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 commands/frankxai/starlight-intelligence-system/content-systemizegit clone --depth 1 https://github.com/frankxai/Starlight-Intelligence-SystemWrote 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/frankxai/starlight-intelligence-system/content-systemize)<a href="https://agentmods.dev/commands/frankxai/starlight-intelligence-system/content-systemize"><img src="https://agentmods.dev/badge/commands/frankxai/starlight-intelligence-system/content-systemize.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 | $0.00053 | $0.02054 |
| Opus 5 | $0.00026 | $0.01027 |
| Sonnet 5 | $0.00011 | $0.00411 |
| Haiku 4.5 | $0.00005 | $0.00205 |
Grade A, and why
content-systemize 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 4d 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/content-systemize
Load SIP.md, VERTICALS.md, VOICES.md, creator/pipeline-<person-slug>.md, genius/profile-<person-slug>.md, and executor/<exec-slug>-playbook.md if an executor is specified. Convert a single content pillar into a reusable production system — so the person (or a trained executor) can ship pillar-aligned content without re-inventing from scratch each time.
Input
$ARGUMENTS
When this command fires
- A pillar from
/creator-pipelinehas 3+ pieces shipped; pattern is visible. - Person wants to delegate pillar production to an executor, or to a future self on autopilot.
- Pillar shape has stabilized — same shape of anchor, same voice beats, same derivative pattern.
When this command does NOT fire
- Pillar has fewer than 3 shipped pieces → halt, pattern recognition needs data.
- No pipeline exists → route to
/creator-pipeline. - Person wants to explore a new pillar, not systemize an existing one → stay in
/creator-pipeline.
Process
-
Validate pillar exists in pipeline.
- Resolve
<person-slug>and<pillar-slug>from args. - Check
creator/pipeline-<person-slug>.mdexists; locate### Pillar — <pillar-name>section. - If either missing, halt:
Pipeline or pillar not found. Run /creator-pipeline first, ship 3+ pieces in the pillar, then systemize. - Count shipped pieces in pillar (from per-piece plans marked shipped, or from
ATTESTATIONS.md). If < 3, halt with:Pillar has fewer than 3 shipped pieces. Pattern recognition needs data. Ship more, then systemize.
- Resolve
-
Extract the repeating pattern.
- Read all shipped pieces in the pillar. What's the repeating shape?
- Hook format (question? contrarian claim? frame flip?)
- Structure (problem → mechanism → application? story → principle → invitation?)
- Voice beats (technical warmth? laconic punch? conversational drift?)
- Length (tight 800w or expansive 2500w?)
- Cadence of ideas (one per section? three stacked?)
- Write one paragraph summarizing the pattern. This is the skeleton's spine.
- Read all shipped pieces in the pillar. What's the repeating shape?
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.
- 4d ago First seen · 174 lines · 53 tokens per session scan A 9129aaea9d3d
content-systemize is a command published in the GitHub repository frankxai/Starlight-Intelligence-System (8 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 2,054 once invoked, about $0.0003 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.
Other commands, from other repositories
evolve-lite-adapt-memory
Mirror a just-saved native memory into the shared evolve store so it becomes shareable and auditable.
evolve-lite-publish
Publish a private guideline to a configured write-scope repo.
presence-reset
Wipe presence state for the current project, all projects, or specific subsystems. Asks for confirmation before destructive action.
promote
Move .memory/ artifacts to permanent knowledge locations.
dream
Run a curator pass over the memory dir. Produces a proposal artifact for /dream-apply. Default curator: rot.
manage-rules
Manage rules — conventional or conditional, trigger design, tag-pinned citations, smoke test.