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Path _dash/engagements.md
URL /engagements/
Date 2026-07-24
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Engagements

Every registry project as a client — deterministic estimates with an AI/broker/platform cost decomposition, evidence-accrued actuals, and estimate-vs-actual variance per engagement.

Table of Contents

💼 Engagements — estimates vs actuals

Every registry project is a client; every entry is a statement of work produced by tools/dash estimate (deterministic estimator-v1 over open issues, priced by the rate card) and settled by tools/dash ledger (actuals accrued from auditable evidence — claude-code-action CI runs, Claude-attributed commits and PRs — into _data/engagements.yml). Each estimate decomposes along the AI-era division of labor: AI performs the implementation, the human broker builds context, defines goals, and validates, the platform runs the pipelines. The traditional column is what the same scope would have cost at pre-AI consulting hours — its ratio to the estimate is the engagement’s leverage. Model and lifecycle: docs/ESTIMATION.md.

$1285

Pipeline (estimated + approved)

$0

In flight

$0

Delivered (est.)

$0.0

Actuals to date

12

Engagements · 7 clients

12.7×

Avg AI leverage (est.)

Status counts: estimated 12 approved 0 in progress 0 delivered 0 reconciled 0 cancelled 0 — estimates are proposals: nothing accrues actuals until a human approves (tools/dash ledger --set-status ENG-NNNN=approved).

Clients

ClientEngagementsOpenDeliveredEstimatedActuals
law-ai 1 1 0 $342.74
lifehacker.dev 2 2 0 $191.9
ai-seed 2 2 0 $190.66
barodybroject 2 2 0 $190.66
zer0-mistakes 2 2 0 $190.66
githubai 2 2 0 $137.1
it-journey 1 1 0 $41.77

Engagements

Est. = AI implementation + human brokerage + platform + confidence contingency. Lev. = traditional-consulting quote ÷ estimate. Every actual links to its evidence (run, commit, PR) inside _data/engagements.yml.

IDClientWork itemTypeTier StatusEst.Actual VarianceLev.
ENG-0001 lifehacker.dev Work order: link-rot — broken #comments anchor on /news/field-notes... bug s estimated $95.95 12.5×
ENG-0002 zer0-mistakes a11y: dialog titles use a bare <h5 class="modal-title"> — heading-o... bug s estimated $95.33 12.6×
ENG-0003 barodybroject Deployment failure: a1edc96 bug s estimated $95.33 12.6×
ENG-0004 githubai Bug [BUG] test bug s estimated $95.33 12.6×
ENG-0005 ai-seed [CI Failure] ci-cd-pipeline on refs/pull/17/merge @ 8a9d774 ci s estimated $95.33 12.6×
ENG-0006 it-journey 🚨 Dependency Security Issues (automated) deps xs estimated $41.77 14.4×
ENG-0007 law-ai evolve: run /evolve on top BACKLOG item unknown m estimated $342.74 10.5×
ENG-0008 lifehacker.dev [sev2] content-bug: missing-preview (/docs/ai-usage/) bug s estimated $95.95 12.5×
ENG-0009 zer0-mistakes fix: <html class="no-js"> is never upgraded to "js" — dead progress... bug s estimated $95.33 12.6×
ENG-0010 barodybroject Deployment failure: 40cbff9 bug s estimated $95.33 12.6×
ENG-0011 githubai [Auto] Repository Health Check chore xs estimated $41.77 14.4×
ENG-0012 ai-seed [CI Failure] ci-cd-pipeline on refs/pull/17/merge @ 99f1a5d ci s estimated $95.33 12.6×

How an engagement flows

  1. Estimatetools/dash estimate classifies an open issue (labels, title, per-repo cost history) into a tier and prices it from the rate card: AI tokens at API list rates, broker hours at the card rate, CI minutes at runner rates, plus a confidence-based contingency. Same inputs, same estimate — the quote is reproducible, not negotiable.
  2. Approve — the broker reviews the plan (approach, deliverables, acceptance), adjusts, and signs: --set-status ENG-NNNN=approved. Only then does the meter start.
  3. Execute — the AI implements (sessions, CI runs, PRs). The daily ai-usage refresh harvests the evidence; tools/dash ledger accrues it to the engagement, deduped by URL.
  4. Deliver & reconcile — on delivered, variance closes the loop: estimate vs actual, band (±10% = on), and the actual leverage against the traditional quote.

Where the numbers come from

Updated 2026-07-23 02:36 UTC · source .github/scripts/dash-gen/engagements.py · register _data/engagements.yml · rate card _data/engagement_rates.yml