Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pronpx agentmods add skills/indranilbanerjee/digital-marketing-pro/continuous-improvement-loopWrote 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/skills/indranilbanerjee/digital-marketing-pro/continuous-improvement-loop)<a href="https://agentmods.dev/skills/indranilbanerjee/digital-marketing-pro/continuous-improvement-loop"><img src="https://agentmods.dev/badge/skills/indranilbanerjee/digital-marketing-pro/continuous-improvement-loop/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/skills/indranilbanerjee/digital-marketing-pro/continuous-improvement-loop"><img src="https://agentmods.dev/badge/skills/indranilbanerjee/digital-marketing-pro/continuous-improvement-loop.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 | $0.00027 | $0.03060 |
| Opus 5.5 | $0.00011 | $0.01224 |
| Sonnet 5.5 | $0.00005 | $0.00612 |
| Haiku 4.5 | $0.00003 | $0.00306 |
Grade A, and why
continuous-improvement-loop 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 17d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- continuous-improvement-loop — 89% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/digital-marketing-pro:continuous-improvement-loop — Part 12 Continuous Loop
Part 12 is the continuous improvement loop that runs alongside live operations from go-live onwards. It aggregates market signals and operating signals into recommendations that feed back into the brand's product, offering, and service decisions.
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.
This is not a one-time activity. It runs perpetually once Part 11 is complete, with formal output at each Quarterly Business Review (QBR) and ad-hoc output when significant signals warrant.
Why this exists
Without an explicit feedback loop, marketing operates on assumptions made months ago. Markets shift, customers evolve, competitors move, products are refined — but if these shifts do not flow back into the strategy, the engagement silently grows stale.
Part 12 closes the loop:
- Market signals → strategy refresh
- Operating signals → tactical optimisation
- Product / offering signals → recommendations to product / business teams
The 4 Signal Sources
Source 1: Quarterly Business Reviews
Every quarterly review (per reporting-cadence.md) generates structured signals:
- KPIs vs targets (which targets were missed; which were beaten; pattern across quarters?)
- Channel-mix performance (any channel consistently outperforming or underperforming the v2 plan?)
- Audience segment performance (any segment showing different behaviour than the personas predicted?)
- Competitive shifts (any competitor moves that materially change the landscape?)
- Strategy alignment audit (is what we are executing still what the v2 strategy says we should be executing?)
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.
- 17d ago Changed · -121 tokens per session b56650eb7546
- 22d ago Changed · -7 lines 38dc578baf16
- 1mo ago First seen · 289 lines · 148 tokens per session scan A 18c0637c1227
continuous-improvement-loop is a skill published in the GitHub repository indranilbanerjee/digital-marketing-pro (911 stars, last pushed today), licensed MIT. It adds 27 tokens to every session and 3,060 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-07.
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