diff --git a/agent/curator.py b/agent/curator.py
index 15edd4cd76..3b96ec7624 100644
--- a/agent/curator.py
+++ b/agent/curator.py
@@ -44,15 +44,11 @@ def _state_file() -> Path:
return get_hermes_home() / "skills" / ".curator_state"
-def _default_state() -> Dict[str, Any]:
- return {
+def load_state() -> Dict[str, Any]:
+ base: Dict[str, Any] = {
"last_run_at": None, "last_run_duration_seconds": None, "last_run_summary": None,
"last_run_summary_shown_at": None, "last_report_path": None, "paused": False, "run_count": 0,
}
-
-
-def load_state() -> Dict[str, Any]:
- base = _default_state()
path = _state_file()
if not path.exists():
return base
@@ -136,8 +132,8 @@ def get_archive_after_days() -> int:
def get_prune_builtins() -> bool:
- """Bundled built-ins are curation candidates (ON by default); a suppression
- list keeps them archived across `hermes update` re-seeds. Hub skills never."""
+ """Bundled built-ins are curation candidates (ON by default); a suppression list
+ keeps them archived across `hermes update` re-seeds. Hub skills are never pruned."""
return bool(_load_config().get("prune_builtins", True))
@@ -163,11 +159,9 @@ def should_run_now(now: Optional[datetime] = None) -> bool:
tick. ``hermes curator run`` bypasses this; the idle check is the caller's."""
if not is_enabled() or is_paused():
return False
-
state = load_state()
last = _parse_iso(state.get("last_run_at"))
- if now is None:
- now = datetime.now(timezone.utc)
+ now = now or datetime.now(timezone.utc)
if last is None:
try:
state["last_run_at"] = now.isoformat()
@@ -203,18 +197,17 @@ def _archive_as_curator(_u, name: str) -> bool:
ledger entry reads as an autonomous transition, not a foreground call."""
try:
from tools.skill_ledger import reset_ledger_actor, set_ledger_actor
- _tok = set_ledger_actor("curator")
+ tok = set_ledger_actor("curator")
except Exception:
- _tok = reset_ledger_actor = None # type: ignore[assignment]
+ tok = reset_ledger_actor = None # type: ignore[assignment]
try:
- ok, _msg = _u.archive_skill(name)
+ return _u.archive_skill(name)[0]
finally:
- if _tok is not None:
+ if tok is not None:
try:
- reset_ledger_actor(_tok)
+ reset_ledger_actor(tok)
except Exception:
pass
- return ok
def apply_automatic_transitions(now: Optional[datetime] = None) -> Dict[str, int]:
@@ -224,8 +217,7 @@ def apply_automatic_transitions(now: Optional[datetime] = None) -> Dict[str, int
starts NOW, not at epoch. Returns a counter dict."""
from tools import skill_usage as _u
- if now is None:
- now = datetime.now(timezone.utc)
+ now = now or datetime.now(timezone.utc)
stale_cutoff = now - timedelta(days=get_stale_after_days())
archive_cutoff = now - timedelta(days=get_archive_after_days())
# Cron-referenced skills are in use by definition (usage only bumps when a
@@ -542,10 +534,7 @@ def _find_reference(args: Dict[str, Any], needles: Set[str]) -> Optional[str]:
def _classify_removed_skills(
- removed: List[str],
- added: List[str],
- after_names: Set[str],
- tool_calls: List[Dict[str, Any]],
+ removed: List[str], added: List[str], after_names: Set[str], tool_calls: List[Dict[str, Any]],
) -> Dict[str, List[Dict[str, Any]]]:
"""Split ``removed`` into consolidated vs pruned. Heuristic: a ``skill_manage``
call on a DIFFERENT, surviving-or-new skill whose file_path/content arguments
@@ -600,18 +589,13 @@ def _parse_structured_summary(llm_final: str) -> Dict[str, List[Dict[str, str]]]
if not isinstance(data, dict):
return out
- def _entries(key: str) -> List[Dict[str, Any]]:
+ def _entries(key: str, *fields: str) -> List[Dict[str, str]]:
raw = data.get(key) or []
- return [e for e in raw if isinstance(e, dict)] if isinstance(raw, list) else []
+ cleaned = ({f: _clean_str(e.get(f)) for f in (*fields, "reason")} for e in raw if isinstance(e, dict)) if isinstance(raw, list) else ()
+ return [e for e in cleaned if all(e[f] for f in fields)]
- for entry in _entries("consolidations"):
- frm, into = _clean_str(entry.get("from")), _clean_str(entry.get("into"))
- if frm and into:
- out["consolidations"].append({"from": frm, "into": into, "reason": _clean_str(entry.get("reason"))})
- for entry in _entries("prunings"):
- name = _clean_str(entry.get("name"))
- if name:
- out["prunings"].append({"name": name, "reason": _clean_str(entry.get("reason"))})
+ out["consolidations"] = _entries("consolidations", "from", "into")
+ out["prunings"] = _entries("prunings", "name")
return out
@@ -633,11 +617,8 @@ def _extract_absorbed_into_declarations(tool_calls: List[Dict[str, Any]]) -> Dic
def _reconcile_classification(
- removed: List[str],
- heuristic: Dict[str, List[Dict[str, Any]]],
- model_block: Dict[str, List[Dict[str, str]]],
- destinations: Set[str],
- absorbed_declarations: Optional[Dict[str, Dict[str, Any]]] = None,
+ removed: List[str], heuristic: Dict[str, List[Dict[str, Any]]], model_block: Dict[str, List[Dict[str, str]]],
+ destinations: Set[str], absorbed_declarations: Optional[Dict[str, Dict[str, Any]]] = None,
) -> Dict[str, List[Dict[str, Any]]]:
"""Merge heuristic (tool-call evidence) with the model's structured block.
First match wins; every removed skill lands in exactly one bucket:
@@ -792,15 +773,8 @@ def _new_run_dir(started_at: datetime) -> Optional[Path]:
def _write_run_report(
- *,
- started_at: datetime,
- elapsed_seconds: float,
- auto_counts: Dict[str, int],
- auto_summary: str,
- before_report: List[Dict[str, Any]],
- before_names: Set[str],
- after_report: List[Dict[str, Any]],
- llm_meta: Dict[str, Any],
+ *, started_at: datetime, elapsed_seconds: float, auto_counts: Dict[str, int], auto_summary: str,
+ before_report: List[Dict[str, Any]], before_names: Set[str], after_report: List[Dict[str, Any]], llm_meta: Dict[str, Any],
) -> Optional[Path]:
"""Write run.json + REPORT.md under logs/curator/{YYYYMMDD-HHMMSS}/. Returns
the report dir, or None if it couldn't be created (reporting is best-effort)."""
