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Run a full marketing engagement using the 12-Part methodology.
Passed 14 of 19 test cases with the skill, and 2 of 19 without it.
and 18 more in the full report
14 fixed3 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
I want to inspect the formatted contents of our Living Project Instruction File for brand 'nexus-tech' under engagement '2026-h2'. Which command fetches and displays this instruction file?
| Checked | Without skill | With skill |
|---|---|---|
| Executes engagement-state.py lif-show for brand nexus-tech and engagement ID 2026-h2 | Missed | Passed |
The instructions your model reads when the skill loads.
Script location. If your host does not set
${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin'sscripts/folder, next toskills/. Run them with Python 3.10 or newer:python, orpython3wherepythonis not found (macOS, many Linux systems).
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.
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 $CLAUDE_PLUGIN_DATA/digital-marketing-pro/brands/{slug}/ when that env var is set) before opening files. On re-invocation mid-session, skip files already in context.
Read these references before producing output:
This skill is invoked via the /digital-marketing-pro:engagement command family. The command is a thin router — this skill is the single source of truth for the engagement lifecycle, the checkpoint protocol, and the per-part production contract. Each subcommand maps to a specific lifecycle action. The skill calls engagement-state.py for persistence via:
python "${CLAUDE_PLUGIN_ROOT}/scripts/engagement-state.py" <subcommand> ...
You should never hand-edit _engagement.json — always go through engagement-state.py.
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 "${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py" init \
--brand "{brand_slug}" --workflow engagement --topic "{engagement_id}"
# Record the returned run_id into _engagement.json so resume can find it:
python "${CLAUDE_PLUGIN_ROOT}/scripts/engagement-state.py" set-checkpoint-run \
test cases passed, vs 2 / 19 without
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