Analyze existing Your Project campaigns in place to diagnose prompt, agent, or quality issues — without copying or exporting. Per-scene content classification with agent attribution via debug_info.agent_name. Use when the user asks 'why is the LLM doing X in my campaigns', 'do a regression review across the last N campaigns', 'audit dialog/NPC behavior', 'compare heavy-dialog vs story-mode scenes', or wants prompt-side root-cause diagnosis. Distinct from download-campaign (which exports to disk) and wa-prod-data-query (which analyzes real-user activity).
Instrucciones de origen · Vista previa de solo lectura
name
wa-campaign-content-analysis
description
Analyze existing Your Project campaigns in place to diagnose prompt, agent, or quality issues — without copying or exporting. Per-scene content classification with agent attribution via debug_info.agent_name. Use when the user asks 'why is the LLM doing X in my campaigns', 'do a regression review across the last N campaigns', 'audit dialog/NPC behavior', 'compare heavy-dialog vs story-mode scenes', or wants prompt-side root-cause diagnosis. Distinct from download-campaign (which exports to disk) and wa-prod-data-query (which analyzes real-user activity).
when_to_use
Use when the user says: analyze the last N campaigns, review dialog quality, audit NPC behavior, regression review, per-scene analysis, agent attribution, why does the LLM X in my campaigns, do all the dialog scenes have Y, find scenes where Z is missing, per-agent breakdown. Do NOT use for: downloading/exporting single campaigns (use download-campaign); real-user activity/retention reports (use wa-prod-data-query); single-bug repros (use repro).
wa-campaign-content-analysis — analyze existing WA campaigns in place
Canonical home for the "look at content already in Firestore and tell me what's happening" workflow. Differs from download-campaign (which exports to disk) and wa-prod-data-query (which analyzes user activity, not scene content). This skill is for prompt / agent / content quality analysis across many existing campaigns without copying or mutating anything.
When to use this skill vs others:
Question class
Skill
"Why does the LLM do X in my campaigns?"
THIS SKILL
"Pull this campaign so I can read it"
download-campaign
"How many real users touched WA last week?"
wa-prod-data-query
"This specific scene has a bug"
repro
"What changed in the prompt last week?"
read git log of $PROJECT_ROOT/prompts/ directly
When to fire this skill
Triggers (any one):
User asks "review the last N campaigns with M+ scenes" or similar cross-campaign content review
User asks "do all my dialog scenes have Y" / "audit NPC behavior" / "is X actually being applied"
User asks "compare heavy-dialog vs story-mode scenes" / per-agent breakdown
User asks "why are NPCs silent" / "why does the PC not speak" / "which agent is generating the response"
After a prompt change — "verify the change actually lands in new scenes"
User names a specific campaign or setting ("the Danerys campaign in Meereen", "the Bran campaign", "the second Visenya run") — see Phase 8 for the user-named-campaign lookup recipe. This is the most common intake shape and the one most likely to mis-route on a literal-name lookup. Do NOT skip to a literal .where("name", "==", ...) query; users often describe by setting/character rather than exact title.
Same venv + auth + clock-skew setup as download-campaign. Do not duplicate it here — read ~/.hermes/skills/download-campaign/SKILL.md Phases 1-3 verbatim and execute them. The firestore connection, WORLDAI_DEV_MODE=true, apply_clock_skew_patch(), and boilerplate are identical.
