/digital-marketing-pro:engagement-workflow — 12-Part Engagement Orchestrator
This skill orchestrates the full marketing engagement using the 12-Part sequential methodology. Every brand engagement runs through the same 12 parts in sequence, producing a canonical set of files at each stage.
Context efficiency
Heavy skill. Grep before Read any referenced file, then Read only matched ranges with offset + limit. List the brand's workspace at ~/.claude-marketing/brands/{slug}/ (or $CLAUDE_PLUGIN_DATA/digital-marketing-pro/brands/{slug}/ when that env var is set) before opening files. On re-invocation mid-session, skip files already in context.
Read these references before producing output:
Operating Mode
This skill is invoked via the /digital-marketing-pro:engagement command family. The command is a thin router — this skill is the single source of truth for the engagement lifecycle, the checkpoint protocol, and the per-part production contract. Each subcommand maps to a specific lifecycle action. The skill calls engagement-state.py for persistence via:
python "${CLAUDE_PLUGIN_ROOT}/scripts/engagement-state.py" <subcommand> ...
You should never hand-edit _engagement.json — always go through engagement-state.py.
Checkpointing & Resume (single source of truth)
Every long engagement run is resumable. The checkpoint protocol is: init a run → save each part as it completes → finalize → publish to the visible output folder. This lets an interrupted run (context exhaustion, user cancel, machine sleep) resume from the next un-checkpointed part instead of restarting from Part 1.
1. On start, after the brand pre-condition passes, open a checkpoint run and link it to engagement state:
python "${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py" init \
--brand "{brand_slug}" --workflow engagement --topic "{engagement_id}"
python "${CLAUDE_PLUGIN_ROOT}/scripts/engagement-state.py" set-checkpoint-run \
--brand "{brand_slug}" --id "{engagement_id}" --run-id "{run_id}"
set-checkpoint-run stores the run_id in _engagement.json, making the resume linkage real (previously the run_id was never persisted).
2. After each part completes and passes its quality gate, the orchestrator saves that part's output:
python "${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py" save \
--brand "{brand}" --run-id "{run_id}" \
--step {part_number} --content-file "{path_to_that_part_deliverable}" --extension md
Pass the actual deliverable path for that part (e.g. Part 3 saves the Four Core Documents path; Part 8 saves the Growth Plan path) — never a placeholder for a different part.
3. Before saving Part 5 (Client Validation) and Part 8 (Growth Plan) deliverables, run the full quality gate:
/digital-marketing-pro:check "{path_to_deliverable}" --full --brand {brand}
If /digital-marketing-pro:check --full returns BLOCKED, fix the CRITICAL issues before checkpointing the part.
4. After the final part, publish every artifact to the user-visible folder and finalize:
python "${CLAUDE_PLUGIN_ROOT}/scripts/output-publisher.py" publish-run \
--brand "{brand}" --run-id "{run_id}"
python "${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py" finalize \
--brand "{brand}" --run-id "{run_id}" --status completed
Then point the user at the visible output folder via /digital-marketing-pro:output-folder {brand}.
To resume an interrupted run, use /digital-marketing-pro:resume — it reloads every saved part and continues from the next un-checkpointed part.
State validation & rework caps
-
Validate a part's outputs against the manifest before marking it complete:
python "${CLAUDE_PLUGIN_ROOT}/scripts/engagement-state.py" validate-part \
--brand "{brand}" --id "{id}" --part {N}
This diffs the actual files on disk against the PART_DEFINITIONS manifest and flags missing deliverables. Use it in file-tree and before next.
-
Repair a partially-initialised engagement directory (instead of crashing on a non-empty dir):
python "${CLAUDE_PLUGIN_ROOT}/scripts/engagement-state.py" init --repair \
--brand "{brand}" --id "{id}"
--repair completes the canonical directory tree and state file on a dir that holds only partial state.
-
v2 re-run cap: a maximum of 2 v2 re-run rounds per part is allowed without explicit user override. The round count is stored in _engagement.json. If a part would exceed 2 rounds, stop and ask the user to explicitly approve further re-runs (records the override in state). This prevents unbounded re-run loops.
Subcommands
/digital-marketing-pro:engagement start <brand-slug> <engagement-id>
Purpose: Initialise a new engagement.
