Cocoops

---
name: ops-dora
description: CocoOps DORA metrics — computes four DORA-adapted delivery metrics from Snowflake task history and git log. Extends the longitudinal delivery thesis through the skill-native CocoOps thesis workflow. Invoked via $ops dora.
version: "1.1.0"
author: CocoPlus
tags:
  - cocoops
  - dora-metrics
  - delivery-intelligence
user-invocable: true
blocking: false
---

## Objective

Compute the four DORA-adapted CocoOps metrics and produce a delivery intelligence report.

Before proceeding, verify that `.cocoplus/` exists. If not, output: "CocoPlus is not initialized. Run `$pod init` first." Then stop.

## Step 1 — Check Demo Mode

Read `cocoplus.toml` if it exists and check `[demo] enabled`. If demo mode is active, read data from `.cocoplus/ops/demo/` instead of production sources.

## Step 2 — Run Deterministic Metrics Computation

Compute the metrics directly with Coco-native tools:

1. If demo mode is active, read `.cocoplus/ops/demo/` and use its task-history and incident fixtures.
2. Otherwise query Snowflake task history with `SnowflakeSqlExecute` and collect deployment/change evidence from `git log`.
3. Compute the four DORA-adapted indicators deterministically from the collected rows:
   - Pipeline Run Frequency: successful production pipeline runs per day.
   - Data Availability Lead: median time from committed change to successful scheduled availability.
   - Failure Recovery Time: median time from failing task/incidence record to the next successful run.
   - Data Quality Failure Rate: failed or rolled-back data quality runs divided by total runs.
4. Write `.cocoplus/ops/dora-snapshot.json` atomically with the raw counts, computed values, benchmark tiers, source mode, and `computed_at`.

If Snowflake is unavailable and no demo data exists, output: "CocoOps: unable to compute metrics — [reason]. Run `$ops demo` to activate demo mode for evaluation." Then stop.

## Step 3 — Read Snapshot

Read `.cocoplus/ops/dora-snapshot.json` for the computed metrics.

Verify the snapshot includes `benchmarks` with the DORA-adapted tier thresholds used to classify each metric. If absent, add the threshold context to the report narrative from the skill definition before displaying tiers, so "Elite/High/Medium/Low" is never presented without benchmark meaning.

## Step 4 — Spawn Haiku Narrative Synthesis

Pass the pre-computed metrics snapshot to a Haiku sub-agent with this mandate:

"Read the DORA metrics snapshot and produce a narrative report. Every statement MUST cite specific pipeline names, dates, or quantities from the data — no vague generalities. The report must:
1. State the overall health tier (Elite/High/Medium/Low)
2. Highlight notable signals with specific named pipelines and dates
3. Identify the top contributor to each below-tier metric
4. Suggest 2-3 prioritized actions based on the data

Do not produce: 'your pipelines may be slow' — instead produce: 'pipeline X has p95 time of Y hours over last Z days'."

## Step 5 — Write and Commit

Write insights to `.cocoplus/ops/dora-insights-<YYYY-MM-DD>.md`. Commit both `dora-snapshot.json` and the insights file only if either file has changed since the last commit (use `git diff --quiet` to check before committing). If neither file changed, skip the commit and note: "Snapshot unchanged since last run — no commit needed."

## Step 5b — Extend Longitudinal Thesis

After committing the snapshot, read `.cocoplus/ops/dora-thesis.md` if present and append a new `### Evidence — <YYYY-MM-DD>` block derived from `dora-snapshot.json`. Never replace prior thesis content. If thesis extension cannot be completed, log a warning to `.cocoplus/hook-errors.log` and continue (non-fatal).

Commit `dora-thesis.md` if it changed: `docs(ops): extend longitudinal delivery thesis — [date]`

## Optional Export

If the developer asks for a stakeholder export, use the `reporting/report-export` skill contract to export `.cocoplus/ops/dora-insights-<YYYY-MM-DD>.md` to the requested format under `.cocoplus/ops/exports`.

PDF requests report renderer availability; do not block DORA computation on PDF rendering.

## Step 6 — Display Report

```
CocoOps DORA Report — <project> — <date>

Pipeline Run Frequency:    <value> / day   [<tier> tier]
Data Availability Lead:    <value> hours   [<tier> tier]
Failure Recovery Time:     <value> min     [<tier> tier]
Data Quality Failure Rate: <value>%        [<tier> tier]

Overall health: <ELITE | HIGH | MEDIUM | LOW>
Benchmark context: DORA-adapted CocoOps thresholds included in dora-snapshot.json

Notable signals:
• <specific pipeline name>: <specific finding with numbers>
• <specific signal with date reference>
```

## Anti-Rationalization Table

| Shortcut / Temptation | Why It Fails |
|-----------------------|--------------|
| Let LLM compute the metrics | Metrics must be deterministic — same inputs always produce same numbers |
| Omit pipeline names from narrative | Vague generalities are useless — citations are a constraint, not a preference |
| Skip committing dora-snapshot.json | Snapshot is a team artifact — it must be in git for team members to see it |
| Replace dora-thesis.md content | Thesis is longitudinal — replacing destroys delivery history; only append dated evidence blocks |
| Block report display on thesis update | Thesis update is async and non-blocking — display proceeds immediately after Step 5 |

## Exit Criteria

- Deterministic metrics computation completes before any LLM work
- Haiku narrative cites specific pipeline names, dates, quantities
- `dora-snapshot.json` committed to git
- Insights file written to date-stamped path
- `dora-thesis.md` is appended and committed if changed