auth.get_user_by_email("$USER@gmail.com")
Key environment facts:
Project ID: worldarchitecture-ai (with -ture-), NOT worldarchitect-ai
Service account: ~/serviceAccountKey.json
UID for $USER: vnLp2G3m21PJL6kxcuAqmWSOtm73
Subcollection name is story, NOT story_entries (Pitfall from download-campaign)
Phase 2 — Build the candidate campaign list
For "last N campaigns with ≥M scenes":
db = firestore.client()
camps = db.collection("users").document(uid).collection("campaigns").stream()
camp_list = []
for c in camps:
cid = c.id
cd = c.to_dict() or {}
name = cd.get("name") or cd.get("title") or"Untitled"# Aggregation count is the cheap+correct path; .limit(2000).stream() caps at 2000
agg = db.collection("users").document(uid).collection("campaigns").document(cid).collection("story").count().get()
entry_count = int(agg[0][0].value)
if entry_count < MIN_ENTRIES:
continue# Fetch last activity timestamp for sorting
q = (db.collection("users").document(uid).collection("campaigns").document(cid)
.collection("story").order_by("timestamp", direction=firestore.Query.DESCENDING).limit(1).stream())
docs = list(q)
last_ts_raw = Noneif docs:
d = docs[0].to_dict() or {}
last_ts_raw = d.get("timestamp") or d.get("created_at")
camp_list.append({"id": cid, "name": name, "entries": entry_count, "last_ts": norm(last_ts_raw)})
camp_list.sort(key=lambda c: c["last_ts"], reverse=True)
last_n = camp_list[:N]
norm() helper — Firestore timestamps are mixed types in this DB. Some docs store DatetimeWithNanoseconds, others store epoch seconds as int/float. Handle both:
defnorm(v):
if v isNone: return0.0ifhasattr(v, "timestamp"):
try: returnfloat(v.timestamp())
except Exception: passifisinstance(v, (int, float)):
if v > 1e12: returnfloat(v) / 1000.0# millis → secondsreturnfloat(v)
return0.0
Don't sort directly on raw values — TypeError: '<' not supported between instances of 'int' and 'DatetimeWithNanoseconds' fires.
De-duplication note: titles alone are not unique (e.g. "Bran the broken" vs "Bran the broken (ignore directive)" — these are intentional distinct campaigns the user is running, not copies). Only de-duplicate on campaign_id. The download-campaign Pitfall #6 (slug collision on duplicate titles) applies — but for in-place analysis you don't need to worry about it, since you're reading live data.
Phase 3 — Per-scene extraction (the schema insight)
Critical schema facts (verified 2026-07-13 from 2,464 gemini scenes across 10 campaigns):
Field
Meaning
Example values
actor
Who authored this doc
"user", "gemini" (LLM), sometimes "system"
mode
USER INTENT mode — what the player typed
"character", "god", "think", "(unset)"
text
Scene content (markdown narrative + quoted speech)
THE TRAP: mode field is NOT which agent wrote the scene. It records what intent mode the user's input triggered. Across 2,464 scenes in 10 long campaigns, only 3 distinct mode values appeared: character (73%), god (19%), think (7%). Zero scenes have mode=dialog or mode=heavydialog even though 38% of all scenes are written by DialogAgent or HeavyDialogAgent.
Always attribute by debug_info.agent_name, not mode. This is the single most important rule for any cross-campaign analysis.
Schema-zero case (verified 2026-07-26 on Visenya v9, qoQtHsU7DxZnR24VNU9w, 412 scenes): Some campaigns return story docs with debug_info = {} (empty dict — no agent_name, no llm_model, no system_instruction_files) AND full_state_updates = {} (empty dict). Symptom: every per-scene debug_info.agent_name lookup returns None, every per-agent histogram collapses to "unknown", and the typical agent distribution table is meaningless. The narrative quality is fine — the schema fields simply were never written.
Detection before per-agent analysis (cheap, mandatory):
sample = next(camp.collection("story").limit(5).stream()).to_dict() or {}
has_agent_tracking = bool((sample.get("debug_info") or {}).get("agent_name"))
has_state_updates = bool(sample.get("full_state_updates"))
ifnot has_agent_tracking:
print("WARN: empty debug_info — per-agent attribution unavailable")
print("Fallback: analyze raw .txt export at ~/llm_wiki/raw/campaigns/<id8>/")
ifnot has_state_updates:
print("WARN: empty full_state_updates — level / NPC timeline unavailable")
print("Fallback: parse `Status: Lvl N` headers from raw .txt scenes")
When either is empty, do not pretend per-agent findings are real. Report "schema-zero: per-agent attribution unavailable; analyzing raw text export instead." The fallback chain is detailed in references/visenya-v9-raw-text-analyzer.md and the regex patterns in references/personal-scale-challenge-pattern.md.
