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 skills/corezoid/corezoid-ai-plugin/corezoid-process-optimizernpx skills add corezoid/corezoid-ai-plugin --skill corezoid-process-optimizergit clone --depth 1 https://github.com/corezoid/corezoid-ai-pluginWrote 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/corezoid/corezoid-ai-plugin/corezoid-process-optimizer)<a href="https://agentmods.dev/skills/corezoid/corezoid-ai-plugin/corezoid-process-optimizer"><img src="https://agentmods.dev/badge/skills/corezoid/corezoid-ai-plugin/corezoid-process-optimizer.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 | $0.00114 | $0.03134 |
| Opus 5 | $0.00057 | $0.01567 |
| Sonnet 5 | $0.00023 | $0.00627 |
| Haiku 4.5 | $0.00011 | $0.00313 |
Grade A, and why
corezoid-process-optimizer 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 5d 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 — 322 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Corezoid Process Optimizer
Mode and scope detection
Determine mode and scope from the user's phrasing before doing anything else.
Mode
| User intent | Mode |
|---|---|
| "optimize", "apply", "fix", "improve" — action verb | AUTO — analyze, plan, execute |
| "show", "what can", "suggest", "check" — analysis verb | PLAN — analyze, report, wait |
In PLAN mode, after presenting the report ask:
"Apply all? Apply by group? (1 — tacts, 2 — data, 3 — naming, 4 — resilience)"
Scope
The user may request a specific optimization group. Detect from keywords:
| Keyword(s) in request | Scope — run only |
|---|---|
| "tacts", "tact", "state changes", "nodes", "merge" | Group 1 |
| "data", "payload", "cleanup", "garbage", "fields" | Group 2 |
| "names", "naming", "titles", "readability", "descriptions" | Group 3 |
| "resilience", "semaphors", "timeouts", "stability" | Group 4 |
| No group keyword — general request | All groups |
If scope is a single group — run Phase 1 analysis only for that group. Skip all others entirely.
Still run lint-process first (its findings feed Group 1 regardless).
Examples:
- "optimize by tacts" → AUTO + Group 1 only
- "show tact optimizations" → PLAN + Group 1 only
- "add missing semaphors" → AUTO + Group 4 only
- "optimize" → AUTO + all groups
Step 0 — Resolve process
Resolve PROCESS_PATH before calling any tools:
- Check if the user provided a path, name, or ID.
- If not — ask: "Which process? Provide a file path, name, or ID."
- If name or ID — search locally:
find . -name "*.conv.json". - Read and parse the file.
- Call
lint-process— record findings. They become Group 1 quick-wins.
Step 1 — Analyze
Build a node map: id → { title, obj_type, logics[], sems[], outgoing edges }.
Trace the execution graph from the Start node following go.to_node_id and err_node_id edges.
Collect candidates for all four groups below.
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.
- 5d ago First seen · 322 lines · 114 tokens per session scan A eff9c5a2982b
corezoid-process-optimizer is a skill published in the GitHub repository corezoid/corezoid-ai-plugin (73 stars, last pushed yesterday), licensed MIT. It adds 114 tokens to every session and 3,134 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 2026-08-30.
Other skills, from other repositories
watch
File sentinel that monitors the working directory for changes and marker comments, then auto-triggers appropriate skills. Poll-based via git diff against the last scan commit. Writes intake items for batch processing and routes marker actions through /do. Use for automatic reactions to file changes; do NOT use for…
review
5-pass structured code review — correctness, security, performance, readability, consistency.
live-preview
Mid-build visual verification loop. Takes screenshots of components during construction, not just after. Catches visual regressions and invisible features before they compound. Requires Playwright or similar screenshot tool.
marshal
Meta-orchestrator that takes any direction — broad, specific, or vague — and autonomously chains skills and context into actionable work. Gathers context from codebase, docs, and memory. Only asks the user when it genuinely cannot proceed. Single-session orchestrator.
wiki
Markdown-first knowledge base where the LLM acts as librarian. Ingests raw sources, compiles and interlinks topic files, self-maintains an index. No vector DB or embeddings required -- uses LLM-native navigation over structured markdown up to 400K words.
stss
Reduce defensive disclaimers, stacked hedging, and self-protective narration in proposals and decision-facing writing. Use when the user asks to rewrite or audit a proposal, plan, research contribution, executive summary, or similar text for directness. Do not use for ordinary code work or unrelated prose.