@@ -812,47 +786,31 @@ def _write_run_report(
after_by_name, before_by_name = _by_name(after_report), _by_name(before_report)
diff = _diff_and_classify(before_names, set(after_by_name), tool_calls, llm_meta.get("final", "") or "")
- transitions: List[Dict[str, str]] = []
- for name in sorted(diff.after_names & before_names):
- s_before = (before_by_name.get(name) or {}).get("state")
- s_after = (after_by_name.get(name) or {}).get("state")
- if s_before and s_after and s_before != s_after:
- transitions.append({"name": name, "from": s_before, "to": s_after})
+ states = ((n, (before_by_name.get(n) or {}).get("state"), (after_by_name.get(n) or {}).get("state")) for n in sorted(diff.after_names & before_names))
+ transitions = [{"name": n, "from": b, "to": a} for n, b, a in states if b and a and b != a]
tc_counts: Dict[str, int] = dict(Counter(tc.get("name", "unknown") for tc in tool_calls))
cron_rewrites = _rewrite_cron_refs(diff.consolidated, diff.pruned)
jobs_updated = int(cron_rewrites.get("jobs_updated", 0))
payload = {
- "started_at": started_at.isoformat(),
- "duration_seconds": round(elapsed_seconds, 2),
- "model": llm_meta.get("model", ""),
- "provider": llm_meta.get("provider", ""),
+ "started_at": started_at.isoformat(), "duration_seconds": round(elapsed_seconds, 2),
+ "model": llm_meta.get("model", ""), "provider": llm_meta.get("provider", ""),
"auto_transitions": auto_counts,
"counts": {
- "before": len(before_names),
- "after": len(diff.after_names),
+ "before": len(before_names), "after": len(diff.after_names),
"delta": len(diff.after_names) - len(before_names),
- "archived_this_run": len(diff.removed),
- "added_this_run": len(diff.added),
- "consolidated_this_run": len(diff.consolidated),
- "pruned_this_run": len(diff.pruned),
- "state_transitions": len(transitions),
- "cron_jobs_rewritten": jobs_updated,
+ "archived_this_run": len(diff.removed), "added_this_run": len(diff.added),
+ "consolidated_this_run": len(diff.consolidated), "pruned_this_run": len(diff.pruned),
+ "state_transitions": len(transitions), "cron_jobs_rewritten": jobs_updated,
"tool_calls_total": sum(tc_counts.values()),
},
"tool_call_counts": tc_counts,
- "archived": diff.removed,
- "consolidated": diff.consolidated,
- "pruned": diff.pruned,
- "pruned_names": [p["name"] for p in diff.pruned],
- "added": diff.added,
- "state_transitions": transitions,
- "cron_rewrites": cron_rewrites,
- "llm_final": llm_meta.get("final", ""),
- "llm_summary": llm_meta.get("summary", ""),
- "llm_error": llm_meta.get("error"),
- "tool_calls": llm_meta.get("tool_calls", []),
+ "archived": diff.removed, "consolidated": diff.consolidated, "pruned": diff.pruned,
+ "pruned_names": [p["name"] for p in diff.pruned], "added": diff.added,
+ "state_transitions": transitions, "cron_rewrites": cron_rewrites,
+ "llm_final": llm_meta.get("final", ""), "llm_summary": llm_meta.get("summary", ""),
+ "llm_error": llm_meta.get("error"), "tool_calls": llm_meta.get("tool_calls", []),
}
_write_json(run_dir / "run.json", payload, "run.json")
@@ -1029,6 +987,9 @@ def _safe_curated_report() -> List[Dict[str, Any]]:
return []
+_AUTO_LABELS = (("marked_stale", "marked stale"), ("archived", "archived"), ("reactivated", "reactivated"))
+
+
def _consolidation_pass(prefix: str, auto_summary: str, dry_run: bool, before_names: Set[str]) -> tuple:
"""The LLM half of a run: fork (unless no candidates), then append the rename
map (`old-name → umbrella`) so users needn't dig into REPORT.md.
@@ -1067,10 +1028,8 @@ def _consolidation_pass(prefix: str, auto_summary: str, dry_run: bool, before_na
def run_curator_review(
- on_summary: Optional[Callable[[str], None]] = None,
- synchronous: bool = False,
- dry_run: bool = False,
- consolidate: Optional[bool] = None,
+ on_summary: Optional[Callable[[str], None]] = None, synchronous: bool = False,
+ dry_run: bool = False, consolidate: Optional[bool] = None,
) -> Dict[str, Any]:
"""Execute a single curator review pass: (1) automatic state transitions (no
LLM); (2) if *consolidate* and there are candidates, fork an AIAgent on the
@@ -1086,8 +1045,7 @@ def run_curator_review(
if consolidate is None:
consolidate = get_consolidate()
start = datetime.now(timezone.utc)
- if dry_run:
- # Count candidates without mutating state.
+ if dry_run: # count candidates without mutating state
counts = {"checked": len(_safe_curated_report()), "marked_stale": 0, "archived": 0, "reactivated": 0}
else:
# Pre-mutation snapshot — best-effort, never blocks the run: a transient
@@ -1102,9 +1060,7 @@ def run_curator_review(
counts = apply_automatic_transitions(now=start)
auto_summary = ", ".join(
- f"{counts[key]} {label}"
- for key, label in (("marked_stale", "marked stale"), ("archived", "archived"), ("reactivated", "reactivated"))
- if counts[key]
+ f"{counts[key]} {label}" for key, label in _AUTO_LABELS if counts[key]
) or "no changes"
