Community Messages Sentiment

community_messages_sentiment

Semantic vector retrieval (the same ranker behind community-message-search) + Vertex (Gemini) per-message aspect scoring over the Onfire community-messages corpus. Pure top-k: target_messages is the number of most-relevant messages we retrieve, the number we LLM-score, and the cap on what we return and bill. Company enrichment against Onfire's people and company records resolves each sender's employer so you get a by-company breakdown in addition to the by-community one.

Routing gate — answer this BEFORE using this skill

Does the request name an OPINION / FEELING — positive/negative, complaint, praise, advocacy, satisfaction, frustration?

• No → this is the WRONG skill. If they want to FIND people/messages that merely MENTION or discuss a topic (e.g. "relevant people talking about my competitors", "who's discussing X", "companies whose employees mentioned Y"), use community-message-search (the search_community_messages tool). If they want who JOINED a community, use community-join-signals.
• Yes → continue here. This tool spends LLM time scoring polarity and bills per returned message; only pay that when the opinion angle is the point.

When to use this

• "What does the Slack community feel about Nexagon?"
• "Find positive vs negative discussions of Codeshield in the last 90 days."
• "Who's complained about Cloudward POCs? Get me their LinkedIns."
• "Find advocates / detractors for vendor X across communities since March."
• "Which companies have the most employees complaining about ?"
• Any question that mixes a topic + a date range + an opinion angle.

Skip this for:

• Plain mention/discovery with no opinion angle ("find people talking about X", "who's discussing Y") — use community-message-search (search_community_messages); this tool would waste LLM time scoring opinion you didn't ask for.
• Sentiment toward a person rather than a product/topic — the people here are the authors of the messages, not the subjects.

Two modes — pick one, then size it with the user

The tool has two retrieval modes (mode). Both LLM-score every candidate and bill per returned message (1 credit each), so size the run with the user first — framed as the number of messages to analyze, never as cost.

mode="top_k" (default) — "about N opinion messages"

Returns the target_messages messages most relevant to the subject. Use for a representative read or when the user names a count.

• target_messages is required. Call without it and the tool returns a needs_input envelope — ASK the user: "How many community messages should I analyze — ~50 for a quick read or ~150–300 for a fuller picture?" then resubmit with target_messages=N.
• Above 500 the tool returns needs_confirmation; confirm the volume, resubmit with confirmed=true.
• You may get slightly fewer than N (off-topic drops) — compare total_returned to target_messages and relay honestly.

mode="date_range" — "ALL the sentiment in this window"

Scores every message mentioning the keywords in [date_from, date_to] — the window is the scope, not a count. Use when the user wants exhaustive coverage: "everything said about X in Q1", a competitor brief, etc.

• The tool first COUNTS the window. Above the threshold it returns needs_confirmation with matched_in_window / will_analyze — tell the user the volume ("~6,300 messages match this window — analyze them?"), then resubmit with confirmed=true.
• Capped per run (max_range_messages, currently 10k). A window bigger than the cap is truncated to the most recent and flagged in note — narrow the dates for full coverage.
• keyword_mode ("all"=AND / "any"=OR) controls the lexical keyword match here (it's ignored in top_k).
• Relevance is enforced by the per-message LLM score (off-topic dropped), not a cosine floor, so discarded_off_topic can be higher than in top_k — tighten the keywords if it is.

Pre-step: resolve company names via match_company before calling

Always do this before passing exclude_companies.

Exclusion drops senders whose resolved current employer (from Onfire's people records, matched by company LinkedIn URL) is in your list. The only accepted key is the canonical company LinkedIn URL — the tool now rejects a bare company name with an error (it can't reliably match "Nexagon" to "Nexagon, Inc.").

Rule: if the user supplies a company name (or domain) for exclusion, call match_company for each one first and use the returned LinkedIn URL.

# User says: "exclude Nexagon employees"
match_company("Nexagon")
# → { "linkedin_url": "linkedin.com/company/nexagon", "name": "Nexagon", ... }

# Then pass the LinkedIn URL to the tool:
community_messages_sentiment(
    ...,
    exclude_companies=["linkedin.com/company/nexagon"],
)

If match_company returns no result for a name, skip that entry and tell the user it couldn't be resolved. Passing a bare company name now raises a validation error — resolve it first.

If the user already provides a company LinkedIn URL (e.g. "linkedin.com/company/nexagon"), skip the match_company call for that entry.