Phase 4 — Content classifiers
Common per-scene metrics:
Quoted-speech attribution (PC vs NPC)
DIALOG_PATTERNS = [
re.compile(r'"([^"\n]{2,500})"'), # straight double
re.compile(r"'([^'\n]{2,500})'"), # straight single
re.compile(r'"([^"\n]{2,500})"'), # curly double
re.compile(r"'([^'\n]{2,500})'"), # curly single
]
VERB_DIALOG_RE = re.compile(
r"\b([A-Z][a-zA-Z'\-]+(?:\s+[A-Z][a-zA-Z'\-]+)?)\s+"r"(?:said|asked|replied|shouted|whispered|cried|answered|murmured|muttered|stated|declared|"r"exclaimed|gasped|snapped|hissed|sneered|smiled|laughed|grumbled|rejoined|added|continued|noted|"r"remarked|observed|responded|countered|suggested|insisted|protested|begged|pleaded|warned|"r"told|commanded|ordered|demanded|inquired|queried|wondered|breathed|sighed|drawled|babbled)\b"r"[^.!?\n]{0,80}",
flags=re.MULTILINE,
)
NAME_COLON_RE = re.compile(r"^\s*([A-Z][a-zA-Z'\-]+(?:\s+[A-Z][a-zA-Z'\-]+)?)\s*:\s*([^\n]{2,500})",
flags=re.MULTILINE)
FIRST_PERSON_RE = re.compile(r"^(I |I'm |I've |I'll |I'd |My |We |Our |Me |Im |Ive |Ill |Id )",
re.IGNORECASE)
defcount_dialog(text, pc_name, npc_names_set):
"""Return (pc_lines, npc_lines, speakers_list) for a single scene text."""ifnot text:
return0, 0, []
pc_lines = npc_lines = 0
speakers = []
MECHANIC_LABELS = {"Intelligence Check","Wisdom Check","Charisma Check","Persuasion Check",
"Deception Check","Intimidation Check","Performance Check","Insight Check",
"Social HP","Resistance","Objective","Outcome","IMMUNITIES","DISSONANCE",
"IDENTITY","DIVINE BONUSES","LEVEL","DIVINE LEVERAGE","XP",
"Administrative Log","Strategic Note","Session Summary"}
# 1. Quoted speech (dedupe straight+curly overlap)
seen = set()
quoted = []
for pat in DIALOG_PATTERNS:
for m in pat.finditer(text):
line = m.group(1).strip()
key = line[:50]
if key in seen: continue
seen.add(key); quoted.append(line)
for q in quoted:
if FIRST_PERSON_RE.match(q):
pc_lines += 1else:
npc_lines += 1# 2. Name-prefixed dialog ("Name: '...'")for m in NAME_COLON_RE.finditer(text):
sn = m.group(1).strip()
line_text = m.group(2).strip().strip("'\"")
ifnot line_text: continueif sn in MECHANIC_LABELS: continueif pc_name and sn.lower() == pc_name.lower():
pc_lines += 1else:
npc_lines += 1
speakers.append(sn)
# 3. Indirect speech ("Name said that...")for m in VERB_DIALOG_RE.finditer(text):
sn = m.group(1).strip()
if sn in MECHANIC_LABELS: continueif pc_name and sn.lower() == pc_name.lower():
pc_lines += 1else:
npc_lines += 1
speakers.append(sn)
return pc_lines, npc_lines, list(set(speakers))
Limitations of this classifier (verified 2026-07-13):
Misses paraphrased NPC speech (no quotes, no Name said). For PC-silent analysis this is fine (NPC paraphrases still get attributed via verb patterns and NAME_COLON).
