effort-map

Effort Map

Score how much real effort went into the current project by reading its agent sessions, then produce effort-DD-MM-YYYY-report.html.

Workflow

1. Detect the active agent: claude when running in Claude Code, codex when running in Codex.
2. Collect the sessions for the current project. From this skill directory run:

   python3 scripts/collect_sessions.py --project "$PWD" \
     --output /tmp/effort-map-sessions.jsonl --stats /tmp/effort-map-stats.json
   

   Add --agent claude or --agent codex only if automatic detection fails. The command prints token and time stats and writes the normalized transcript.
3. Treat the current folder as the project boundary. Stop with a clear message when no matching sessions exist.
4. Read /tmp/effort-map-sessions.jsonl in chunks. Collect every user prompt verbatim (trim noise, drop tool-result echoes and system reminders).
5. Score each metric from 0 to 100 from the evidence in the transcript, and write one honest sentence of WHY for each:
   • effort — reasoning, planning, and iteration versus one-shotting.
   • bugs — time and turns spent chasing and fixing bugs.
   • experience — tuning UX, polish, and details versus generate-and-done.
   • architecture — care about stack, structure, and design decisions.
   • copy_slop — copying other solutions with no changes or taste (high = more slop).
6. Merge the stats from /tmp/effort-map-stats.json (avg_tokens, total_tokens, avg_time_minutes, sessions_matched). Do not invent token or time numbers; use the ones the collector computed.
7. Write /tmp/effort-map-findings.json:

   {
     "agent": "Claude",
     "project": "project-name",
     "generated": "2026-07-06",
     "sessions_analyzed": 12,
     "prompts": ["first prompt", "second prompt"],
     "stats": { "avg_tokens": 45231, "total_tokens": 542772, "avg_time_minutes": 23.4 },
     "metrics": {
       "effort":       {"score": 72, "why": "..."},
       "bugs":         {"score": 40, "why": "..."},
       "experience":   {"score": 65, "why": "..."},
       "architecture": {"score": 80, "why": "..."},
       "copy_slop":    {"score": 15, "why": "..."}
     }
   }
   

8. Render the report. From this skill directory run:

   python3 scripts/render_report.py --input /tmp/effort-map-findings.json --output-dir "$PWD"
   

9. Return the absolute report path, the five scores, and the session count.

Scoring Rules

• Score from evidence only. Base each number on what the transcript shows, not on a guess about the user.
• Keep every WHY to one specific sentence that names what happened.
• Remove secrets, credentials, and personal data from prompts before writing them.
• Never modify the project while scoring. The only file written into the project is the HTML report.