| name | whatidid |
| description | Generate a daily activity report from Claude Code session data. Harvests human/assistant turns from ~/.claude/projects/*/*.jsonl, analyzes them with claude-haiku-4-5 via the Anthropic API, and renders an HTML report with KPI cards, goals table, and effort/leverage breakdown.
|
| argument-hint | [YYYY-MM-DD] |
What Did I Do — Daily Activity Report
Overview
Produces a structured "what did I do today" report by reading Claude Code session
JSONL files, extracting user instructions and tool summaries, calling the Anthropic
API for goal/task analysis, and rendering an HTML report with KPI cards, goals table,
and token cost breakdown.
The report answers: what goals were pursued, what tasks were completed, how much time
was spent, and what the ROI was relative to the seat cost.
When to Use
- At the end of a workday to generate a standup/status report
- When you need an objective log of what was worked on for a given date
- When asked to "report on today", "what did I do", or given a date argument
Not for: Real-time session introspection, cross-repo analysis beyond git metadata,
or project planning.
Helpers & References
| File | Purpose |
|---|
helpers/whatidid.rs | Entrypoint — orchestrates harvest → analyze → report |
helpers/harvest.rs | Scans ~/.claude/projects/*/*.jsonl for today's sessions |
helpers/analyze.rs | Calls Anthropic API; caches result to cache/YYYY-MM-DD.json |
helpers/report.rs | Renders HTML with KPI cards and goals table; opens in browser |
references/architecture.whatidid.md | Data flow, session format, token cost model |
references/effort-estimation.whatidid.md | Effort estimation heuristics |
Steps
1. Resolve the target date
Use $ARGUMENTS if provided (format: YYYY-MM-DD). Otherwise default to today.
2. Harvest session data
Run helpers/harvest.rs [YYYY-MM-DD] via rust-script.
Scans all ~/.claude/projects/<project-slug>/*.jsonl files. Each JSONL line has a
timestamp field (ISO 8601). Events with a matching date prefix are included.
Key event types extracted:
type: "human" → message.content[].text (user instructions; noise-filtered)type: "assistant" → message.content[] text blocks + tool_use names- Metadata from every line:
cwd, gitBranch, sessionId, slug
Noise filtered from human turns: single-word confirmations ("ok", "yes", "act"),
<system-reminder> injections, <current_datetime> tags.
Output: JSON array of session records with messages[], tool_calls, read_calls,
edit_calls, bash_calls.
3. Analyze with Anthropic API
Run helpers/analyze.rs <sessions.json> [YYYY-MM-DD] via rust-script.
Requires ANTHROPIC_API_KEY in environment. Inject via:
op run --env-file=$HOME/dev/.env -- rust-script helpers/whatidid.rs
Builds a transcript from harvested sessions (cwd, branch, SIGNALS block with tool
counts, then interleaved human/assistant turns). Sends to claude-haiku-4-5-20251001.
Model prompt lives in prompts/analysis.txt. Returns structured JSON digest:
goals[] each with tasks[], human_hours, domain_skills, tech_skills.
Caches result to cache/YYYY-MM-DD.json — re-runs skip the API call.
4. Compute KPIs
total_human_hours = sum(goal.human_hours for goal in goals)
human_value = total_human_hours × 72 # $72/hr blended rate
seat_cost_per_mo = 39 # Claude Code Pro seat
leverage = human_value / seat_cost_per_mo
Token cost model: prefix-matched against report.rs → MODEL_PRICING.
Fallback: $3.00 input / $15.00 output per 1M tokens.
5. Generate HTML report
Run helpers/report.rs <digest.json> [YYYY-MM-DD] via rust-script.
Layout:
Header bar — date, primary_focus, session count
Narrative — day_narrative (2 sentences from digest)
KPI cards — Hours | Human value | Leverage
Goals table — Goal | Tasks (bullet list) | Hours | Skills
Written to /tmp/whatidid-YYYY-MM-DD.html. Opened automatically via open.
6. Report to user
[1/3] harvesting sessions for 2026-05-17...
[2/3] analyzing with Claude...
[3/3] rendering report...
Date: 2026-05-17 | Goals: 5 | Hours: 6.5h
Human value: $468 | Leverage: 12x
Report: /tmp/whatidid-2026-05-17.html
Token Cost Model
| Provider | Model prefix | Input $/1M | Output $/1M |
|---|
| Anthropic | claude-opus-4 | $15.00 | $75.00 |
| Anthropic | claude-sonnet-4 | $3.00 | $15.00 |
| Anthropic | claude-haiku | $0.80 | $4.00 |
| OpenAI | gpt-5, gpt-4o | $2.50 | $10.00 |
| OpenAI | gpt-4.1 | $2.00 | $8.00 |
| OpenAI | o3 | $10.00 | $40.00 |
| Google | gemini-2.5-pro | $1.25 | $10.00 |
| Google | gemini-2.5-flash | $0.15 | $0.60 |
Fallback (unknown model): $3.00 input / $15.00 output.
Update report.py → _MODEL_PRICING when rates change.
Common Mistakes
- Including injected context as user instructions — filter
<current_datetime>,
<system-reminder>, and single-word approval messages ("ok", "yes", "go") before
building the transcript.
- Double-counting sessions — a single working session may span midnight; use
created_at (session start) not updated_at as the date key.
- Calling the API without a cached result check — always check
cache/YYYY-MM-DD.json before hitting GitHub Models; the API has rate limits.
- Hardcoding model names — use prefix matching in
_MODEL_PRICING so new model
variants don't silently fall through to zero cost.
