hermes-agent/optional-skills/security/unbroker/scripts/report.py
SHL0MS c2828f2b9b feat(skills): add security/unbroker (autonomous data-broker removal)
unbroker finds where a consenting person's info is exposed across data
brokers and people-search sites and files the removals, running as far as
each site allows and handing only genuinely human-only steps (hard CAPTCHA,
gov-ID, phone, fax) back as an end-of-run digest.

- Deterministic stdlib CLI (scripts/pdd.py) owns config, dossiers+consent,
  the broker DB, tier planning, the ledger, email, and the autonomous
  action queue; the agent scans/submits with native tools (web_extract,
  browser_*, delegate_task, cronjob, terminal).
- Verify-before-disclose, least-disclosure (never volunteers SSN), consent
  gate, opaque ids, optional age-at-rest encryption, file-locked ledger.
- Jurisdiction-aware (CCPA/CPRA, GDPR, generic); CA DROP one-shot covers
  the state registry (~545) in a single request; BADBOOL + curated
  people-search coverage; scheduled re-scan for re-listing.
- No CAPTCHA-solving services or anti-bot bypass; browser email mode needs
  no stored password.
- 85 hermetic tests (tests/skills/test_unbroker_skill.py; SMTP/IMAP via
  injected fakes, registry via CSV fixtures). Ships placeholder data only.

Broker dataset adapted from BADBOOL (Yael Grauer, CC BY-NC-SA 4.0).
2026-07-02 21:16:10 -04:00

