reputation-management
Run reputation programs: reviews, sentiment, brand safety.
Skill by indranilbanerjee
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/indranilbanerjee-reputation-management
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/reputation-management
What's inside
- Skill name
reputation-management- Description
- Run reputation programs: reviews, sentiment, brand safety. Crisis → crisis-response; one review → review-response. "get more Google reviews"
- License
- MIT
- Instructions
- 220 lines in SKILL.md · view on GitHub
Files (7)
- SKILL.md
- brand-safety.md
- crisis-communication.md
- recovery-playbooks.md
- review-management-platforms.md
- review-strategy.md
- sentiment-monitoring.md
SKILL.md (first lines)
# Reputation Management
> **Script location.** If your host does not set `${CLAUDE_PLUGIN_ROOT}`, the scripts are in this plugin's `scripts/` folder, next to `skills/`. Run them with Python 3.10 or newer: `python`, or `python3` where `python` is not found (macOS, many Linux systems).
## When to Use This Skill
Activate this skill when the user's request involves any of the following:
- Generating more customer reviews or managing existing reviews across platforms
- Responding to negative reviews (Google, Yelp, G2, Capterra, Trustpilot, Amazon, BBB, industry-specific sites)
- Preparing for or responding to a brand crisis (product recall, executive scandal, data breach, viral complaint, lawsuit)
- Assessing and mitigating brand safety risks in advertising and partnerships
- Monitoring brand sentiment across social media, review platforms, and press
- Building a reputation recovery plan after a negative event
- Handling negative press, unfavorable search results, or misinformation
- Managing employee reviews on platforms like Glassdoor or Indeed
- Designing proactive reputation-building strategies
- Evaluating brand safety settings for ad placements and content adjacency
- Navigating legal constraints on reputation responses (defamation, HIPAA, regulated industries)
- Addressing fake review attacks or review manipulation by competitors
## Brand Context (Auto-Applied)
Before producing any marketing output from this module:
1. **Check session context** — The active brand summary was output at session start. Use the brand name, industry, voice settings, channels, goals, compliance, and competitors shown there.
2. **If you need the full profile**, read: `~/.claude-marketing/brands/{slug}/profile.json`
3. **Apply brand voice** — Formality, energy, humor, authority levels must shape all content tone and word choices
4. **Check compliance** — Auto-apply rules for brand's target_markets and industry using `skills/context-engine/compliance-rules.md`
5. **Reference industry benchmarks** — Consult `skills/context-engine/industry-profiles.md` for the brand's industry
6. **Use platform specs** — Reference `skills/context-engine/platform-specs.md` for character limits and format requirements
7. **Check campaign history** — Run `python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns` before planning new work
8. **If no brand exists**, say: "No brand profile found. Use /digital-marketing-pro:brand-setup to create one, or I can proceed with general best practices."
9. **Check brand guidelines** — If `~/.claude-marketing/brands/{slug}/guidelines/_manifest.json` exists, load and enforce: `restrictions.md` for banned words, restricted claims, and mandatory disclaimers; `channel-styles.md` for channel-specific tone overrides (may differ from base voice); `messaging.md` for approved key messages, taglines, and positioning language; `voice-and-tone.md` for detailed voice rules beyond the 4 numeric scores. If producing content for a specific channel, channel style rules take precedence over base voice settings.
Do not ask the user for information that already exists in their brand profile.
## Required Context
Before executing, gather the following from the user (ask if not provided):
- **Current situation**: Is this proactive reputation building, reactive crisis response, or ongoing reputation maintenance?
- **Severity level**: For crisis situations, what is the scope and intensity? (Single negative review, trending negative conversation, press coverage, legal action)
- **Industry**: Needed for compliance constraints (healthcare/HIPAA, finance/FINRA, legal/bar rules, government)
- **Platform landscape**: Which review platforms and social channels are most relevant to the business
- **Current review profile**: Average rating, review volume, review trend, and response rate
- **Brand voice**: Tone and communication style guidelines
- **Stakeholders**: Who needs to be involved in approvals (legal, PR, C-suite, customer service)
- **Existing monitoring**: What tools or processes are in place for sentiment tracking
- **History**: Any past crises or reputation issues and how they were handled
- **Resources**: Team capacity for review management, crisis response, and ongoing monitoring
## Capabilities
### Review Generation
- **FTC-compliant solicitation**: Ask for reviews without incentivizing positive reviews specifically. Incentives for leaving a review (not a positive review) require disclosure. Never gate reviews by satisfaction level (asking happy customers to review publicly while routing unhappy customers to private feedback is prohibited)
- **Platform-specific timing**: Google reviews (post-purchase/service completion), G2/Capterra (after meaningful product usage, typically 30-60 days), Amazon (post-delivery with review request button), Yelp (never directly solicit -- Yelp penalizes solicited reviews)
- **Review request sequences**: Email, SMS, in-app prompts, QR codes on receipts/packaging, post-interaction follow-ups
- **Review volume strategy**: Consistent review velocity matters more than spikes. Build automated review request flows triggered by key customer milestones
- **Review platform prioritization**: Focus efforts on platforms that influence purchase decisions for the specific industry (Google for local, G2 for SaaS, TripAdvisor for hospitality, Healthgrades for medical)
### Negative Review Response Framework
- **Response timing**: Respond within 24 hours for public reviews. Speed demonstrates attentiveness. Delayed responses appear dismissive