# Persist before the LLM pass so a crash mid-review still records the run.
@@ -1139,14 +1095,12 @@ def run_curator_review(
try:
report_path = _write_run_report(
started_at=start, elapsed_seconds=elapsed, auto_counts=counts, auto_summary=auto_summary,
- before_report=before_report, before_names=before_names,
- after_report=_safe_curated_report(), llm_meta=llm_meta,
+ before_report=before_report, before_names=before_names, after_report=_safe_curated_report(), llm_meta=llm_meta,
)
if report_path is not None:
state2["last_report_path"] = str(report_path)
except Exception as e:
logger.debug("Curator report write failed: %s", e, exc_info=True)
-
save_state(state2)
_notify(on_summary, f"curator: {final_summary}")
@@ -1284,14 +1238,9 @@ def _run_llm_review(prompt: str) -> Dict[str, Any]:
agent_kwargs["acp_command"] = acp_command
agent_kwargs["acp_args"] = list(rp.get("args") or [])
review_agent = AIAgent(
- model=model_name,
- provider=provider,
- api_key=rp.get("api_key"),
- base_url=rp.get("base_url"),
- api_mode=rp.get("api_mode"),
- credential_pool=rp.get("credential_pool"),
- request_overrides=request_overrides,
- **agent_kwargs,
+ model=model_name, provider=provider, api_key=rp.get("api_key"), base_url=rp.get("base_url"),
+ api_mode=rp.get("api_mode"), credential_pool=rp.get("credential_pool"),
+ request_overrides=request_overrides, **agent_kwargs,
# No ``terminal``: a shell mv/cp/rm under the skills tree writes bytes
# with NO ledger entry, so rollback would restore a hollow skill. Every
# mutation goes through ledgered skill_manage; dropping the toolset
@@ -1299,10 +1248,7 @@ def _run_llm_review(prompt: str) -> Dict[str, Any]:
enabled_toolsets=["skills"],
# Umbrella-building over hundreds of skills takes 50-100 API calls.
max_iterations=9999,
- quiet_mode=True,
- platform="curator",
- skip_context_files=True,
- skip_memory=True,
+ quiet_mode=True, platform="curator", skip_context_files=True, skip_memory=True,
)
# Disable recursive nudges — the curator must never spawn its own review.
review_agent._memory_nudge_interval = 0
@@ -1335,17 +1281,13 @@ def _run_llm_review(prompt: str) -> Dict[str, Any]:
# --- Public entrypoint for the session-start hook ---
def maybe_run_curator(
- *,
- idle_for_seconds: Optional[float] = None,
- on_summary: Optional[Callable[[str], None]] = None,
+ *, idle_for_seconds: Optional[float] = None, on_summary: Optional[Callable[[str], None]] = None,
) -> Optional[Dict[str, Any]]:
"""Best-effort: run a curator pass if all gates pass. Returns the result
dict if a pass was started, else None. Never raises."""
try:
# Idle gating: only enforce when the caller provided a measurement.
- if not should_run_now() or (
- idle_for_seconds is not None and idle_for_seconds < get_min_idle_hours() * 3600.0
- ):
+ if not should_run_now() or (idle_for_seconds is not None and idle_for_seconds < get_min_idle_hours() * 3600.0):
return None
return run_curator_review(on_summary=on_summary)
except Exception as e:
diff --git a/agent/insights.py b/agent/insights.py
index 9beeeea9f3..4819c24af9 100644
--- a/agent/insights.py
+++ b/agent/insights.py
@@ -13,13 +13,7 @@ from datetime import datetime
from decimal import Decimal
from typing import Any, Dict, List, Optional
-from agent.usage_pricing import (
- CanonicalUsage,
- estimate_usage_cost,
- format_cost_label,
- format_duration_compact,
- has_known_pricing,
-)
+from agent.usage_pricing import CanonicalUsage, estimate_usage_cost, format_cost_label, format_duration_compact, has_known_pricing
_TOKEN_KEYS = ("input_tokens", "output_tokens", "cache_read_tokens", "cache_write_tokens")
_SKILL_TOOLS = {"skill_view", "skill_manage"}
@@ -30,16 +24,8 @@ def _fmt_est_cost(est_cost: float) -> str:
return format_cost_label(Decimal(str(est_cost)))
-def _estimate_cost(
- session_or_model: Dict[str, Any] | str,
- input_tokens: int = 0,
- output_tokens: int = 0,
- *,
- cache_read_tokens: int = 0,
- cache_write_tokens: int = 0,
- provider: Optional[str] = None,
- base_url: Optional[str] = None,
-) -> tuple[float, str]:
+def _estimate_cost(session_or_model: Dict[str, Any] | str, input_tokens: int = 0, output_tokens: int = 0, *, cache_read_tokens: int = 0,
+ cache_write_tokens: int = 0, provider: Optional[str] = None, base_url: Optional[str] = None) -> tuple[float, str]:
"""Estimate the USD cost for a session row or a model/token tuple."""
if isinstance(session_or_model, dict):
s = session_or_model
@@ -55,9 +41,7 @@ def _estimate_cost(
def _bar_chart(values: List[int], max_width: int = 20) -> List[str]:
peak = max(values) if values else 1
- if peak == 0:
- return ["" for _ in values]
- return ["█" * max(1, int(v / peak * max_width)) if v > 0 else "" for v in values]
+ return ["" for _ in values] if peak == 0 else ["█" * max(1, int(v / peak * max_width)) if v > 0 else "" for v in values]
def _short_model(model: Optional[str]) -> str:
@@ -91,10 +75,8 @@ def _day(ts: Any) -> str:
def _scoped(before: str, after: str = "", *, src: str = " AND s.source = ?") -> tuple[str, str]:
- """(unfiltered, source-filtered) query pair sharing one body.
-
- Built once at class definition, so no runtime value can alter query structure.
- """
+ """(unfiltered, source-filtered) query pair sharing one body. Built once at class definition,
+ so no runtime value can alter query structure."""
return before + after, before + src + after
@@ -173,9 +155,7 @@ class InsightsEngine:
self._conn = db._conn
try:
self._has_assistant_calls_index = bool(self._conn.execute(
- "SELECT 1 FROM sqlite_master WHERE type='index' AND name=?",
- (self._MESSAGES_ASSISTANT_CALLS_INDEX,),
- ).fetchone())
+ "SELECT 1 FROM sqlite_master WHERE type='index' AND name=?", (self._MESSAGES_ASSISTANT_CALLS_INDEX,)).fetchone())
except sqlite3.Error:
self._has_assistant_calls_index = False
if not self._has_assistant_calls_index:
@@ -185,33 +165,25 @@ class InsightsEngine:
setattr(self, base + suffix, getattr(self, base + suffix).replace(strip, ""))
def _query(self, base: str, cutoff: float, source: Optional[str]) -> list:
- """Rows of ``_WITH_SOURCE`` or ``_ALL`` (instance attrs, so the
- unpinned fallback applies)."""
- if source:
- return self._conn.execute(getattr(self, base + "_WITH_SOURCE"), (cutoff, source)).fetchall()
- return self._conn.execute(getattr(self, base + "_ALL"), (cutoff,)).fetchall()
+ """Rows of ``_WITH_SOURCE`` or ``_ALL`` (instance attrs, so the unpinned fallback applies)."""
