Manager Review

• Exit code 2: Stop. Guide user to setup.
• Exit code 0 or 1: Proceed.

Check 2: Team Member's Notion Page ID

Ask the manager for the team member's Notion page URL or ID. Extract and store if needed.

Check 3: Data Source Availability

Scan and report:

Data sources for {name}'s review:
  [x] Notion — {N} databases found
  [x] BEO progress data — {N} entries
  [ ] Fathom meetings — not configured
  [x] Chat logs — {N} days

Workflow

Step 0: Parse Command

Extract team member name from trigger (e.g., manager review - Sarah).

Default period: Current quarter (Q1: Jan-Mar, Q2: Apr-Jun, Q3: Jul-Sep, Q4: Oct-Dec).

Step 1: Fetch All Data from Notion

python3 03-skills/okr-self-review/scripts/fetch_okrs.py \
  --page-id <NOTION_PAGE_ID> \
  --include-done \
  --json

Returns JSON with: functional_okrs, ai_native_okrs, key_projects, functional_skills, core_behaviours. Each entry includes page_id, text, status, self_rating, checkin_6months, checkin_3months, quarter, year.

If zero data returned: Notify manager and proceed with available evidence sources only. Flag low confidence in output.

Step 2: Gather Evidence

From all available sources in parallel:

• BEO data (if BEO_SHEET_ID in .env):

  python3 03-skills/okr-self-review/scripts/fetch_beo_data.py \
    --name "{name}" \
    --start-date {quarter_start} \
    --end-date {quarter_end} \
    --json
  

• Fathom (if FATHOM_API_KEY in .env): Fetch meetings via API
• Chat logs: Read 01-memory/chat/*.md within review period
• Local context: 04-workspace/ artifacts

Step 3: Analyze & Synthesize

Cross-reference all data sources. Identify:

• Progress & achievements — what shipped, measurable outcomes, completed OKRs
• Gaps & risks — stalled OKRs, missing updates, dropped projects
• Update freshness — when was each OKR last updated? Flag stale items
• Self vs evidence misalignment — self-ratings that don't match observed output
• Behavioural signals — core behaviours demonstrated (or absent) in evidence

Step 4: Generate Briefing

Produce the output using the template below.

Analysis Guidelines

Evidence Weighting

Detecting Misalignment

Question Design Rules

Every question must be:

• Specific — reference actual OKR text, project name, or data point
• Evidence-backed — cite the source (BEO, Fathom, Notion, Chat)
• Outcome-oriented — ask about results, impact, decisions — not feelings
• Actionable — the answer should clarify status, unblock, or align

Cap at 5 questions total — 3 Critical, 2 Important. No Optional tier. Force-rank by impact.

Never generate generic questions. If there's not enough data to make a specific question, flag the data gap instead.

Output Template

# Manager Review Briefing
## {name} — {quarter} {year}

**Review Period**: {start_date} to {end_date}
**Data Sources**: {list with entry counts}
**Data Confidence**: {High | Medium | Low — based on source availability}

---

## 1. Manager Brief (TL;DR)

**Overall Signal**: {On Track | At Risk | Exceeding | Unclear}

- **Biggest Win**: {specific achievement with evidence source}
- **Biggest Concern**: {specific risk or gap with evidence source}
- **Visibility Gaps**: {areas where data is missing or stale}

---

## 2. Key Focus Areas (Top 3)

The most important topics to prioritize in this conversation:

1. **{Topic}** — {why this matters, what data shows}
2. **{Topic}** — {why this matters, what data shows}
3. **{Topic}** — {why this matters, what data shows}

---

## 3. Risks & Gaps

- {risk/gap} — {evidence} [{source tag}]
- {risk/gap} — {evidence} [{source tag}]
- {risk/gap} — {evidence} [{source tag}]

---

## 4. Where to Appreciate

- {specific achievement or behaviour} — {evidence} [{source tag}]
- {specific achievement or behaviour} — {evidence} [{source tag}]

---

## 5. Questions (Max 5)

Generate exactly 5 questions. Prioritize by impact.

### 🔴 Critical (Must Ask)

1. "{specific question referencing data}"
   *Evidence*: {what triggered this question} [{source}]
   *Tone*: {direct | curious | supportive}

2. "{specific question}"
   *Evidence*: {data point} [{source}]
   *Tone*: {recommended tone}

3. "{specific question}"
   *Evidence*: {data point} [{source}]
   *Tone*: {tone}

### 🟡 Important (If Time Permits)

4. "{question}"
   *Evidence*: {data point} [{source}]
   *Tone*: {tone}

5. "{question}"
   *Evidence*: {data point} [{source}]
   *Tone*: {tone}

---

## 6. Self vs Evidence Insights

| Area | Self-Rating | Evidence Signal | Gap |
|------|------------|-----------------|-----|
| {OKR/Skill/Behaviour} | {N}/5 | {what evidence shows} | {Over / Under / Aligned} |

**Notable patterns**:
- {pattern — e.g., "Consistently rates execution high but key projects lack completion evidence"}

---

## 7. Suggested Manager Actions

Post-conversation next steps:

- [ ] {action — e.g., "Align on realistic Q2 target for OKR X"}
- [ ] {action — e.g., "Request weekly async update on stalled Project Y"}
- [ ] {action — e.g., "Acknowledge strong delivery on Z in team standup"}

---

## 8. Data Confidence Notes

| Source | Status | Note |
|--------|--------|------|
| Notion OKRs | {Available / Partial / Missing} | {detail} |
| BEO Updates | {Available / Partial / Missing} | {detail} |
| Fathom | {Available / Partial / Missing} | {detail} |
| Chat Logs | {Available / Partial / Missing} | {detail} |

**Assumptions made**: {list any inferences made due to missing data}

Style Guidelines

• Concise, structured, skimmable — bullet points over paragraphs
• Objective and evidence-based — no assumptions without signals
• Balance constructive feedback with recognition
• Avoid harsh or interrogative phrasing — this helps managers have good conversations
• Use source tags: [Notion], [BEO], [Fathom], [Chat], [Workspace]

Edge Cases

Integration

Version: 1.0 | Updated: 2026-03-18