First-person quoted lines are heuristic-PC. False positives: third-person narration with self-quoted thought ("My mother always said, 'Don't trust X'"). False negatives: PC speaks in third person ("Bran Stark replies, 'I am the Three-Eyed Raven'"). For long-campaign diagnosis the false-positive rate is acceptable (median PC=0 stays 0 in any case).
Per-game-state pc_name resolution is needed for NAME_COLON/VERB_DIALOG attribution. Pull from users/{uid}/campaigns/{cid}/game_states/current_state.player_character_data.name.
Other useful per-scene metrics
Word count — len(text.split()). Median words/scene per agent reveals scene depth (Combat ~345, HeavyDialog ~436, PlanningAgent ~50).
Quoted-line density — (pc_lines + npc_lines) / words × 100. Low = action-only; high = dialog-heavy.
Scene-bucket histograms — bucket by NPC line count [(0,0),(1,1),(2,3),(4,7),(8,15),(16,31),(32,63),(64+)].
Phase 5 — Per-agent aggregation
Group scenes by debug_info.agent_name (not mode):
from collections import defaultdict
import statistics
by_agent = defaultdict(list)
for s in all_scenes:
by_agent[s["agent"]].append(s)
for agent, sl insorted(by_agent.items(), key=lambda kv: -len(kv[1])):
iflen(sl) < 10: continue
n = len(sl)
print(f"{agent}: scenes={n} med_pc={statistics.median([s['pc_lines'] for s in sl])} "f"med_npc={statistics.median([s['npc_lines'] for s in sl])} "f"%pc_silent={100*sum(1for s in sl if s['pc_lines']==0)/n:.1f}% "f"%two_way={100*sum(1for s in sl if s['pc_lines']>=1and s['npc_lines']>=1)/n:.1f}%")
Typical distribution across long campaigns:
Agent
% of scenes
Notes
HeavyDialogAgent
25-30%
"high-stakes conversations" — should have 2-way dialog; rarely does
GodModeAgent
18-22%
Directive queries; 99%+ PC-silent (player asks, AI answers)
StoryModeAgent
12-16%
Default narrative mode
DialogAgent
10-14%
Active dialog turns (routed via matches_game_state or MODE_DIALOG)
LevelUpAgent
6-10%
Level-up modal — minimal dialog
PlanningAgent
5-8%
Choice generation only — 100% PC-silent, <100 words
CharacterCreationAgent
3-5%
First scene only
CombatAgent
2-4%
Combat — short scenes, NPC monologue for villain banter
FactionManagementAgent
3-4%
Faction minigame
Diagnostic pattern: if a "dialog agent" has % PC-silent > 50%, the prompt is biased toward NPC monologue and needs fixing (see references/pc-silent-prompt-diagnosis.md).
Phase 6 — Verify a previously-shipped fix actually took effect
When the user asks "is the dialog fix working?" / "let me check after deploy" / "is X actually being applied" AFTER a prompt-fix PR has merged, run this verification phase BEFORE assuming the fix worked. The full verification recipe (with regex tightening, pre/post comparison, and 5-cause taxonomy) lives in references/prompt-fix-effectiveness-verification.md. Summary:
Find the merge SHA timestamp from git log of the changed prompt file.
Locate the user's most-recent campaign via users/{uid}/campaigns subcollection (NOT top-level campaigns — that's test fixtures only, per wa-prod-data-query Pitfall #1).
Stream story subcollection and split by merge time. Use the same actor=gemini + mode=<relevant> filter for both sides.
Compute NPC-attributed direct quotes (requires "..." AFTER speech verb within 80 chars — NOT just "Name said" paraphrase, which inflates counts).
Read 2-3 sample scenes verbatim to confirm the classifier isn't fooled by italicized action (*Florent swallows...*) or HUD panels.
If pre/post are statistically indistinguishable, the prompt fix landed but the LLM isn't following. Five causes, in priority order: wrong place in prompt, structural panels crowd, model capacity, wrong bug class fixed, cache miss on prompt update.