+ sql, params = (getattr(self, base + "_WITH_SOURCE"), (cutoff, source)) if source else (getattr(self, base + "_ALL"), (cutoff,))
+ return self._conn.execute(sql, params).fetchall()
def generate(self, days: int = 30, source: str = None) -> Dict[str, Any]:
- """Generate a complete insights report for the last ``days`` days,
- optionally filtered by source platform."""
+ """Generate a complete insights report for the last ``days`` days, optionally filtered by source platform."""
cutoff = time.time() - (days * 86400)
# Drain the SessionDB's async accounting queue so counters are exact
# (self.db may be a raw sqlite3 connection in tests — guard).
flush = getattr(self.db, "flush_token_counts", None)
if callable(flush):
flush()
-
sessions = self._get_sessions(cutoff, source)
tool_usage = self._get_tool_usage(cutoff, source)
skill_usage = self._get_skill_usage(cutoff, source)
message_stats = self._get_message_stats(cutoff, source)
if not sessions:
- return {
- "days": days, "source_filter": source, "empty": True, "overview": {}, "models": [],
- "platforms": [], "tools": [], "skills": self._compute_skill_breakdown([]),
- "activity": {}, "top_sessions": [],
- }
-
+ return {"days": days, "source_filter": source, "empty": True, "overview": {}, "models": [], "platforms": [], "tools": [],
+ "skills": self._compute_skill_breakdown([]), "activity": {}, "top_sessions": []}
models = self._compute_model_breakdown(sessions, cutoff, source)
return {
"days": days, "source_filter": source, "empty": False, "generated_at": time.time(),
@@ -228,10 +200,8 @@ class InsightsEngine:
"""Analytics-usage payload (tools + skills) without a full generate(); the
instr()-prefiltered skill query loads only skill_view/skill_manage messages."""
cutoff = time.time() - (days * 86400)
- return {
- "tools": self._compute_tool_breakdown(self._get_tool_usage(cutoff, source)),
- "skills": self._compute_skill_breakdown(self._get_skill_usage(cutoff, source)),
- }
+ return {"tools": self._compute_tool_breakdown(self._get_tool_usage(cutoff, source)),
+ "skills": self._compute_skill_breakdown(self._get_skill_usage(cutoff, source))}
# ------------------------------------------------------------------ SQL
@@ -245,19 +215,15 @@ class InsightsEngine:
tool_counts = Counter()
for row in self._query("_GET_TOOL_NAMES", cutoff, source):
tool_counts[row["tool_name"]] += row["count"]
-
tool_calls_counts = Counter()
for row in self._query("_GET_TOOL_CALLS", cutoff, source):
try:
tool_calls_counts.update(filter(None, (fn.get("name") for fn in _iter_functions(row["tool_calls"]))))
except (TypeError, AttributeError):
continue
-
if tool_calls_counts and tool_counts:
- tool_counts = Counter({
- tool: max(tool_counts.get(tool, 0), tool_calls_counts.get(tool, 0))
- for tool in set(tool_counts) | set(tool_calls_counts)
- })
+ tool_counts = Counter({tool: max(tool_counts.get(tool, 0), tool_calls_counts.get(tool, 0))
+ for tool in set(tool_counts) | set(tool_calls_counts)})
elif tool_calls_counts:
tool_counts = tool_calls_counts
return [{"tool_name": name, "count": count} for name, count in tool_counts.most_common()]
@@ -274,9 +240,7 @@ class InsightsEngine:
skill_name = (_parse_json(func.get("arguments"), dict) or {}).get("name")
if not isinstance(skill_name, str) or not skill_name.strip():
continue
- entry = skill_counts.setdefault(
- skill_name, {"skill": skill_name, "view_count": 0, "manage_count": 0, "last_used_at": None},
- )
+ entry = skill_counts.setdefault(skill_name, {"skill": skill_name, "view_count": 0, "manage_count": 0, "last_used_at": None})
entry["view_count" if tool_name == "skill_view" else "manage_count"] += 1
if timestamp is not None and (entry["last_used_at"] is None or timestamp > entry["last_used_at"]):
entry["last_used_at"] = timestamp
@@ -284,13 +248,10 @@ class InsightsEngine:
def _get_message_stats(self, cutoff: float, source: str = None) -> Dict:
rows = self._query("_GET_MESSAGE_STATS", cutoff, source)
- return dict(rows[0]) if rows else {
- "total_messages": 0, "user_messages": 0, "assistant_messages": 0, "tool_messages": 0,
- }
+ return dict(rows[0]) if rows else {"total_messages": 0, "user_messages": 0, "assistant_messages": 0, "tool_messages": 0}
def _get_model_usage(self, cutoff: float, source: str = None) -> List[Dict]:
- """Per-model usage rows; [] when the table is missing (older DB) so the
- caller falls back to the per-session aggregate."""
+ """Per-model usage rows; [] when the table is missing (older DB) so the caller falls back to the per-session aggregate."""
try:
return [dict(row) for row in self._query("_GET_MODEL_USAGE", cutoff, source)]
except sqlite3.OperationalError:
@@ -304,16 +265,12 @@ class InsightsEngine:
# main-loop usage only — sum the breakdown when available so overview
# totals match the per-model table and aux spend isn't undercounted.
rows = models or sessions
- total_input, total_output, total_cache_read, total_cache_write = (
- sum(int(r.get(k) or 0) for r in rows) for k in _TOKEN_KEYS
- )
+ total_input, total_output, total_cache_read, total_cache_write = (sum(int(r.get(k) or 0) for r in rows) for k in _TOKEN_KEYS)
total_tokens = total_input + total_output + total_cache_read + total_cache_write
total_tool_calls = sum(s.get("tool_call_count") or 0 for s in sessions)
total_messages = sum(s.get("message_count") or 0 for s in sessions)
-
total_cost = actual_cost = 0.0
- models_with_pricing, models_without_pricing = set(), set()
- status_counts = Counter()
+ models_with_pricing, models_without_pricing, status_counts = set(), set(), Counter()
for s in sessions:
model = s.get("model") or ""
estimated, status = _estimate_cost(s)
@@ -324,26 +281,16 @@ class InsightsEngine:
(models_with_pricing if known else models_without_pricing).add(_short_model(model))
if models:
total_cost = sum(float(m.get("cost") or 0.0) for m in models)