Steps:
- Validate that the brand profile exists at
~/.claude-marketing/brands/{brand-slug}/profile.json. If not, instruct the user to run /digital-marketing-pro:brand-setup first.
- Run
python ${CLAUDE_PLUGIN_ROOT}/scripts/engagement-state.py init --brand {brand-slug} --id {engagement-id}.
- Confirm the directory tree was created and report the next required action (Part 1 intake).
- Walk the user through Part 1 Stone vs Opinion intake by asking the questions one batch at a time.
Part 1 intake questions (ask in this order):
Stone — what the client knows for certain:
- Company basics: founded year, employee count, headquarters location, geographic operations
- Business model: revenue streams, pricing tiers, primary product/service categories
- Current marketing: channels currently active, monthly marketing spend, current measurable KPIs
- Tech stack: CRM, email service provider, analytics setup, ad accounts
- Customer base scale: customer count, biggest named customer, average order value if known
For each Stone fact, capture:
- The fact itself
- Source (how the user knows / what document confirmed it)
Save each via:
python ${CLAUDE_PLUGIN_ROOT}/scripts/engagement-state.py add-stone-fact --brand {slug} --id {id} --fact-json '{"category":"...","fact":"...","source":"..."}'
Opinion — what the client believes:
- Brand positioning: how does the client describe their position in the market?
- Customer base: who do they think their customers are? Why do they buy?
- Competitors: who do they consider their main competitors?
- Growth opportunities: where do they think the biggest opportunity is?
- What is working: what marketing activity does the client believe is working?
- What is not working: what does the client believe is not working?
For each Opinion, capture:
- The hypothesis
- Client's evidence for it (could be intuition, anecdote, partial data)
- Research question — what would the unbiased research need to verify or refute?
Save each via:
python ${CLAUDE_PLUGIN_ROOT}/scripts/engagement-state.py add-opinion --brand {slug} --id {id} --hypothesis-json '{"category":"...","hypothesis":"...","client_evidence":"...","research_question":"..."}'
On completion of Part 1: mark Part 1 as completed via mark-part-completed --part 1, advise the user to proceed to Part 2 (External Research).
/digital-marketing-pro:engagement next [brand] [id]
Purpose: Advance to the next part.
Steps:
- Read engagement status via
engagement-state.py status
- Identify the current part and next not-yet-completed part
- Confirm with the user that the current part is genuinely complete (do not auto-advance — ask)
- On confirmation, mark current as completed, advance current_part pointer
- Brief the user on what the new part requires
/digital-marketing-pro:engagement status [brand] [id]
Purpose: Show engagement status.
Steps:
- Run
engagement-state.py status — get the full state
- Read the Living Project Instruction File header
- Format a human-readable summary:
- Engagement: brand + id + start date
- Current part: part name + days in
- Completed parts: list
- Pending parts: list
- Open re-run decisions: count
- LIF last updated: date
- If the engagement has open items needing resolution, list them
/digital-marketing-pro:engagement file-tree [brand] [id]
Purpose: Show the engagement directory file tree.
Steps:
- Run
engagement-state.py file-tree
- Format as an indented tree
- Highlight files that are missing per the canonical structure. Use
engagement-state.py validate-part --part {N} to diff each completed part's actual files against the PART_DEFINITIONS manifest (e.g., if Part 3 is marked completed but 3.1-business-and-sbu-analysis.md is missing, validate-part flags it deterministically instead of eyeballing).
/digital-marketing-pro:engagement validate [brand] [id]
Purpose: Run the Part 5 Client Validation flow.
Pre-condition: Parts 2, 3, 4 must be completed.
Steps:
- Verify pre-conditions (Parts 2, 3, 4 completed)
- Invoke the
client-validation-document skill — it produces the Part 5 deliverable: a structured document presenting each finding from v1 with ACCEPT/REJECT/EDIT/DEFER options
- Run the full quality gate on the Part 5 deliverable before it goes to the client:
/digital-marketing-pro:check "{part5_path}" --full --brand {brand}. If it returns BLOCKED, fix the CRITICAL issues first (this gate is mandatory before Part 5 and Part 8 deliverables).