Report findings as a pre/post delta + the cause list as options for the user to choose from.
Class-level lesson: prompt-fix-shipped-but-LLM-ignores-it is a distinct bug class from prompt-bug-detected. Verification ≠ detection. The earlier recipe (references/pc-silent-prompt-diagnosis.md) covered detection; this phase covers verification.
Phase 7 — Root-cause diagnosis → prompt files
When per-agent analysis reveals a content-quality regression (e.g. "PC is silent in 70% of dialog scenes"), the cause is almost always in one of three prompt files:
$PROJECT_ROOT/prompts/dialog_system_instruction.md — dialog-specific guidance. Check for NPC-only bias. Search for player character, speak as the PC, PC voice, on behalf of the player — if all zero hits, the prompt never instructs the LLM to put words in the PC's mouth.
$PROJECT_ROOT/prompts/narrative_system_instruction.md (L60-80) and narrative_lite_system_instruction.md (L65-80) — look for "Narrative Authority" / "Player describes / GM describes" blocks. If the GM side reserves NPC reactions without explicitly granting PC dialogue, the LLM defaults to PC-silent.
$PROJECT_ROOT/agents.py — check each agent's REQUIRED_PROMPT_ORDER for whether any PC-voice prompt slot exists. HeavyDialogAgent at L2686-2697 has none.
Don't add backend enforcement. Per root-cause-first skill discipline, fix the prompt layer first. Adding a server-side "PC must speak N times" re-prompt loop is a tail-risk: it doubles token cost on every dialog scene and the LLM will eventually find a way to bypass.
Phase 7 — Persist findings
Three artifacts to produce:
Per-scene JSONL dump at ~/.hermes/<topic>_<date>/all_scenes_by_agent.jsonl — one row per scene with campaign_id, agent, pc_lines, npc_lines, speakers, word count.
Wiki source page at ~/llm_wiki/wiki/sources/<topic>-<date>.md — YAML frontmatter + diagnosis + verbatim sample scenes + prompt citations. Future agents can search this.
Also open a follow-up bead:
cd$HOME/your-project.com && br create "<one-line summary>" \
--type bug --priority 2 --description "<file paths + line numbers + link to wiki page>"
Pitfalls (this list IS the skill — review before running)
Don't de-duplicate by title — "Bran the broken" and "Bran the broken (ignore directive)" are intentionally distinct campaigns the user is running. De-dup only on campaign_id.
Campaign doc name vs title — the campaign-level document uses title (not name) for real-user campaigns. A .where("name", "==", ...) query returns 0 hits even when the campaign exists. See references/campaign-doc-field-naming.md and always read cd.get("title") or cd.get("name"). Verified 2026-07-15, $USER's account.
User-named lookup ≠ literal title — "the Danerys campaign in Meereen" is almost never a literal title match; use the Phase 8 three-step recipe (title pre-filter → scene-level keyword confirmation → opening-scene verification). Don't burn a clarifying menu on the user when the top match is 95% likely correct.
Mixed timestamp types — DatetimeWithNanoseconds vs int epoch vs float epoch-millis. Normalize via the norm() helper before sorting. Sorting raw fails with TypeError: '<' not supported.
/tmp is sandbox-scoped per execute_code call — write artifacts to ~/.hermes/<topic>_<date>/ or ~/llm_wiki/wiki/sources/ instead. Path returned by one execute_code call is gone in the next.
f-string {VAR} in execute_code — the sandbox f-string parser doesn't handle braces inside strings used inside an f-string literal. Write the wiki content via write_file (cleanest) or escape {{/}} everywhere.
Per-campaign PC name resolution — for accurate NAME_COLON/VERB_DIALOG attribution you need player_character_data.name from each campaign's game_states/current_state. Without it, all NAME_COLON lines fall to NPC counter and inflate the npc count. Some campaigns have None for pc_name; fall back to "unknown PC" and accept the inflated NPC count.