-
# Guard against negative durations from clock drift.
- durations = [
- s["ended_at"] - s["started_at"]
- for s in sessions
- if s.get("started_at") and s.get("ended_at") and s["ended_at"] > s["started_at"]
- ]
+ durations = [s["ended_at"] - s["started_at"] for s in sessions
+ if s.get("started_at") and s.get("ended_at") and s["ended_at"] > s["started_at"]]
started = [s["started_at"] for s in sessions if s.get("started_at")]
n = len(sessions)
return {
- "total_sessions": n,
- "total_messages": total_messages,
- "total_tool_calls": total_tool_calls,
- "total_input_tokens": total_input,
- "total_output_tokens": total_output,
- "total_cache_read_tokens": total_cache_read,
- "total_cache_write_tokens": total_cache_write,
- "total_tokens": total_tokens,
- "estimated_cost": total_cost,
- "actual_cost": actual_cost,
+ "total_sessions": n, "total_messages": total_messages, "total_tool_calls": total_tool_calls,
+ "total_input_tokens": total_input, "total_output_tokens": total_output,
+ "total_cache_read_tokens": total_cache_read, "total_cache_write_tokens": total_cache_write,
+ "total_tokens": total_tokens, "estimated_cost": total_cost, "actual_cost": actual_cost,
"total_hours": sum(durations) / 3600 if durations else 0,
"avg_session_duration": sum(durations) / len(durations) if durations else 0,
"avg_messages_per_session": total_messages / n if sessions else 0,
@@ -359,19 +306,15 @@ class InsightsEngine:
"included_cost_sessions": status_counts["included"],
}
- def _compute_model_breakdown(
- self, sessions: List[Dict], cutoff: float, source: str = None
- ) -> List[Dict]:
+ def _compute_model_breakdown(self, sessions: List[Dict], cutoff: float, source: str = None) -> List[Dict]:
"""Tokens/cost per model from session_model_usage, so a session that
switched models via ``/model`` splits across every model it used.
Sessions without per-model rows (pre-table data) fall back to their
single recorded aggregate. Tool calls aren't tied to an API call, so
they stay attributed to the session's recorded model."""
count_keys = _TOKEN_KEYS + ("reasoning_tokens", "api_call_count")
- model_data = defaultdict(lambda: {
- "sessions": set(), **dict.fromkeys(_TOKEN_KEYS, 0), "reasoning_tokens": 0, "total_tokens": 0,
- "api_calls": 0, "tool_calls": 0, "cost": 0.0, "actual_cost": 0.0,
- })
+ model_data = defaultdict(lambda: {"sessions": set(), **dict.fromkeys(_TOKEN_KEYS, 0), "reasoning_tokens": 0, "total_tokens": 0,
+ "api_calls": 0, "tool_calls": 0, "cost": 0.0, "actual_cost": 0.0})
def _accumulate(model, provider, base_url, session_id, counts: Dict[str, int], *,
stored_cost=None, actual_cost=None, cost_status=None):
@@ -383,24 +326,15 @@ class InsightsEngine:
d["total_tokens"] += sum(counts[k] for k in _TOKEN_KEYS)
d["api_calls"] += counts["api_call_count"]
if stored_cost is None:
- estimate, status = _estimate_cost(
- model, counts["input_tokens"], counts["output_tokens"],
- cache_read_tokens=counts["cache_read_tokens"], cache_write_tokens=counts["cache_write_tokens"],
- provider=provider or None, base_url=base_url,
- )
+ estimate, status = _estimate_cost(model, counts["input_tokens"], counts["output_tokens"], cache_read_tokens=counts["cache_read_tokens"],
+ cache_write_tokens=counts["cache_write_tokens"], provider=provider or None, base_url=base_url)
else:
estimate, status = float(stored_cost or 0.0), cost_status or "unknown"
d["cost"] += estimate
d["actual_cost"] += float(actual_cost or 0.0)
d["cost_status"] = status
- if has_known_pricing(model, provider or None, base_url):
- d["has_pricing"] = True
- else:
- d.setdefault("has_pricing", False)
-
- usage_totals = defaultdict(lambda: dict.fromkeys(count_keys, 0) | {
- "estimated_cost_usd": 0.0, "actual_cost_usd": 0.0,
- })
+ d["has_pricing"] = has_known_pricing(model, provider or None, base_url) or d.get("has_pricing", False)
+ usage_totals = defaultdict(lambda: dict.fromkeys(count_keys, 0) | {"estimated_cost_usd": 0.0, "actual_cost_usd": 0.0})
for r in self._get_model_usage(cutoff, source):
totals: Dict[str, Any] = usage_totals[r["session_id"]]
counts = {key: r[key] or 0 for key in count_keys}
@@ -408,12 +342,9 @@ class InsightsEngine:
totals[key] += counts[key]
totals["estimated_cost_usd"] += r["estimated_cost_usd"] or 0.0
totals["actual_cost_usd"] += r["actual_cost_usd"] or 0.0
- _accumulate(
- r["model"], r["billing_provider"], r.get("billing_base_url"), r["session_id"], counts,
- stored_cost=r["estimated_cost_usd"] if r.get("cost_status") or r.get("cost_source") else None,
- actual_cost=r["actual_cost_usd"], cost_status=r.get("cost_status"),
- )
-
+ _accumulate(r["model"], r["billing_provider"], r.get("billing_base_url"), r["session_id"], counts,
+ stored_cost=r["estimated_cost_usd"] if r.get("cost_status") or r.get("cost_source") else None,
+ actual_cost=r["actual_cost_usd"], cost_status=r.get("cost_status"))
# Reconcile against the aggregate row: covers legacy sessions,
# interrupted migrations, and absolute cumulative updates without
# double-counting already-attributed route deltas.
@@ -424,31 +355,20 @@ class InsightsEngine:
residual_cost = max(0.0, float(s.get("estimated_cost_usd") or 0.0) - totals["estimated_cost_usd"])
residual_actual = max(0.0, float(s.get("actual_cost_usd") or 0.0) - totals["actual_cost_usd"])
if any(residual.values()) or residual_cost or residual_actual:
- _accumulate(
- s.get("model"), s.get("billing_provider"), s.get("billing_base_url"), s["id"], residual,
- stored_cost=residual_cost, actual_cost=residual_actual, cost_status=s.get("cost_status"),
- )
-
+ _accumulate(s.get("model"), s.get("billing_provider"), s.get("billing_base_url"), s["id"], residual,
+ stored_cost=residual_cost, actual_cost=residual_actual, cost_status=s.get("cost_status"))
for s in sessions:
- tool_calls = s.get("tool_call_count") or 0
- if tool_calls:
- model_data[_short_model(s.get("model"))]["tool_calls"] += tool_calls
-
- result = []
- for model, data in model_data.items():
- entry = {"model": model, **data, "sessions": len(data["sessions"])}
- # Models seen only via tool-call attribution never hit _accumulate —
- # default these so the output shape is uniform for JSON consumers.
