| name | analyze-ticket-effort |
| description | Use when measuring how much analyst time is spent populating/maintaining Jira tickets, or baselining ticket volume per client/type/month. Reconstructs work sessions from ticket event timestamps (creation, field edits, comments) — works with empty worklogs. |
| argument-hint | [--window 180d|365d] [--client <label>] [--sample <n>] |
Analyze Ticket Effort
Measure per-ticket analyst effort and per-client ticket volume from Jira event timestamps, using the session-reconstruction method. Executes Phases 0–2 of the effort-analysis methodology.
The Iron Law
NO EFFORT REPORT WITHOUT STATED BIASES FIRST
Burst time is a lower bound, not a measurement of total effort. Every deliverable states the known biases verbatim (undercounts, overcounts, window sensitivity) and pairs raw burst figures with with-overhead figures. An effort number without its biases is how a lower bound becomes a budget.
Announce at Start
Before any other action, say verbatim:
I am using the analyze-ticket-effort skill to measure analyst effort and ticket volume from Jira event history.
Red Flags
| Thought | Reality |
|---|
| "Worklogs are empty, so effort can't be measured" | That's the point of this skill — sessions are reconstructed from event timestamps. |
| "Burst time IS the effort" | Burst time is a lower bound. Always add per-session overhead and say so. |
| "A 30-day sample is enough" | Short windows run light vs 12-month numbers. Use ≥6–12 months for rate-setting. |
| "I'll query the client custom field by its display name" | Object custom fields only match by label via cf[NNNNN] = "Label". Re-run schema discovery if unsure. |
| "The attributed totals are close enough" | Always cross-check the sum of per-client counts against the unfiltered project total. Gaps hide unattributed tickets. |
| "The monthly slope shows a clear decline" | Check R² first (<0.3 = ranging), exclude ramp months, and look for single-client artifacts before declaring a trend. |
| "I'll fetch all 500 tickets in the foreground" | 3 calls/ticket — background the fetch loop with resumable [ -s ] guards. |
| "Client names and volumes can go in the PR/commit" | Sensitive. Results go to local artifacts/ only. |
Prerequisites
jr CLI installed and authenticated (jr auth login)- Python 3 available on PATH
- Optional but recommended:
artifacts/metrics/effort-priors.yaml (copy from ${CLAUDE_PLUGIN_ROOT}/templates/effort-priors-tmpl.yaml) with the project key, client field ID, and exclusion list already discovered
Workflow
Delegate the heavy lifting to the metrics-analyst agent (Quinn) when running the full analysis; run inline only for a quick single-client or small-sample question.
Stage 1: Schema discovery (Phase 0)
- Verify jr:
jr --version && jr me
- If the priors file exists, load
project_key, client_field, exclusions from it. Otherwise run schema discovery per the method doc (dump custom fields from one known ticket; enumerate clients via assets search; inspect records; build exclusion list) and offer to save the results into a new priors file.
- Survey field quality: check worklogs are actually empty, sample the Priority field distribution.
Stage 2: Volume baseline (Phase 1)
- One JQL query per client label over the window (default 180d).
- Produce: per-client totals, type mix, monthly counts.
- Cross-check attributed sum vs unfiltered project total — report the gap.
- Trend stats per client and desk-wide: mean, stdev, slope, R² — apply the ranging rule.
Stage 3: Effort measurement (Phase 2)
- Pick the sample:
--client if given, else top client(s) by volume; --window ≥180d for rate-setting.
- Background-fetch view/changelog/comments per ticket (resumable loop).
- Run session reconstruction (GAP=30min, +5min/session overhead — from the priors file if customized).
- Report per type: touch avg/median, sessions avg, words, with-overhead totals.
- Compare against
effort_priors in the priors file if present; flag drifts >25%.
- Offer to update the priors file with the new measurements.
Stage 4: Deliverable
Write artifacts/metrics/effort-analysis-<date>.md containing: summary tables, method notes, the known-biases section (mandatory, verbatim discipline), coverage/exclusions, and priors-drift comparison. Present the summary inline.
References
${CLAUDE_PLUGIN_ROOT}/data/effort-analysis-method.md — canonical method (Phases 0–2)${CLAUDE_PLUGIN_ROOT}/templates/effort-priors-tmpl.yaml — local priors file template/model-ticket-cost — turns these measurements into annual cost scenarios/extract-severity — severity/criticality cross-tab from the same fetched comments
