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/ainova-systems/intelligence-sync/intelligence-extract-skillnpx skills add ainova-systems/intelligence-sync --skill intelligence-extract-skillgit clone --depth 1 https://github.com/ainova-systems/intelligence-syncWhat 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 | $0.00014 | $0.00685 |
| Opus 5 | $0.00007 | $0.00342 |
| Sonnet 5 | $0.00003 | $0.00137 |
| Haiku 4.5 | $0.00001 | $0.00068 |
Grade A, and why
intelligence-extract-skill 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 2d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Extract Skill
Use when a workflow that ran during this session should become a reusable artifact — same sequence will be needed again by this user or by someone else using shared intelligence. Starts from observed session behavior instead of design-from-scratch.
When to use this vs intelligence-add-skill
intelligence-add-skill— design from scratch / from codebase analysisintelligence-extract-skill— extract from the conversation that just happened
Both end at the same artifact format. Extract starts from observed behavior, so the steps already exist as real working procedure.
Steps
-
Identify the pattern from session: list the concrete steps the assistant or user-and-assistant performed during the conversation. Include user decisions at each branch and assistant actions.
-
Generalize: strip session-specific details (file names, dates, specific phrasing), keep the repeatable structure. The artifact should work for the next instance of this task type, not just the one that ran.
-
Determine artifact type:
- Multi-step workflow with concrete steps → skill
- Behavioral preference / constraint / pattern to default to → rule (use
intelligence-learn-from-contextfor single preferences from session) - Knowledge area / persona / expertise scope → agent
-
Determine domain prefix (for skill / agent): reuse the existing domain when one fits — list
intelligence/skills/andintelligence/agents/. Derive from repo structure only when no existing domain matches. -
Determine naming (for skill):
<domain>-<verb>-<noun>with convention verbs —add-(one new member of a set that already exists),create-(the container itself, where nothing hosted it),update-(revise what is there),run-(execute),review-(read-only analysis). -
Check for matching agent: if creating a skill and an agent already covers the domain, link via
agent:frontmatter. If no matching agent and one is warranted, callintelligence-add-agentfirst.
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.
- 2d ago First seen · 48 lines · 14 tokens per session scan A 76730146e8e0
intelligence-extract-skill is a skill published in the GitHub repository ainova-systems/intelligence-sync (4 stars, last pushed 4d ago), licensed MIT. It adds 14 tokens to every session and 685 once invoked, about $0.0001 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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