- entry.setdefault("has_pricing", False)
- entry.setdefault("cost_status", "unknown")
- result.append(entry)
- result.sort(key=lambda x: (x["total_tokens"], x["sessions"]), reverse=True)
- return result
+ if s.get("tool_call_count"):
+ model_data[_short_model(s.get("model"))]["tool_calls"] += s["tool_call_count"]
+ # Models seen only via tool-call attribution never hit _accumulate —
+ # default has_pricing/cost_status so the output shape is uniform for JSON consumers.
+ defaults = (("has_pricing", False), ("cost_status", "unknown"))
+ result = [{"model": model, **data, "sessions": len(data["sessions"]), **{k: v for k, v in defaults if k not in data}}
+ for model, data in model_data.items()]
+ return sorted(result, key=lambda x: (x["total_tokens"], x["sessions"]), reverse=True)
def _compute_platform_breakdown(self, sessions: List[Dict]) -> List[Dict]:
- platform_data = defaultdict(lambda: {
- "sessions": 0, "messages": 0, **dict.fromkeys(_TOKEN_KEYS, 0), "total_tokens": 0, "tool_calls": 0,
- })
+ platform_data = defaultdict(lambda: {"sessions": 0, "messages": 0, **dict.fromkeys(_TOKEN_KEYS, 0), "total_tokens": 0, "tool_calls": 0})
for s in sessions:
d = platform_data[s.get("source") or "unknown"]
d["sessions"] += 1
@@ -457,53 +377,32 @@ class InsightsEngine:
d[k] += s.get(k) or 0
d["total_tokens"] += s.get(k) or 0
d["tool_calls"] += s.get("tool_call_count") or 0
-
- result = [{"platform": platform, **data} for platform, data in platform_data.items()]
- result.sort(key=lambda x: x["sessions"], reverse=True)
- return result
+ return sorted(({"platform": platform, **data} for platform, data in platform_data.items()), key=lambda x: x["sessions"], reverse=True)
def _compute_tool_breakdown(self, tool_usage: List[Dict]) -> List[Dict]:
"""Ranked tool list with percentages."""
total_calls = sum(t["count"] for t in tool_usage)
- return [
- {"tool": t["tool_name"], "count": t["count"], "percentage": (t["count"] / total_calls * 100) if total_calls else 0}
- for t in tool_usage
- ]
+ return [{"tool": t["tool_name"], "count": t["count"], "percentage": (t["count"] / total_calls * 100) if total_calls else 0} for t in tool_usage]
def _compute_skill_breakdown(self, skill_usage: List[Dict]) -> Dict[str, Any]:
"""Per-skill usage → summary + ranked list."""
total_skill_loads = sum(s["view_count"] for s in skill_usage)
total_skill_edits = sum(s["manage_count"] for s in skill_usage)
total_skill_actions = total_skill_loads + total_skill_edits
-
- top_skills = []
- for skill in skill_usage:
- total_count = skill["view_count"] + skill["manage_count"]
- top_skills.append({
- "skill": skill["skill"], "view_count": skill["view_count"], "manage_count": skill["manage_count"],
- "total_count": total_count,
- "percentage": (total_count / total_skill_actions * 100) if total_skill_actions else 0,
- "last_used_at": skill.get("last_used_at"),
- })
- top_skills.sort(
- key=lambda s: (s["total_count"], s["view_count"], s["manage_count"], s["last_used_at"] or 0, s["skill"]),
- reverse=True,
- )
+ top_skills = [{
+ "skill": skill["skill"], "view_count": skill["view_count"], "manage_count": skill["manage_count"], "total_count": total_count,
+ "percentage": (total_count / total_skill_actions * 100) if total_skill_actions else 0, "last_used_at": skill.get("last_used_at"),
+ } for skill in skill_usage for total_count in (skill["view_count"] + skill["manage_count"],)]
+ top_skills.sort(key=lambda s: (s["total_count"], s["view_count"], s["manage_count"], s["last_used_at"] or 0, s["skill"]), reverse=True)
return {
- "summary": {
- "total_skill_loads": total_skill_loads,
- "total_skill_edits": total_skill_edits,
- "total_skill_actions": total_skill_actions,
- "distinct_skills_used": len(skill_usage),
- },
+ "summary": {"total_skill_loads": total_skill_loads, "total_skill_edits": total_skill_edits,
+ "total_skill_actions": total_skill_actions, "distinct_skills_used": len(skill_usage)},
"top_skills": top_skills,
}
def _compute_activity_patterns(self, sessions: List[Dict]) -> Dict:
"""Activity by day of week, hour, and active-day streak."""
- day_counts = Counter() # 0=Monday ... 6=Sunday
- hour_counts = Counter()
- daily_counts = Counter() # "YYYY-MM-DD" -> count
+ day_counts, hour_counts, daily_counts = Counter(), Counter(), Counter() # weekday (0=Monday), hour, "YYYY-MM-DD"
for s in sessions:
ts = s.get("started_at")
if not ts:
@@ -512,11 +411,9 @@ class InsightsEngine:
day_counts[dt.weekday()] += 1
hour_counts[dt.hour] += 1
daily_counts[dt.strftime("%Y-%m-%d")] += 1
-
day_names = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
day_breakdown = [{"day": day_names[i], "count": day_counts.get(i, 0)} for i in range(7)]
hour_breakdown = [{"hour": i, "count": hour_counts.get(i, 0)} for i in range(24)]
-
max_streak = 0
if daily_counts:
dates = [datetime.strptime(d, "%Y-%m-%d") for d in sorted(daily_counts)]
@@ -524,15 +421,8 @@ class InsightsEngine:
for prev, cur in zip(dates, dates[1:]):
current_streak = current_streak + 1 if (cur - prev).days == 1 else 1
max_streak = max(max_streak, current_streak)
-
- return {
- "by_day": day_breakdown,
- "by_hour": hour_breakdown,
- "busiest_day": max(day_breakdown, key=lambda x: x["count"]),
- "busiest_hour": max(hour_breakdown, key=lambda x: x["count"]),
- "active_days": len(daily_counts),
- "max_streak": max_streak,
- }
+ return {"by_day": day_breakdown, "by_hour": hour_breakdown, "busiest_day": max(day_breakdown, key=lambda x: x["count"]),
+ "busiest_hour": max(hour_breakdown, key=lambda x: x["count"]), "active_days": len(daily_counts), "max_streak": max_streak}
_TOP_METRICS = (
("Most messages", lambda s: s.get("message_count") or 0, "{} msgs"),
@@ -546,11 +436,8 @@ class InsightsEngine:
timed = [s for s in sessions if s.get("started_at") and s.get("ended_at")]
if timed:
longest = max(timed, key=lambda s: s["ended_at"] - s["started_at"])
- top.append({
- "label": "Longest session", "session_id": longest["id"][:16],
- "value": format_duration_compact(longest["ended_at"] - longest["started_at"]),
- "date": _day(longest["started_at"]),
- })
+ top.append({"label": "Longest session", "session_id": longest["id"][:16],
+ "value": format_duration_compact(longest["ended_at"] - longest["started_at"]), "date": _day(longest["started_at"])})
for label, metric, fmt in self._TOP_METRICS:
best = max(sessions, key=metric)
value = metric(best)
@@ -568,108 +455,69 @@ class InsightsEngine:
def _cost_lines(o: Dict, templates: tuple) -> List[str]:
"""One formatted line per non-zero cost bucket (estimated, included, unknown)."""