Input shape

top_k (default) — N most-relevant messages:

community_messages_sentiment(
    keywords=["Nexagon"],                                        # 1-10 topic tokens; steer the relevance search
    sentiment_subject="Positive or negative experience with Nexagon",
    date_from="2026-02-20",                                       # ISO date, inclusive
    date_to="2026-05-20",                                         # ISO date, inclusive
    mode="top_k",                                                 # default
    target_messages=150,                                          # REQUIRED in top_k — scored AND returned ≤ this. ASK the user if unknown.
    concurrency=None,                                             # optional; defaults to server ceiling
    exclude_companies=["linkedin.com/company/nexagon"],          # resolved LinkedIn URLs only (bare names error)
)

date_range — every keyword-matching message in the window:

community_messages_sentiment(
    keywords=["Nexagon"],                                        # the lexical match defining the candidate set
    sentiment_subject="Positive or negative experience with Nexagon",
    date_from="2026-01-01",
    date_to="2026-03-31",
    mode="date_range",                                            # scope = the whole window, not a count
    keyword_mode="all",                                           # "all"=AND / "any"=OR across keywords (date_range only)
    confirmed=True,                                               # set AFTER the user confirms the volume the count gate reported
    exclude_companies=["linkedin.com/company/nexagon"],
)                                                                 # target_messages is ignored in date_range

How to phrase sentiment_subject

This is the aspect the LLM judges against AND the phrasing that drives candidate relevance — be specific, don't just pass the keyword.

The subject framing is what makes this aspect-based: "I love Slack but Nexagon's UI is terrible" gets correctly classified as negative toward Nexagon, not mixed-overall. It also steers retrieval, so a sharp subject yields more on-topic candidates and fewer drops.

Output shape

{
  "dataset": { "id": "ds_abc123...", "row_count": 142, "expires_at": "...", "schema": [...] },
  "mode": "top_k",                 // or "date_range"
  "target_messages": 150,          // top_k only; date_range carries "matched_in_window" instead
  "total_returned": 142,
  "total_scanned": 150,
  "discarded_off_topic": 8,
  "scoring_errors": 0,
  "sentiment_subject": "...",
  "keywords": [...],
  "date_range": { "from": "...", "to": "..." },
  "counts": {
    "positive": 47, "negative": 71, "neutral": 24,
    "irrelevant": 0, "error": 0
  },
  "unique_senders_with_linkedin": 128,
  "unique_companies_resolved": 64,
  "by_community": [
    { "community_type": "Slack", "community_name": "DevSecOps Community",
      "positive": 18, "negative": 29, "neutral": 9, "irrelevant": 0, "error": 0 },
    ...
  ],
  "by_company": [
    { "company_name": "Acme Corp", "company_linkedin_url": "linkedin.com/company/acme",
      "company_industry": "Computer Software", "company_size": "1001-5000",
      "positive": 4, "negative": 12, "neutral": 3, "irrelevant": 0, "error": 0 },
    ...up to 15
  ],
  "top_positive": [
    { "linkedin_url": "...", "sender_name": "...", "sender_job_title": "Staff Engineer",
      "company_name": "Acme Corp", "company_industry": "Computer Software",
      "community_type": "Slack", "community_name": "DevSecOps Community",
      "message_text": "...", "confidence": 0.92, "reason": "..." },
    ...up to 5
  ],
  "top_negative": [ ...up to 5 ],
  "note": "Returned 142 of the 150 requested — ...",   // present ONLY when fewer than requested
  "wall_clock_seconds": 18.4,
  "concurrency_used": 1000
}

• total_scanned = messages actually LLM-scored (the top-k, ≤ target_messages).
• total_returned = on-topic messages kept, returned, and billed (= row_count).
• discarded_off_topic = scored candidates the model flagged irrelevant (mentioned the subject but carried no opinion) and dropped — scored but NOT billed.
• scoring_errors = candidates that could NOT be scored (a transient LLM error). Excluded and NOT billed. If scoring fails for everything and nothing survives, the tool returns an explicit {"error": ...} instead of a clean empty result — surface that as "scoring failed, please retry," never as "no sentiment found." counts shows irrelevant/error as 0 because neither is in the returned (billed) set; discarded_off_topic and scoring_errors are where they're reported.

Dataset columns

The persisted CSV includes: linkedin_url, sender_name, sender_job_title, company_linkedin_url, company_name, company_industry, company_size, company_country, community_type, community_name, message_timestamp, message_text, sentiment_subject, sentiment_value, confidence, reason, relevance_score, message_id.

relevance_score is the semantic match score (0–1) of the message to the subject. Company fields are populated for senders whose LinkedIn URL matches a person record Onfire holds. Senders with no match have null company fields.

The post-call playbook (READ THIS)

The dataset is the deliverable. The inline JSON is a preview.

After every successful call, do these four things in order:

1. State the headline. One sentence from counts + by_community + by_company, e.g. "Sentiment toward Nexagon skewed negative — 71 negative vs 47 positive across 128 unique authors from 64 companies, heaviest in r/devops and the DevSecOps Slack; Acme Corp and Beta Inc employees are the most vocal critics." If total_returned is less than target_messages (see note), say so explicitly ("only 142 relevant messages existed for this subject/window").

2. Quote 1-2 exemplars from top_positive and top_negative with the author's LinkedIn URL, job title, and company. These are sorted by confidence — they're the most defensible quotes you have.

3. Offer the dataset as a file. Call download_dataset(dataset_id="<id>") and surface the link as "Full per-message breakdown (linkedin, company, sentiment, confidence, reason): <link>". Do not skip this.

