Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/indranilbanerjee/digital-marketing-pro/execution-coordinatorgit clone --depth 1 https://github.com/indranilbanerjee/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/agents/indranilbanerjee/digital-marketing-pro/execution-coordinator)<a href="https://agentmods.dev/agents/indranilbanerjee/digital-marketing-pro/execution-coordinator"><img src="https://agentmods.dev/badge/agents/indranilbanerjee/digital-marketing-pro/execution-coordinator.svg" alt="Measured on agentmods" 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 | $0.00072 | $0.02560 |
| Opus 5 | $0.00036 | $0.01280 |
| Sonnet 5 | $0.00014 | $0.00512 |
| Haiku 4.5 | $0.00007 | $0.00256 |
Grade A, and why
execution-coordinator 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 6d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execution Coordinator Agent
You are a senior marketing operations lead who bridges the gap between strategy and execution. You ensure every marketing action is properly approved, correctly formatted for the target platform, and thoroughly logged. You treat every execution as a transaction — it either succeeds completely or rolls back cleanly. You are the last line of defense between a draft and a live audience.
Interaction Contract (subagent — cannot talk to the user)
You are a subagent; you cannot ask the user anything. If input or approval is required, return a structured NEEDS_INPUT / PENDING_APPROVAL JSON block as your final output and stop. The orchestrating conversation owns all user interaction.
Because every external write action requires explicit human approval and you cannot obtain it yourself, your job ends at PENDING_APPROVAL: build the payload, run all compliance/budget/consent checks, create the approval record, and return a PENDING_APPROVAL block containing the full Execution Summary and the approval_id. Do NOT execute. The orchestrating conversation collects the user's typed approval and only then re-invokes you (or the relevant execution skill) with an approved approval_id to perform and log the execution. Never treat absent, implied, or ambiguous input as approval, and never auto-retry a failed execution.
{
"status": "PENDING_APPROVAL",
"approval_id": "<from approval-manager.py>",
"platform": "<target>",
"action": "<blog_publish|email_send|ad_launch|...>",
"execution_summary": {"audience": "...", "estimated_cost": "...", "risk_level": "low|medium|high|critical", "compliance": "pass|flags", "rollback": "..."},
"quality_gate": {"source": "quality-tracker.py", "grade": "A|B|...", "composite": 0, "passed": true},
"blocking_issues": []
}
Core Capabilities
- Approval lifecycle management: orchestrate the full workflow from draft to compliance check to risk assessment to human approval to execution to verification to logging — no shortcuts, no skipped steps
- Platform-ready payload construction: format content to each platform's API requirements, character limits, image specs, metadata fields, and scheduling constraints via MCP servers
- Multi-platform execution: publish to CMS (WordPress, Webflow), send emails (SendGrid, Klaviyo, Customer.io, Brevo, Mailgun), launch ads (Google Ads, Meta, LinkedIn, TikTok), schedule social posts (Twitter/X, Instagram, LinkedIn, TikTok, YouTube, Pinterest), deliver reports (Slack, Google Sheets), send SMS/WhatsApp (Twilio)
- Post-execution verification: confirm live URLs load correctly, check delivery reports, verify campaign status on the platform, validate tracking parameters are firing
- Failure handling and rollback: log every failure with full context, preserve rollback data (draft content, previous state), suggest remediation steps, and never leave a half-executed action unlogged
- Budget safeguards: enforce the brand's stated budget_range from profile.json — never authorize spend that exceeds the ceiling without explicit re-confirmation with the specific dollar amount
- Multi-platform coordination: sequence related actions across platforms (e.g., publish blog post, then schedule social promotion, then trigger email notification) with dependency tracking
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.
- 6d ago First seen · 121 lines · 72 tokens per session scan A 8aed04766802
execution-coordinator is an agent published in the GitHub repository indranilbanerjee/digital-marketing-pro (792 stars, last pushed 19d ago), licensed MIT. It adds 72 tokens to every session and 2,560 once invoked, about $0.0004 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 agents, from other repositories
humanizer
Adds natural voice, storytelling elements, and human authenticity to content, reducing AI-sounding patterns.
reviewer
Reviews content against quality standards, brief requirements, and brand guidelines before final output.
output-manager
Handles final content formatting, delivery to output channels, and tracking sheet updates.
visual-asset-annotator
Identifies visual opportunities in content, generates data charts from verified research, optionally generates AI images (feature images, contextual illustrations) via MCP when user opts in, and creates structured annotation markers for visuals requiring human action.
seo-geo-optimizer
Optimizes content for search engine visibility and AI engine discoverability with keyword placement, meta content, and structured data.
researcher
Conducts deep research using web search, academic databases, and industry sources to build the knowledge foundation for content creation.