est_cost = o.get("estimated_cost", 0.0)
- values = (
- _fmt_est_cost(est_cost) if est_cost > 0 else "",
- o.get("included_cost_sessions", 0), o.get("unknown_cost_sessions", 0),
- )
+ values = (_fmt_est_cost(est_cost) if est_cost > 0 else "", o.get("included_cost_sessions", 0), o.get("unknown_cost_sessions", 0))
return [tpl.format(v) for tpl, v in zip(templates, values) if v]
def format_terminal(self, report: Dict) -> str:
"""Format the insights report for terminal display (CLI)."""
if report.get("empty"):
- days = report.get("days", 30)
src = f" (source: {report['source_filter']})" if report.get("source_filter") else ""
- return f" No sessions found in the last {days} days{src}."
-
+ return f" No sessions found in the last {report.get('days', 30)} days{src}."
o = report["overview"]
period_label = f"Last {report['days']} days"
if report.get("source_filter"):
period_label += f" ({report['source_filter']})"
padding = 58 - len(period_label) - 2
left_pad = padding // 2
- lines = [
- "",
- " ╔══════════════════════════════════════════════════════════╗",
- " ║ 📊 Hermes Insights ║",
- f" ║{' ' * left_pad} {period_label} {' ' * (padding - left_pad)}║",
- " ╚══════════════════════════════════════════════════════════╝",
- "",
- ]
-
+ lines = ["", " ╔══════════════════════════════════════════════════════════╗", " ║ 📊 Hermes Insights ║",
+ f" ║{' ' * left_pad} {period_label} {' ' * (padding - left_pad)}║", " ╚══════════════════════════════════════════════════════════╝", ""]
if o.get("date_range_start") and o.get("date_range_end"):
start_str = datetime.fromtimestamp(o["date_range_start"]).strftime("%b %d, %Y")
end_str = datetime.fromtimestamp(o["date_range_end"]).strftime("%b %d, %Y")
lines += [f" Period: {start_str} — {end_str}", ""]
-
- lines += self._section("📋 Overview")
- lines.append(f" Sessions: {o['total_sessions']:<12} Messages: {o['total_messages']:,}")
- lines.append(f" Tool calls: {o['total_tool_calls']:<12,} User messages: {o['user_messages']:,}")
- lines.append(f" Input tokens: {o['total_input_tokens']:<12,} Output tokens: {o['total_output_tokens']:,}")
- lines.append(f" Total tokens: {o['total_tokens']:,}")
+ lines += self._section("📋 Overview") + [
+ f" Sessions: {o['total_sessions']:<12} Messages: {o['total_messages']:,}",
+ f" Tool calls: {o['total_tool_calls']:<12,} User messages: {o['user_messages']:,}",
+ f" Input tokens: {o['total_input_tokens']:<12,} Output tokens: {o['total_output_tokens']:,}",
+ f" Total tokens: {o['total_tokens']:,}",
+ ]
if o["total_hours"] > 0:
lines.append(f" Active time: ~{format_duration_compact(o['total_hours'] * 3600):<11} Avg session: ~{format_duration_compact(o['avg_session_duration'])}")
lines += [f" Avg msgs/session: {o['avg_messages_per_session']:.1f}", ""]
-
# Cost buckets: show included/unknown sessions instead of collapsing to $0.
- cost_lines = self._cost_lines(o, (
- " Estimated: {}", " Included: {} session(s) (subscription — no provider invoice)",
- " Unknown: {} session(s) (no pricing data)",
- ))
+ cost_lines = self._cost_lines(o, (" Estimated: {}", " Included: {} session(s) (subscription — no provider invoice)",
+ " Unknown: {} session(s) (no pricing data)"))
if cost_lines:
lines += self._section("💰 Cost") + cost_lines + [""]
-
if report["models"]:
- lines += self._section("🤖 Models Used")
- lines.append(f" {'Model':<30} {'Sessions':>8} {'Tokens':>12}")
- for m in report["models"]:
- lines.append(f" {m['model'][:28]:<30} {m['sessions']:>8} {m['total_tokens']:>12,}")
- lines.append("")
-
+ lines += self._section("🤖 Models Used") + [f" {'Model':<30} {'Sessions':>8} {'Tokens':>12}"]
+ lines += [f" {m['model'][:28]:<30} {m['sessions']:>8} {m['total_tokens']:>12,}" for m in report["models"]] + [""]
platforms = report["platforms"]
if len(platforms) > 1 or (platforms and platforms[0]["platform"] != "cli"):
- lines += self._section("📱 Platforms")
- lines.append(f" {'Platform':<14} {'Sessions':>8} {'Messages':>10} {'Tokens':>14}")
- for p in platforms:
- lines.append(f" {p['platform']:<14} {p['sessions']:>8} {p['messages']:>10,} {p['total_tokens']:>14,}")
- lines.append("")
-
+ lines += self._section("📱 Platforms") + [f" {'Platform':<14} {'Sessions':>8} {'Messages':>10} {'Tokens':>14}"]
+ lines += [f" {p['platform']:<14} {p['sessions']:>8} {p['messages']:>10,} {p['total_tokens']:>14,}" for p in platforms] + [""]
if report["tools"]:
- lines += self._section("🔧 Top Tools")
- lines.append(f" {'Tool':<28} {'Calls':>8} {'%':>8}")
- for t in report["tools"][:15]:
- lines.append(f" {t['tool']:<28} {t['count']:>8,} {t['percentage']:>7.1f}%")
+ lines += self._section("🔧 Top Tools") + [f" {'Tool':<28} {'Calls':>8} {'%':>8}"]
+ lines += [f" {t['tool']:<28} {t['count']:>8,} {t['percentage']:>7.1f}%" for t in report["tools"][:15]]
if len(report["tools"]) > 15:
lines.append(f" ... and {len(report['tools']) - 15} more tools")
lines.append("")
-
skills = report.get("skills", {})
top_skills = skills.get("top_skills", [])
if top_skills:
- lines += self._section("🧠 Top Skills")
