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-learn-from-contextnpx skills add ainova-systems/intelligence-sync --skill intelligence-learn-from-contextgit 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 | $0.00017 | $0.00913 |
| Opus 5 | $0.00009 | $0.00456 |
| Sonnet 5 | $0.00003 | $0.00183 |
| Haiku 4.5 | $0.00002 | $0.00091 |
Grade A, and why
intelligence-learn-from-context 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn from Context
Use after a session where a meaningful preference, working pattern, or recurring friction emerged that should persist into future sessions. Runs in two phases — analyze (read-only) then apply (after user approval).
Principle: positive framing
LLMs follow whatever is named. Negation ("never do X") often draws attention to X. Positive framing ("default to Y", "prefer Y") steers behavior more cleanly.
This skill translates user-stated lessons before encoding:
- "Don't use NOT-comparison structures" → "State positively what IS"
- "Stop generating 3 options" → "Default to one strong recommendation"
- "Never push toward architecture framing" → "Reflect the user's framing in their own words first"
The original negative pattern stays in the rule body as an illustrative example (paired with positive replacement), but the LLM-facing instruction is positive.
Phase A — Analyze (read-only)
-
Read authoring conventions first. Discover the paths, never assume them: the umbrella is the directory holding
config.yaml(intelligence/,Intelligence/, a codename), and the engine module is the directory under it holding bothscripts/sync.shandscripts/VERSION(conventionallysync/). The meta-skills live in<module>/skills/, not directly under the umbrella. Load<module>/skills/intelligence-add-rule/SKILL.md,<module>/skills/intelligence-add-skill/SKILL.md,<module>/skills/intelligence-add-agent/SKILL.md, and<module>/docs/CONVENTIONS.md(Authoring Discipline section). This skill writes nothing on its own — it delegates to the add-* skills, which carry the authoring conventions. -
Capture the lesson from session context or user input. Strip session-specific detail, keep the underlying pattern.
-
Translate to positive form:
- "Never do X" → "Default to Y"
- "Stop doing Y" → "Do Z instead"
- Already-positive lessons keep as-is. Confirm the translation with the user if removing the negation changes meaning.
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 · 70 lines · 17 tokens per session scan A 5182f08b6059
intelligence-learn-from-context is a skill published in the GitHub repository ainova-systems/intelligence-sync (4 stars, last pushed 4d ago), licensed MIT. It adds 17 tokens to every session and 913 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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