- After the user reviews and provides decisions, parse them into a triggers list per the Decision Matrix categories
- Run
engagement-state.py decision-matrix --triggers "{comma-separated}" to compute the v2 re-run plan
- Present the re-run plan to the user
- Mark Part 5 completed; on user approval of the re-run plan, advance to Part 6
/digital-marketing-pro:engagement re-run-decision [brand] [id]
Purpose: Apply the Decision Matrix to compute v2 re-runs.
Steps:
- Read the Part 5 Client Validation Document
- Categorise rejected/edited findings into Decision Matrix triggers
- Show the triggers and the computed re-runs
- Estimate the cost (rough token count) of each re-run
- Await user approval — they can accept, modify (skip some, add others), or reject
- Record the executed plan via
engagement-state.py record-rerun-execution
/digital-marketing-pro:engagement update-back [brand] [id] --doc <doc-id> --reason <reason>
Purpose: Apply the Update-Back Rule to bump a source document version after Part 7+.
Pre-condition: The user has already drafted the corrected document content.
Steps:
- Read the current version of the doc
- Confirm the correction with the user (validation step per the Update-Back Rule)
- Bump the version via
engagement-state.py bump-version --doc {id} --reason "{reason}"
- Save the new version file with a header noting v(prev) → v(new) changes
- Update the Living Project Instruction File via
lif-log-change — it now appends the change to living-instruction-file.md and refreshes the header date, so the LIF reflects the correction immediately
- Identify downstream documents that may need review and add to the engagement's review queue
/digital-marketing-pro:engagement lif-show [brand] [id]
Purpose: Display the Living Project Instruction File.
Steps: Run engagement-state.py lif-show and format the markdown output for readability.
/digital-marketing-pro:engagement list-engagements [brand]
Purpose: List all engagements (optionally filtered by brand).
Steps: Run engagement-state.py list-engagements --brand {slug} and format as a table.
Production shorthands
The command family also exposes four production shorthands that route straight to the part-producing skills (documented in Per-Part Production Targets below). These match the command surface one-to-one:
/digital-marketing-pro:engagement four-core <brand> <id> [--doc 3.X] [--view v2] [--combined] — Part 3, invokes the four-core-documents skill/digital-marketing-pro:engagement growth-plan <brand> <id> — Part 8, invokes the growth-plan skill/digital-marketing-pro:engagement yearly-planner <brand> <id> — Part 8 companion, invokes the yearly-planner skill/digital-marketing-pro:engagement loop <brand> <id> — Part 12, invokes the continuous-improvement-loop skill
Per-Part Production Targets
Each part is produced by real, existing agents and skills. This orchestrator dispatches to the targets below — there are no wrapper skills named external-research / preparation-documents / channel-strategy-fanout / execution-artefacts / ai-creative-instructions; those never existed. Use the exact targets named here:
| Part | Real target(s) |
|---|
| 1 | (this skill — intake walked here directly) |
| 2 | agents market-intelligence + competitive-intel; skill audience-intelligence (invoke as a skill); reference compliance-rules.md (load as context) |
| 3 | skill four-core-documents (produces 3.1, 3.2, 3.3, 3.4) |
| 4 | command competitor-analysis + skills audience-intelligence + agent market-intelligence |
| 5 | skill client-validation-document |
| 6 | re-runs invoke skill four-core-documents with --view v2 |
| 7 | skills content-engine + campaign-orchestrator + analytics-insights |
| 8 | skills growth-plan + yearly-planner |
| 9 | per-channel skills — paid-advertising, aeo-geo, social-strategy, seo-plan, email-sequence (one per channel family) |
| 10 | skill content-engine (execution / output mode) |
| 11 | skills content-engine + ad-creative + video-script (creative briefs); asset rendering happens in your own creative tooling (design team, AI image/video tools, or a connected design platform), then finished assets are signed via c2pa-metadata |
| 12 | skill continuous-improvement-loop |
Parallel Dispatch
Several parts of the engagement contain independent sub-tasks that should be dispatched in parallel via multiple Task tool calls in a single message — not sequentially. Dispatching independent sub-tasks concurrently is substantially faster than running them one after another; actual time varies by engagement depth, model, and rate limits. Keep concurrent subagents to a handful (roughly 3–8) — past that you queue against API rate limits and the win drops; under 3 there is nothing to parallelize.
Cost note: total token usage is broadly similar (you're doing the same work) but billed-per-turn input costs trend up slightly because each parallel subagent re-loads its context.