First-person PC classifier misses third-person PC speech — fine for PC-silent analysis (median stays 0), bad for quantifying how much PC does speak. If you need precise PC speech counts, sample 10-20 scenes manually.
Test users pollute the data — same as wa-prod-data-query Pitfall 3. For $USER's account this is mostly fine (we filter by uid), but if you ever broaden to multi-uid scans, apply the test-user filter (emails containing test, anon, dev-runner, example.com, jleechantest).
Schema field drift — story-doc schema has evolved over time. Older campaigns may use narrative_text instead of text, created_at instead of timestamp, part instead of id. Normalize field reads: text = d.get("scene_text") or d.get("narrative_text") or d.get("text") or d.get("content"). Same fallback chain for timestamps.
User-intent "mode" vs agent "mode" — these are TWO DIFFERENT FIELDS with the same name. story_doc.mode is user intent. agents.py also uses MODE class constant for agent identity. Don't conflate them.
Don't de-duplicate by title — "Bran the broken" and "Bran the broken (ignore directive)" are intentionally distinct campaigns the user is running. De-dup only on campaign_id.
Schema-zero story docs (debug_info = {}, full_state_updates = {}) — verified 2026-07-26 on Visenya v9. Per-agent attribution falls back to "unknown" for every scene; per-agent histograms and per-agent percentages become meaningless. Detection: run a 5-doc sample probe before the full per-agent aggregation — if (sample.get("debug_info") or {}).get("agent_name") returns None for all 5, the campaign is schema-zero. Fallback: analyze the raw .txt export at ~/llm_wiki/raw/campaigns/<id8>/<title>_<id8>.txt (use download-campaign first). The raw text has ====== SCENE N ====== delimiters and Status: Lvl N ... headers per scene. Patterns documented in references/personal-scale-challenge-pattern.md. Reporting fabricated per-agent percentages (e.g. "HeavyDialogAgent: 25-30%") when the underlying debug_info is empty is dishonest analysis, not a number to summarize.
Sample driver script
See scripts/per_scene_dialog_audit.py (in this skill) for a working end-to-end implementation:
Pulls last N campaigns ≥ M scenes
Streams story subcollection per campaign in ASC timestamp order
Classifies each gemini scene via count_dialog()
Aggregates per-agent
Writes summary JSON + JSONL dump
Related
download-campaign — exports a single campaign (or batch) to disk. Use when the user wants to read campaign content; this skill is for analyzing content in place.
wa-prod-data-query — real-user engagement/retention reports. Wrong domain (user activity, not scene content).
repro — single-bug repro for one specific user-reported scene. This skill is the cross-campaign version.
references/pc-silent-prompt-diagnosis.md — worked example of taking a per-agent breakdown ("PC silent 67%") and tracing it to specific prompt lines.
references/prompt-fix-effectiveness-verification.md — verification recipe for AFTER a prompt fix has shipped: pre/post comparison, regex tightening (NPC-attributed quotes require "..." AFTER speech verb), and 5-cause taxonomy for "PR merged but LLM still not following."
references/personal-scale-challenge-pattern.md — when the LLM auto-escalates to mythic-tier antagonists as the PC scales past the parity band; prompt-only fixes to preserve personal-scale challenge at high tier. Includes the five-prompt-edit recipe (Tier Compression, Consequence-Hiding Heuristic, mythic-NPC personhood, Force-a-Trade, anti-creep on major events) and the regex patterns for measuring personal-scale challenge density from raw text.
references/visenya-v9-raw-text-analyzer.md — pipeline reference for analyzing a campaign whose debug_info is empty; walks through /tmp/analyze_visenya_v9_v2.py style implementation against the download-campaign raw .txt export.
tests/test_agent_attribution.py — verify debug_info.agent_name precedence over mode field.
scripts/test_user_named_campaign_lookup.py — Phase 8 three-step recipe (get_campaign_title, title_prefilter, keyword_scan, confirm_opening_scene) + the campaign-level name vs title field fallback + the "guess and verify, no clarification menu" output contract.