- **Response structure**: Acknowledge the concern, apologize for the experience (not admitting fault), explain what happened if appropriate, offer a resolution, and move the conversation offline
- **Tone calibration**: Professional and empathetic regardless of review tone. Never argue, get defensive, or blame the customer publicly. The response is for future readers, not just the reviewer
- **Legal review triggers**: Know when a response requires legal review (threatened litigation, allegations of illegal activity, regulated industry topics, potential defamation claims)
- **Follow-up protocol**: After resolving the issue offline, politely ask if the customer would consider updating their review. Never demand or pressure
- **Review disputes**: Platform-specific processes for flagging fake, defamatory, or policy-violating reviews for removal
### Crisis Communication (3-Tier Framework)
**Tier 1 -- Minor Crisis** (isolated complaint, single negative article, localized social media issue)
- **Severity indicators**: Limited reach, no media pickup, contained to one platform or conversation
- **Response timeline**: Respond within 24 hours with prepared acknowledgment
- **Actions**: Direct customer response, monitor for spread, prepare holding statement if needed
- **Stakeholders**: Customer service lead, social media manager
**Tier 2 -- Moderate Crisis** (trending complaint, multiple media outlets, influencer amplification, regional issue)
- **Severity indicators**: Growing reach, media inquiries, multiple customer complaints on the same issue, hashtag trending
- **Response timeline**: Public statement within 4 hours. Internal alignment within 2 hours
- **Actions**: Activate crisis team, issue holding statement, prepare full response, monitor real-time, brief executives
- **Stakeholders**: VP/Director of Marketing, PR team, legal counsel, customer service leadership
- **Stakeholder messaging matrix**: Different messages for customers (empathy + action), employees (facts + guidance), media (official statement), investors (impact assessment + response plan), partners (reassurance + timeline)
- **Brand voice shift protocol**: Move from standard marketing voice to crisis voice -- more human, more direct, less polished, zero humor, zero promotion
**Tier 3 -- Severe Crisis** (data breach, product safety issue, executive misconduct, viral outrage, regulatory action)
- **Severity indicators**: National/international media coverage, regulatory involvement, potential legal liability, significant customer impact
- **Response timeline**: Initial acknowledgment within 1 hour. Full response within 4 hours. Ongoing updates every 24-48 hours
- **Actions**: CEO-level response, legal coordination, regulatory notification (if required), customer notification, operational remediation, third-party investigation (if needed)
- **72-hour timeline**: Hour 0-1 (acknowledge, assemble team), Hour 1-4 (fact-finding, holding statement), Hour 4-24 (detailed response, customer outreach, media statement), Hour 24-48 (operational updates, stakeholder briefings), Hour 48-72 (recovery plan announcement, ongoing communication cadence)
- **Stakeholders**: CEO/C-suite, general counsel, board of directors (if public company), PR agency, regulatory contacts
### Brand Safety (4-Layer Framework)
**Layer 1 -- Ad Placement Safety**
- Ensuring ads do not appear alongside harmful, offensive, or brand-inappropriate content
- Platform-specific brand safety settings (Google content exclusions, Meta inventory filters, YouTube placement exclusions)
- Third-party verification tools (IAS, DoubleVerify) for programmatic environments
- Keyword exclusion lists and site exclusion lists
**Layer 2 -- Association Safety**
- Vetting partners, influencers, and sponsors for brand alignment and risk
- Due diligence on co-marketing partners, event sponsors, and media placements
- Ongoing monitoring of brand-associated entities for emerging controversies
**Layer 3 -- Content Safety**
- Ensuring brand-produced content does not inadvertently create brand safety issues
- Content review process for cultural sensitivity, inclusivity, and potential misinterpretation
- Social media post approval workflows and crisis-proofing content calendars
**Layer 4 -- Data Safety**
- Protecting customer data in marketing operations
- Compliance with GDPR, CCPA, and other privacy regulations in marketing contexts
- Vendor data handling assessments for marketing technology partners
### Sentiment Monitoring Framework
- **Monitoring scope**: Social media mentions, review platforms, news/press, forums, employee review sites, search results
- **Sentiment scoring**: Positive, neutral, negative classification with intensity weighting
- **Alert thresholds**: Define spike thresholds that trigger escalation (e.g., 3x normal negative mention volume in 24 hours)From indranilbanerjee/digital-marketing-pro, MIT.
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Signals
- Stars
- 909
- Forks
- 146
- Open issues
- 0
- Last commit (this folder)
- Oct 11, 2026 (19 days ago)
- Last release
- Jul 29, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Website
- indranil.in
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Write a white-labeled report for one client from tracked data.
SkillAFreshness: A★ 909Claude Code +9 - 0.674score
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indranilbanerjee
Design the analytics layer with script-backed benchmarks: KPI trees, report templates, MMM, cookieless.
SkillAFreshness: A★ 909Claude Code +9 - 0.674score
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indranilbanerjee
Project ad budget pacing by script from live accounts or given numbers: month-end overspend.
SkillAFreshness: A★ 909Claude Code +9 - 0.674score
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indranilbanerjee
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SkillAFreshness: A★ 909Claude Code +9 - 0.674score
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Cite this page
reputation-management skill. Agent Mart. https://agentmart.dev/skills/indranilbanerjee-reputation-management (last updated 2026-10-24)