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/siddiqss/semantic-seo-suitenpx agentmods add skills/siddiqss/semantic-seo-suite/content-distributionWrote 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/siddiqss/semantic-seo-suite/content-distribution)<a href="https://agentmods.dev/skills/siddiqss/semantic-seo-suite/content-distribution"><img src="https://agentmods.dev/badge/skills/siddiqss/semantic-seo-suite/content-distribution/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/siddiqss/semantic-seo-suite/content-distribution"><img src="https://agentmods.dev/badge/skills/siddiqss/semantic-seo-suite/content-distribution.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 | $0.00126 | $0.00815 |
| Opus 5 | $0.00063 | $0.00407 |
| Sonnet 5 | $0.00025 | $0.00163 |
| Haiku 4.5 | $0.00013 | $0.00081 |
Grade A, and why
content-distribution 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 10d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
content-distribution
Publishing isn't distribution. The map decides what to write; this makes sure each piece is seen while organic traffic is still compounding. It reads the same workspace, respects the tier, and points its plays at the brand's actual personas — not a generic channel list.
Read first: ../../framework/content-distribution.md (atoms, channel fit, cadence, the
honesty rule).
Preconditions
entity-profile.json(audience + personas drive channel fit) +topical-map.json(statuses tell published from planned).- Naming the specific communities/newsletters needs
web_search: true(T1). At T0 the plan proposes channel types + atoms + cadence but not named venues — say so; don't invent subreddits or metrics.
Workflow
-
Build the plan (T0, offline).
python ../../scripts/distribution_plan.py --map brands/<slug>/topical-map.json \ --entity-profile brands/<slug>/entity-profile.json --brand "<Brand>" \ --out brands/<slug>/outreach/distribution-plan.mdPer node (published first, then core→outer): fitting channels, the repurposing atoms, and a cadence. Priorities/channel fit are
derived; the atoms areassertedformats. -
Find the real venues (T1, web_search). For the top personas, discover the specific subreddits, Slack/Discord communities, newsletters, and creators the ICP actually uses. Record each
measured+ dated with why-relevant. Never assert a community exists without checking; never attach a reach/engagement estimate. -
Write the distribution plan →
brands/<slug>/outreach/distribution-plan.md:- Priority-ordered pieces with channels, atoms, cadence.
- The named venues per persona (
measured), or an explicit note they weren't looked up (T0). - For a product that can demo itself (e.g. an AI video tool), flag the dogfood atom — generate the short-form asset with the product; the promo and the demo are one.
-
Feed the loop.
- Community questions worth answering → query-network additions via topical-map-builder.
- A link or citation earned while promoting → hand to link-opportunities / answer-engine-optimizer.
- Once GSC has data, seo-performance-tracker shows which distributed pieces actually converted attention to rankings.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 70 lines · 126 tokens per session scan A aa0ca7de825c
content-distribution is a skill published in the GitHub repository siddiqss/semantic-seo-suite (9 stars, last pushed 2mo ago), licensed MIT. It adds 126 tokens to every session and 815 once invoked, about $0.0006 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 2026-08-31.
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