- lines.append(f" {'Skill':<28} {'Loads':>7} {'Edits':>7} {'Last used':>11}")
+ lines += self._section("🧠 Top Skills") + [f" {'Skill':<28} {'Loads':>7} {'Edits':>7} {'Last used':>11}"]
for skill in top_skills[:10]:
last_used = _day(skill.get("last_used_at")) if skill.get("last_used_at") else "—"
- lines.append(
- f" {skill['skill'][:28]:<28} {skill['view_count']:>7,} {skill['manage_count']:>7,} {last_used:>11}"
- )
+ lines.append(f" {skill['skill'][:28]:<28} {skill['view_count']:>7,} {skill['manage_count']:>7,} {last_used:>11}")
summary = skills.get("summary", {})
- lines.append(
- f" Distinct skills: {summary.get('distinct_skills_used', 0)} "
- f"Loads: {summary.get('total_skill_loads', 0):,} "
- f"Edits: {summary.get('total_skill_edits', 0):,}"
- )
- lines.append("")
-
+ lines += [f" Distinct skills: {summary.get('distinct_skills_used', 0)} Loads: {summary.get('total_skill_loads', 0):,} "
+ f"Edits: {summary.get('total_skill_edits', 0):,}", ""]
act = report.get("activity", {})
if act.get("by_day"):
lines += self._section("📅 Activity Patterns")
bars = _bar_chart([d["count"] for d in act["by_day"]], max_width=15)
- for bar, d in zip(bars, act["by_day"]):
- lines.append(f" {d['day']} {bar:<15} {d['count']}")
- lines.append("")
-
- busy_hours = sorted(act["by_hour"], key=lambda x: x["count"], reverse=True)
- busy_hours = [h for h in busy_hours if h["count"] > 0][:5]
+ lines += [f" {d['day']} {bar:<15} {d['count']}" for bar, d in zip(bars, act["by_day"])] + [""]
+ busy_hours = [h for h in sorted(act["by_hour"], key=lambda x: x["count"], reverse=True) if h["count"] > 0][:5]
if busy_hours:
hour_strs = [f"{_hour12(h['hour'])} ({h['count']})" for h in busy_hours]
lines.append(f" Peak hours: {', '.join(hour_strs)}")
@@ -678,20 +526,15 @@ class InsightsEngine:
if act.get("max_streak") and act["max_streak"] > 1:
lines.append(f" Best streak: {act['max_streak']} consecutive days")
lines.append("")
-
if report.get("top_sessions"):
lines += self._section("🏆 Notable Sessions")
- for ts in report["top_sessions"]:
- lines.append(f" {ts['label']:<20} {ts['value']:<18} ({ts['date']}, {ts['session_id']})")
- lines.append("")
-
+ lines += [f" {ts['label']:<20} {ts['value']:<18} ({ts['date']}, {ts['session_id']})" for ts in report["top_sessions"]] + [""]
return "\n".join(lines)
def format_gateway(self, report: Dict) -> str:
"""Format the insights report for gateway/messaging (shorter)."""
if report.get("empty"):
return f"No sessions found in the last {report.get('days', 30)} days."
-
o = report["overview"]
lines = [
f"📊 **Hermes Insights** — Last {report['days']} days\n",
@@ -701,39 +544,22 @@ class InsightsEngine:
if o["total_hours"] > 0:
lines.append(f"**Active time:** ~{format_duration_compact(o['total_hours'] * 3600)} | **Avg session:** ~{format_duration_compact(o['avg_session_duration'])}")
lines.append("")
-
cost_parts = self._cost_lines(o, ("{} estimated", "{} included (subscription)", "{} unknown"))
if cost_parts:
lines += [f"**Cost:** {' | '.join(cost_parts)}", ""]
-
if report["models"]:
- lines.append("**🤖 Models:**")
- for m in report["models"][:5]:
- lines.append(f" {m['model'][:25]} — {m['sessions']} sessions, {m['total_tokens']:,} tokens")
- lines.append("")
-
+ lines += ["**🤖 Models:**"] + [f" {m['model'][:25]} — {m['sessions']} sessions, {m['total_tokens']:,} tokens" for m in report["models"][:5]] + [""]
if len(report["platforms"]) > 1:
- lines.append("**📱 Platforms:**")
- for p in report["platforms"]:
- lines.append(f" {p['platform']} — {p['sessions']} sessions, {p['messages']:,} msgs")
- lines.append("")
-
+ lines += ["**📱 Platforms:**"] + [f" {p['platform']} — {p['sessions']} sessions, {p['messages']:,} msgs" for p in report["platforms"]] + [""]
if report["tools"]:
- lines.append("**🔧 Top Tools:**")
- for t in report["tools"][:8]:
- lines.append(f" {t['tool']} — {t['count']:,} calls ({t['percentage']:.1f}%)")
- lines.append("")
-
+ lines += ["**🔧 Top Tools:**"] + [f" {t['tool']} — {t['count']:,} calls ({t['percentage']:.1f}%)" for t in report["tools"][:8]] + [""]
skills = report.get("skills", {})
if skills.get("top_skills"):
lines.append("**🧠 Top Skills:**")
for skill in skills["top_skills"][:5]:
suffix = f", last used {_day(skill['last_used_at'])}" if skill.get("last_used_at") else ""
- lines.append(
- f" {skill['skill']} — {skill['view_count']:,} loads, {skill['manage_count']:,} edits{suffix}"
- )
+ lines.append(f" {skill['skill']} — {skill['view_count']:,} loads, {skill['manage_count']:,} edits{suffix}")
lines.append("")
-
act = report.get("activity", {})
if act.get("busiest_day") and act.get("busiest_hour"):
lines.append(f"**📅 Busiest:** {act['busiest_day']['day']}s ({act['busiest_day']['count']} sessions), {_hour12(act['busiest_hour']['hour'])} ({act['busiest_hour']['count']} sessions)")
@@ -741,5 +567,4 @@ class InsightsEngine:
lines.append(f"**Active days:** {act['active_days']}")
if act.get("max_streak", 0) > 1:
lines.append(f"**Best streak:** {act['max_streak']} consecutive days")
-
return "\n".join(lines)