161 lines
7 KiB
Python

"""Status dashboards, Markdown reports, human-task digest, and Google Sheets row export."""
from __future__ import annotations
import brokers as brokers_mod
import ledger as ledger_mod
STATE_LABELS = {
"new": "Not started",
"searching": "Searching",
"not_found": "Not found",
"found": "Found (action needed)",
"indirect_exposure": "Indirect exposure (PII on a relative's record)",
"action_selected": "Action selected",
"submitted": "Submitted",
"verification_pending": "Awaiting verification",
"awaiting_processing": "Processing",
"confirmed_removed": "Removed",
"reappeared": "Reappeared",
"human_task_queued": "Human task",
"blocked": "Blocked",
}
def status_counts(subject_id: str) -> dict:
counts: dict[str, int] = {}
for case in ledger_mod.load(subject_id).values():
state = case.get("state", "new")
counts[state] = counts.get(state, 0) + 1
return counts
def metrics(subject_id: str) -> dict:
"""Outcome metrics: what's actually confirmed vs merely claimed, and what's overdue.
removal_rate is confirmed_removed over cases we actually acted on (found/submitted/... ),
NOT over the whole broker DB, so it reflects real progress on real exposure. `in_flight`
is 'claimed' (submitted/verifying/processing) but not yet re-scan-confirmed. `overdue`
counts cases whose recheck window has already passed (the cron backlog).
"""
c = status_counts(subject_id)
removed = c.get("confirmed_removed", 0)
in_flight = c.get("submitted", 0) + c.get("verification_pending", 0) + c.get("awaiting_processing", 0)
open_found = c.get("found", 0) + c.get("reappeared", 0) + c.get("action_selected", 0) \
+ c.get("indirect_exposure", 0)
acted = removed + in_flight + open_found + c.get("human_task_queued", 0) + c.get("blocked", 0)
return {
"confirmed_removed": removed,
"in_flight_claimed": in_flight, # submitted but NOT yet verified gone
"open_needs_action": open_found,
"blocked": c.get("blocked", 0),
"human_tasks": c.get("human_task_queued", 0),
"acted_total": acted,
"removal_rate": round(removed / acted, 3) if acted else 0.0,
"overdue_rechecks": len(ledger_mod.due(subject_id)),
}
def render_markdown(subject_id: str) -> str:
ledger = ledger_mod.load(subject_id)
counts = status_counts(subject_id)
total = sum(counts.values())
removed = counts.get("confirmed_removed", 0)
m = metrics(subject_id)
lines = [
f"# unbroker - status for `{subject_id}`",
"",
f"**{removed} / {total} confirmed removed** · removal rate (of acted-on cases): "
f"{int(m['removal_rate'] * 100)}%",
"",
f"- Confirmed removed: {m['confirmed_removed']}",
f"- In flight (submitted, not yet re-scan-confirmed): {m['in_flight_claimed']}",
f"- Open / needs action: {m['open_needs_action']}",
f"- Blocked (anti-bot): {m['blocked']} · Human tasks: {m['human_tasks']}",
f"- Overdue rechecks (cron backlog): {m['overdue_rechecks']}",
"",
"| State | Count |",
"|---|---|",
]
for state in ledger_mod.STATES:
if counts.get(state):
lines.append(f"| {STATE_LABELS.get(state, state)} | {counts[state]} |")
tasks = [c for c in ledger.values() if c.get("state") == "human_task_queued"]
if tasks:
lines += ["", "## Outstanding human tasks"]
for c in tasks:
reason = c.get("human_task_reason", "manual step required")
lines.append(f"- **{c.get('broker_id')}** - {reason}")
indirect = [c for c in ledger.values() if c.get("state") == "indirect_exposure"]
if indirect:
lines += ["", "## Indirect exposure (your PII on third-party records)",
"Not removable via the broker's self-service opt-out (the record is about someone "
"else). Lever: a targeted CCPA/GDPR delete-my-PII request naming only your own "
"identifiers."]
for c in indirect:
ev = c.get("evidence") or {}
note = ev.get("summary") or "subject's identifiers appear on another person's listing"
lines.append(f"- **{c.get('broker_id')}** - {note}")
return "\n".join(lines) + "\n"
def human_tasks_markdown(subject_id: str) -> str:
"""ONE consolidated digest of everything that genuinely needs a human.
The autonomous run accumulates human-only work silently (never interrupting);
this digest is presented once, at the end, so the operator clears it in a
single sitting. Includes queued tasks and blocked-site operator-browser checks.
"""
ledger = ledger_mod.load(subject_id)
tasks = [(bid, c) for bid, c in sorted(ledger.items()) if c.get("state") == "human_task_queued"]
blocked = [(bid, c) for bid, c in sorted(ledger.items()) if c.get("state") == "blocked"]
lines = [f"# Human tasks for `{subject_id}`", ""]
if not tasks and not blocked:
lines.append("Nothing needs a human right now.")
return "\n".join(lines) + "\n"
lines.append(f"{len(tasks)} manual step(s) + {len(blocked)} blocked site(s). "
"Everything else ran (or will run) autonomously.")
if tasks:
lines += ["", "## Manual steps"]
for bid, c in tasks:
b = brokers_mod.get(bid) or {}
opt = b.get("optout") or {}
lines.append(f"### {b.get('name', bid)}")
lines.append(f"- Why: {c.get('human_task_reason', 'manual step required')}")
where = opt.get("url") or opt.get("email") or "(see broker record)"
lines.append(f"- Where: {where}")
for q in (opt.get("quirks") or [])[:2]:
lines.append(f"- Note: {q}")
lines.append("- Withhold: SSN and full ID numbers - always.")
lines.append(f"- When done, tell the agent so it records the outcome for `{bid}`.")
if blocked:
lines += ["", "## Blocked sites (open in YOUR browser - it gets through where bots don't)"]
for bid, c in blocked:
b = brokers_mod.get(bid) or {}
url = ((b.get("search") or {}).get("url")) or "(see broker record)"
lines.append(f"- **{b.get('name', bid)}** - open {url}, search the subject, and report "
"the verdict (or a screenshot) back to the agent.")
return "\n".join(lines) + "\n"
def sheets_rows(subject_id: str) -> list[list[str]]:
"""Header + one row per case for the optional Google Sheets tracker.
The agent appends these via the `google-workspace` skill, e.g.:
google_api.py sheets append <SHEET_ID> "Sheet1!A:F" --values <json-rows>
"""
rows = [["broker_id", "state", "found", "tier", "removed_at", "next_recheck"]]
for bid, c in sorted(ledger_mod.load(subject_id).items()):
rows.append([
bid,
c.get("state", ""),
str(c.get("found", "")),
(c.get("automation") or {}).get("tier_used", ""),
c.get("removal_confirmed_at") or "",
c.get("next_recheck_at") or "",
])
return rows