recipe.yaml, real evidence
- [ ] T031 [P] [US2] BLOCKED-ON-OPERATOR: competitor-scan seed has no real evidence (constitution III) — t031 report recommends substituting Parlobyg email-triage (tenant-derived: needs owner-initiated + double-scrub sign-off) or another proven outcome
- [ ] T032 [US2] Publish all three seeds through the real protocol (PRs, review, merge), verify each discoverable via `m2-market search` (SC-002)
**Checkpoint**: ≥3 published evidence-backed listings live

620
uv.lock
View file

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