| name | brief-glassdoor-signal |
| description | Produce a Glassdoor / employee-sentiment research brief — overall ratings, sub-ratings, tenure data with red-flag detection scoped to the relevant team, compensation data, theme analysis of top complaints and positives, recent direction inflections. Aggregate sentiment, not a recap of individual reviews. Use to gauge whether public employee discourse aligns or contradicts the company's outward image. Reads templates/research-briefs/glassdoor-signal.md and writes to <WORKING_FOLDER>/research/companies/<slug>/glassdoor-signal.md. |
| disable-model-invocation | false |
| allowed-tools | Read, Write, WebSearch, WebFetch, Bash(mkdir *), Bash(test *) |
Brief — Glassdoor Signal
Aggregate sentiment, not a list of individual reviews. The point is patterns and inflections.
Inputs
$ARGUMENTS:
- Required:
<company_name>.
- Optional:
--slug=<slug>.
- Optional:
--team=<engineering|sales|product|...> — scope tenure red flags + theme analysis to a team.
Procedure
1. Resolve config + paths
${CAREER_DATA_DIR}/config.json → WORKING_FOLDER. Output: ${WORKING_FOLDER}/research/companies/${slug}/glassdoor-signal.md.
2. Read template
${PLUGIN_ROOT}/templates/research-briefs/glassdoor-signal.md.
3. Gather
- Glassdoor company page (rating, sub-ratings, review count, CEO approval, recommend-to-friend).
- Glassdoor reviews list (latest 30 + historical sample).
- Levels.fyi / Blind / TeamBlind / Comparably for compensation cross-checks.
4. Compute headline numbers
From the company page directly. Trend: compare current rating to last 12 months if Wayback snapshots available.
5. Tenure red-flag check
- Compute median tenure overall.
- If
--team provided, scope to that team's reviewers (use job-title field).
- Only flag sub-1-year medians scoped to a relevant team or to the whole company at <50 headcount. Sub-1-year medians at company-wide scale on a 5000-person org are noise.
- Quote 1–2 representative tenure-red-flag reviews if you flag.
6. Theme analysis
For the latest 30 reviews:
- Cluster negative reviews into 3–5 themes by frequency.
- Cluster positive reviews into 3–5 themes by frequency.
- For each theme, quote 1–2 representative reviewers verbatim with role + date.
- Look for patterns. Single-instance complaints ≠ themes.
7. Inflection detection
If reviews show a clear before/after — leadership change, layoffs, acquisition — note it with one quote on each side.
8. Compensation data
Pull stratified data (role × geography) from Levels / Glassdoor / Blind. Always cite source. Self-reports get "(self-reported)" tag.
9. Write output + update CRM
Append +glassdoor to the row.
Companion delegation
If social-feedback companion is installed, cross-check on Reddit / HN / Blind — social-feedback:check-discourse <company> employee sentiment. Use to validate or qualify Glassdoor themes.
Guardrails
- Don't over-index on outliers. Single 1-star review with personal grievance is not a theme.
- Tenure red flags are scoped. See decision rule above.
- No invented quotes. Every blockquote is verbatim from a real review with date.
- Self-reported comp data is tagged.
- Acknowledge low review volume (<20) explicitly — high-variance.
Failure modes
- Glassdoor page missing or fetch blocked → fall back to Indeed reviews + Comparably; mark with
(non-Glassdoor source).
- Review volume too low (<10) → write
## Status: low-signal and avoid theme analysis.
Idempotency
- Re-run prompts before overwrite.
--refresh silently overwrites.
