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/unbound-force/gaze/learning-systemsnpx skills add unbound-force/gaze --skill learning-systemsgit clone --depth 1 https://github.com/unbound-force/gazeWrote 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/unbound-force/gaze/learning-systems)<a href="https://agentmods.dev/skills/unbound-force/gaze/learning-systems"><img src="https://agentmods.dev/badge/skills/unbound-force/gaze/learning-systems.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 | $0.00010 | $0.00291 |
| Opus 5 | $0.00005 | $0.00146 |
| Sonnet 5 | $0.00002 | $0.00058 |
| Haiku 4.5 | $0.00001 | $0.00029 |
Grade A, and why
learning-systems 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.
What it actually says
Learning Systems
The forge improves over time by recording outcomes and querying insights.
Recording Outcomes
After every forge completion, record the outcome:
forge_record_outcome(
bead_id="<id>",
duration_ms=120000,
success=true,
strategy="file-based",
files_touched=["internal/foo/bar.go"],
error_count=0,
retry_count=0
)
Querying Insights
Strategy Insights
Which decomposition strategies work best:
forge_get_strategy_insights(task="<task description>")
Returns success rates for file-based, feature-based, and risk-based strategies.
File Insights
Historical gotchas for specific files:
forge_get_file_insights(files=["internal/foo/bar.go"])
Returns past failure patterns, edge cases, and performance traps.
Pattern Insights
Common failure patterns across all forges:
forge_get_pattern_insights()
Returns top 5 most frequent failure patterns with recommendations.
When to Store vs Query
- Store after completing work: learnings, decisions, gotchas
- Query before starting work: check if someone solved it before
- Use
hivemind_storefor general learnings - Use
forge_record_outcomefor structured forge metrics
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 · 65 lines · 10 tokens per session scan A 07ef1ff2ed8a
learning-systems is a skill published in the GitHub repository unbound-force/gaze (2 stars, last pushed today), licensed Apache-2.0. It adds 10 tokens to every session and 291 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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