Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add indranilbanerjee/digital-marketing-pro/plugin install digital-marketing-proWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/indranilbanerjee/digital-marketing-pro/resume)<a href="https://agentmods.dev/commands/indranilbanerjee/digital-marketing-pro/resume"><img src="https://agentmods.dev/badge/commands/indranilbanerjee/digital-marketing-pro/resume/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/indranilbanerjee/digital-marketing-pro/resume"><img src="https://agentmods.dev/badge/commands/indranilbanerjee/digital-marketing-pro/resume.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.01458 |
| Opus 5 | $0.00019 | $0.00729 |
| Sonnet 5 | $0.00008 | $0.00292 |
| Haiku 4.5 | $0.00004 | $0.00146 |
Grade A, and why
resume scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Resume Interrupted Workflow
Pick up a long-running DMP workflow that stopped before the final step — instead of restarting from scratch, load the saved part outputs and continue from the next part.
Trigger
User runs /digital-marketing-pro:resume (with optional workflow and/or run-id arguments). Also surface this command in any error message when a workflow terminates abnormally.
What this fixes
DMP's headline workflow /digital-marketing-pro:engagement runs the 12-Part Strategy Flow, producing 50-60 canonical files (actual time varies by engagement depth and model). If the session terminated partway through (context-window exhaustion, network blip, the user cancels, machine sleeps), the in-memory part outputs used to be lost and the user had to restart from Part 1. The same applied to /digital-marketing-pro:campaign-plan, /digital-marketing-pro:content-engine, /digital-marketing-pro:seo-audit, /digital-marketing-pro:competitor-analysis, /digital-marketing-pro:campaign-audit, /digital-marketing-pro:launch-campaign.
Now every part of every long workflow writes its output via checkpoint-manager.py, so a fresh session can reload those artifacts and skip the parts that already completed. This is the direct fix for the user-team feedback that "dm pro also taking too long to process" — the workflow itself is not made faster, but a single interruption no longer means losing completed work.
Process
Step 1: Pick the run to resume
If the user supplied a run-id, use it directly. If they supplied only a workflow (e.g. engagement / campaign-plan), filter to in-progress runs of that workflow and pick the most recent. Otherwise list every in-progress run and ask the user to choose if there's ambiguity.
# Auto-pick the most recent in-progress run for the active brand
python "${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py" resume --brand "{active_brand}"
# Filter to a specific workflow
python "${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py" resume --brand "{active_brand}" --workflow engagement
# Resume a specific run id
python "${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py" resume --brand "{active_brand}" --run-id "{run_id}"
# List everything (use when there are several)
python "${CLAUDE_PLUGIN_ROOT}/scripts/checkpoint-manager.py" list --brand "{active_brand}"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 111 lines · 38 tokens per session scan A 6f8fc7ede121
resume is a command published in the GitHub repository indranilbanerjee/digital-marketing-pro (806 stars, last pushed 4d ago), licensed MIT. It adds 38 tokens to every session and 1,458 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
brand-setup
Configure brand voice, terminology, compliance guardrails, and style guide for content production.
output-folder
Print the absolute path to the user-visible ContentForge output folder and open it in the OS file manager.
create-content
Run the full 10-phase content production pipeline — research, draft, fact-check, humanize, and publish.
audit-content
Audit your content library for freshness decay, coverage gaps, and optimization opportunities.
content-brief
Generate a research-backed content brief with keyword data, competitor analysis, search intent, and SEO strategy.
publish
Publish finished content to Webflow or WordPress with preview, verification, and HTML export fallback.