Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Maudeunfledged834/startup-founder-skills --skill sentiment-monitoringgit clone --depth 1 https://github.com/Maudeunfledged834/startup-founder-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/maudeunfledged834/startup-founder-skills/sentiment-monitoring)<a href="https://agentmods.dev/skills/maudeunfledged834/startup-founder-skills/sentiment-monitoring"><img src="https://agentmods.dev/badge/skills/maudeunfledged834/startup-founder-skills/sentiment-monitoring/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/maudeunfledged834/startup-founder-skills/sentiment-monitoring"><img src="https://agentmods.dev/badge/skills/maudeunfledged834/startup-founder-skills/sentiment-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.01762 |
| Opus 5.5 | $0.00022 | $0.00705 |
| Sonnet 5.5 | $0.00011 | $0.00352 |
| Haiku 4.5 | $0.00006 | $0.00176 |
Grade A, and why
sentiment-monitoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to sentiment-monitoring — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sentiment Monitoring
When to Use
- Founder wants to track what customers and the public are saying about their product
- Founder wants to catch bad reviews early and respond before they spread
- Founder wants to understand community sentiment trends over time
- Founder wants to monitor specific review platforms for new reviews
This is different from review-mining (mining competitor reviews for pain points). This skill monitors your OWN product's reputation.
Context Required
- Product name and any common misspellings or abbreviations
- Platforms to monitor — the founder must provide the list of places to watch. Common options:
- Product Hunt (product page reviews and comments)
- Google Maps / Google Business reviews
- G2, Capterra, TrustRadius
- Trustpilot
- App Store / Play Store
- Reddit mentions
- Twitter/X mentions
- Hacker News mentions
- Industry-specific forums
- Monitoring frequency (daily for post-launch, weekly for steady state)
- Response policy — does the founder want draft responses for negative reviews?
- Escalation threshold — what severity warrants immediate attention?
Workflow
- Set up the monitoring list — the founder provides which platforms to watch. For each platform, note:
- Direct URL to the product's review/listing page
- Current rating and review count (baseline)
- How to check for new reviews (RSS, manual, API, or alert tool)
- Define the severity scale — categorize incoming sentiment:
- Critical (respond within 24h): public accusations of data loss, security issues, billing fraud, or legal threats. 1-star reviews with detailed complaints that could go viral.
- Negative (respond within 48h): legitimate complaints about bugs, missing features, poor support, or pricing frustration. 1-2 star reviews.
- Mixed (respond within 1 week): 3-star reviews with constructive feedback. "Good product but..."
- Positive (acknowledge): 4-5 star reviews. Thank the reviewer, ask for referrals.
- Scan platforms — check each platform on the founder's list for new reviews, mentions, or discussions since the last scan.
- Analyze each finding — for every new review or mention:
- Platform and date
- Sentiment: positive / mixed / negative / critical
- Core issue: what specifically is the person saying (quote verbatim)
- Validity: is this a legitimate product issue, user error, or bad-faith review?
- Impact: how visible is this? (high-traffic platform, many upvotes, or buried)
- Pattern: does this match other recent complaints? (signals a systemic issue)
- Draft responses — for negative and critical reviews, draft a response that:
- Acknowledges the issue without being defensive
- Shows the complaint was heard and understood
- Offers a specific next step (DM, email, fix timeline)
- Is written in the founder's voice, not corporate PR speak
- Flag patterns — if 3+ reviews mention the same issue, escalate it as a product issue, not just a review problem.
- Generate the sentiment report — summary of findings with trends.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 143 lines · 56 tokens per session scan A 2cd1d00b7ddf
sentiment-monitoring is a skill published in the GitHub repository Maudeunfledged834/startup-founder-skills (8 stars, last pushed 16d ago), licensed MIT. It adds 56 tokens to every session and 1,762 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to sentiment-monitoring, differing in 0 lines, and is treated as a copy.
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