Parts that benefit from parallel dispatch:
| Part | Parallel-eligible work | How to dispatch |
|---|
| Part 2 — External Research | Market sizing, competitor landscape, customer signals, regulatory landscape — none depend on each other | Dispatch the market-intelligence agent and the competitive-intel agent as parallel Task calls; invoke audience-intelligence as a skill; load compliance-rules.md (a reference file) as context — not as a subagent |
| Part 4 — Competitive + Customer + Market | Four documents (4.1, 4.2, 4.3, 4.4) are independent — they reference Part 2 only | Dispatch all four in a single message with the four respective subagents |
| Part 9 — Channel Strategy Fan-out | Up to 17 channel docs in 7 families. Families 2 (Paid platforms), 3 (Organic & Influencer), 4 (Marketplace & CRM), 5 (Content/ATL/BTL/PR) are independent after Families 1 (Search & Campaign) and 6 (Web + Measurement) complete | Sequence: F1 → (F2 ∥ F3 ∥ F4 ∥ F5 in parallel) → F6 → F7. The middle batch is four parallel Task calls in one message. |
| Part 10 — Execution Artefacts | Ad copy, post copy, headlines, CTAs across channels — independent per channel | Dispatch one subagent per channel in parallel |
| Part 11 — AI Creative Instructions | Visual asset briefs — independent per asset | Dispatch in parallel per asset |
Parts that MUST stay sequential (have hard data dependencies):
- Part 1 → Part 2 (intake feeds research scope)
- Part 3 → Part 4 (Four Core Documents feed competitive/customer/market analysis)
- Part 5 → Part 6 (Client Validation drives which docs need v2 re-runs)
- Part 7 → Part 8 (prep docs feed the Growth Plan)
- Part 8 → Part 9 (Growth Plan drives channel fan-out)
Cross-cutting rules:
- Never dispatch parallel agents that need to write to the same file simultaneously — chunk by output file.
- Each parallel subagent gets the engagement slug and the LIF path so it can read shared context, but writes ONLY to its own numbered per-part subdirectory (01-… 12-…).
- Subagents never mutate engagement state. A subagent must NOT call
lif-log-change, mark-part-completed, bump-version, or any other engagement-state.py write, and must NOT touch _engagement.json or living-instruction-file.md. Those are unlocked read-modify-write files; concurrent writers lose updates. Each subagent returns its output as per-part files only. After a parallel batch completes, the orchestrator alone applies state mutations — one lif-log-change per batch, plus mark-part-completed / bump-version as needed — and then re-reads the LIF before the next step.
- If a parallel batch fails partway, the failed subagent's outputs are NOT auto-rolled-back — re-dispatch only the failed ones; the successful peers stay valid.
For multi-dimensional commands outside the 12-part flow (e.g. /digital-marketing-pro:competitor-analysis, /digital-marketing-pro:seo-audit, /digital-marketing-pro:content-engine), the same pattern applies — dispatch independent dimensions in parallel via multiple Task calls in a single message.
Running an engagement in a single conversation
A large-context model can hold much of an engagement — intake, external research, the Four Core Documents (61 steps), competitive/customer/market analysis, Client Validation, selective v2 re-runs, preparation docs, Growth Plan + Yearly Planner, channel fan-out, execution artefacts, creative briefs, and the continuous-improvement loop — within one working session (a full engagement typically produces 50–60 canonical documents).
The checkpoint + persistence pattern is still the default — always. Even when everything fits in one conversation:
engagement-state.py + checkpoint-manager.py remain the system of record: audit trail, cross-conversation resume, and multi-user / multi-day continuity all depend on persisted state.- Do NOT skip LIF updates or state writes on the assumption that "it's all in context." An interruption still loses in-memory work, and a teammate resuming the engagement reads persisted state — not your conversation.
- The only single-conversation convenience is that you re-read fewer files mid-session because they are already in context. It does not remove the need to persist, checkpoint, and update the LIF.
Quality Discipline
- Never hand-edit
_engagement.json. Always go through engagement-state.py.
- Never delete v1. When v2 is produced, both stay.
- Always update the LIF when source docs change. Use
lif-log-change — it appends to the change-log section of living-instruction-file.md and refreshes the header date, so the LIF never goes stale.
- Always cite source per fact. Stone facts cite the validation source; Opinion hypotheses cite the client's evidence.