4. Mention the dataset_id and that follow-up questions are free. "The dataset (ds_abc123) is queryable for 7 days — ask me 'who said X', 'show me only Slack messages', or 'break it down by company industry'."

Follow-up questions: use query_datasets, do NOT re-run the tool

The dataset holds only on-topic rows (sentiment_value ∈ positive/negative/neutral) — there are no irrelevant/error rows to filter out.

Only re-run community_messages_sentiment when the user genuinely changes the inputs — different keywords, wider date range, different sentiment subject, or a larger target_messages to analyze/return more. A bigger target_messages is a new (billed) call; tell the user explicitly.

Reading the counts honestly

• discarded_off_topic is the count of scored candidates that mentioned the subject but didn't carry an opinion about it. A high ratio of discarded_off_topic to total_scanned means the subject/keywords were too broad — tighten them. These are NOT billed. (Candidates that failed scoring are counted separately in scoring_errors, also not billed.)
• counts is over the returned messages only (positive/negative/neutral); irrelevant/error show 0 there by design.
• unique_senders_with_linkedin is the people reach number — how many authors you can follow up with.
• unique_companies_resolved tells you how many employers are represented. A high ratio of resolved companies means the by_company breakdown is reliable; a low ratio means many senders had no matching person record.
• total_returned < target_messages means the relevant pool ran out — say so (don't imply we found exactly what was asked).

Hard rules

• Always offer download_dataset after a successful call.
• When total_returned < target_messages, lead with that ("only N relevant messages existed for this subject/window") before quoting numbers.
• For drill-downs, use query_datasets — never re-run for slicing.
• Don't quote message_text as gospel — it's truncated to 500 chars. Tell the user if they want the full text of a specific message it isn't available.
• Company fields are null for senders with no matching person record. Don't claim company-level insight for messages where company_name is null.

Common pitfalls

• Calling without asking how many. target_messages is required; if you don't pass it the tool just asks you to get a count. Decide the volume with the user FIRST (frame as volume, not cost).
• Stopping at the inline counts. The user will ask follow-ups. Surface the dataset_id.
• Re-running for "show me only negatives". Wasteful. Use query_datasets.
• Using a vague sentiment_subject like "Nexagon". The aspect framing is what makes this worth the LLM cost — and a sharper subject means fewer off-topic drops, so you hit your target.
• Forgetting date math. "Last 3 months" → convert before calling.
• Treating top_positive/top_negative as exhaustive. They are 5 exemplars sorted by confidence.
• Ignoring null company fields. unique_companies_resolved < unique_senders_with_linkedin means some senders weren't resolved — the by_company breakdown is a subset, not the full picture.
• Passing a bare company name to exclude_companies. This now raises a validation error. Always resolve via match_company first and pass the LinkedIn URL.

Worked examples

"What's the community saying about Nexagon lately?"

First, if they didn't say how many: "How many messages should I analyze — ~100 for a quick read or ~250 for a fuller picture?" Say they pick 200.

community_messages_sentiment(
    keywords=["Nexagon"],
    sentiment_subject="Positive or negative experience with Nexagon",
    date_from="<today minus 90d>",
    date_to="<today>",
    target_messages=200,
    exclude_companies=["linkedin.com/company/nexagon"],  # resolved via match_company; strips insider voices
)

Then:

1. "Across the last 3 months, sentiment toward Nexagon skewed negative — 71 negative vs 47 positive across 128 unique authors from 64 companies (192 of the 200 most-relevant messages were on-topic). Negative discussion concentrated in r/devops and the DevSecOps Slack; employees at Acme Corp and Beta Inc are the most vocal critics."
2. Quote 1 positive + 1 negative exemplar with LinkedIn URL, job title, and company.
3. download_dataset(dataset_id="...") → surface link.
4. "The dataset (ds_...) is queryable for 7 days — ask me 'which industries complain most', 'show me only the Slack threads', or anything else."

"Which companies have the most employees complaining about Cloudward POCs?"

community_messages_sentiment(
    keywords=["Cloudward", "POC"],
    sentiment_subject="Friction or dissatisfaction during Cloudward POCs",
    date_from="<today minus 180d>",
    date_to="<today>",
    target_messages=150,
)

Then use by_company from the inline result to identify top negative companies, and drill with:

query_datasets("SELECT company_name, company_industry, linkedin_url, sender_name, message_text
                FROM ds_<id>
                WHERE sentiment_value='negative'
                ORDER BY company_name, confidence DESC")

"How does sentiment compare for Nexagon vs Codeshield?"

Two separate calls (different keywords, different subject framing — split target_messages across them, e.g. 150 each), then narrate both headlines side by side and offer both download links.

What this skill does NOT do

• It does not look up sentiment about a specific person. The authors are returned; the targets are the keywords.
• It does not query a CRM. Output is community messages, not pipeline/opportunity data.
• It does not produce a customer-facing PDF or HTML report — pair it with docx or pdf skills if needed.
• It does not enrich the returned LinkedIn URLs with emails/phones. Pipe linkedin_url values into contact_data_enrichment if the user asks to reach out.