- Never auto-advance parts. The user confirms part completion explicitly.
- Always parallelize independent work. When a Part has 2+ independent sub-tasks (see Parallel Dispatch above), dispatch them in a single message with multiple Task calls. Sequential dispatch of independent work wastes wall-clock time and API turns.
- Never auto-execute v2 re-runs without user approval. Show the plan, get approval, then run. Cap: 2 v2 re-run rounds per part without an explicit user override (stored in state).
Examples
Example: Starting a new engagement
User: I want to start a new engagement for Acme Corp this quarter.
Skill response:
1. Verify Acme has a brand profile (slug: acme-corp). Found.
2. Initialise engagement: engagement-state.py init --brand acme-corp --id 2026-q2
3. Engagement directory created at ~/.claude-marketing/brands/acme-corp/engagements/2026-q2/
4. Walk Part 1 intake. Begin with Stone questions:
"Let's start with what we know for certain. What year was Acme founded? Where is it headquartered? How many employees today?"
5. (User answers; each fact captured via add-stone-fact)
6. Move to Opinion questions:
"Now let's capture what you believe but haven't formally validated. How would you describe Acme's positioning in the market today?"
7. (User answers; each hypothesis captured via add-opinion)
8. Mark Part 1 completed. Advise: "Part 1 is complete. The unbiased External Research (Part 2) is next — recommend running it now or scheduling it for the next session."
Example: Mid-engagement status check
User: Where are we on the Beta Industries engagement?
Skill response:
- Reads engagement-state.py status --brand beta-industries --id 2026-h1
- Shows:
Engagement: beta-industries / 2026-h1
Started: 2026-04-01 (32 days in)
Current part: Part 9 — Channel Strategy Fan-out (in progress)
Completed parts: 1, 2, 3, 4, 5, 6, 7, 8
Pending parts: 9 (in progress), 10, 11, 12
Open re-run decisions: 1 (Part 6 v2 re-runs in progress)
Open items requiring resolution: 2 (LinkedIn Ads CPL forecast pending; Q3 budget envelope pending)
LIF last updated: 2 days ago
Next required action: complete remaining Part 9 channel docs (5 of 9 complete)
Example: Applying the Decision Matrix
User: We just finished Part 5 client validation. The client rejected our positioning recommendation and added two new competitors. What re-runs do we need?
Skill response:
1. Categorise the changes:
- Two new competitors → trigger "competitors_changed"
- Positioning rejected → trigger "positioning_changed"
2. Run engagement-state.py decision-matrix --triggers "competitors_changed,positioning_changed"
3. Output the triggered re-runs:
Triggered re-runs: 3.1, 3.2, 3.3, 3.4, 4.1, 4.2
(competitors_changed triggers all four Core Docs + 4.1 + 4.2; positioning_changed adds 3.3 which is already in scope)
4. Estimate token cost: ~85K tokens for the full re-run set
5. Ask: "Approve all? Modify? Skip any?"
6. On user approval, mark Part 6 started and invoke four-core-documents skill with view=v2 for each triggered doc
Example: Update-Back correction in flight
User: We've been live for two months. Q1 channel data shows our actual CAC for Segment X is INR 4,800 — but Core Doc 3.1 v2.0 had it estimated at INR 3,000. Need to update.
Skill response:
1. Validate the correction: 60+ days of data, statistically meaningful sample. ✓
2. Read current 3.1 v2.0
3. Bump version: engagement-state.py bump-version --doc 3.1 --reason "Segment X CAC corrected from INR 3,000 to INR 4,800 based on Q1 channel data"
4. Save 3.1 v2.1 with header note: "v2.0 → v2.1 changes: Section 4 (Unit Economics) — Segment X CAC corrected. LTV:CAC drops from 4.0 to 2.5; below 3.0 health threshold."
5. lif-log-change --section "Recent Corrections" --summary "Segment X CAC corrected to INR 4,800. Channel mix for Segment X under review."
6. Identify downstream review needed: Core Doc 3.4 (DMFlow) channel allocation for Segment X may need revision
7. Add to engagement review queue
Related skills
four-core-documents — produces Part 3 deliverablesclient-validation-document — produces Part 5 deliverablegrowth-plan + yearly-planner — produce Part 8 deliverablescontinuous-improvement-loop — handles Part 12
Related references
