Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/samibs/skillfoundrynpx agentmods add skills/samibs/skillfoundry/guardloopWrote 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/samibs/skillfoundry/guardloop)<a href="https://agentmods.dev/skills/samibs/skillfoundry/guardloop"><img src="https://agentmods.dev/badge/skills/samibs/skillfoundry/guardloop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/samibs/skillfoundry/guardloop"><img src="https://agentmods.dev/badge/skills/samibs/skillfoundry/guardloop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 5.1 | $0.00012 | $0.01680 |
| Opus 5.5 | $0.00005 | $0.00672 |
| Sonnet 5.5 | $0.00002 | $0.00336 |
| Haiku 4.5 | $0.00001 | $0.00168 |
Grade A, and why
guardloop 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 1mo 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/guardloop — Adaptive Learning Guardrail Engine
Analyzes recurring failure patterns harvested from coding sessions and promotes them into enforced rules in
agents/_guardloop-rules.md.Powered by GuardLoop (github.com/samibs/guardloop.dev) × SkillFoundry.
Usage
/guardloop Pattern frequency report (default)
/guardloop analyze Same as above — full report with candidates
/guardloop promote Promote ready patterns → agents/_guardloop-rules.md
/guardloop scan Scan codebase for known failure patterns right now
/guardloop status Show pattern counts + hook health
/guardloop reset Reset all pattern counters (use after major cleanup)
Instructions
You are the GuardLoop Engine — the self-learning layer that converts observed LLM failures into enforced guardrails. You learn from this project's real history, not from theoretical rules.
Default / analyze — Pattern Frequency Report
Step 1: Run analysis script
bash scripts/guardloop-analyze.sh
Step 2: Read the pattern state directly for additional context
.claude/hooks/state/guardloop-patterns.json
Step 3: Read last 5 entries tagged guardloop from the knowledge base to show recent examples
memory_bank/knowledge/errors-universal.jsonl
(filter lines where "tags" array contains "guardloop", take the last 5)
Step 4: Present the report:
GuardLoop Analysis — <date>
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Patterns tracked: 10
Total detections: N
Ready to promote: N
Already promoted: N
[table from script output]
Recent detections:
[last 3 from knowledge base]
If there are patterns ready to promote, recommend: Run /guardloop promote to generate guardrails.
promote — Promote Patterns to Agents
Step 1: Run promotion script
bash scripts/guardloop-promote.sh
Step 2: Read the updated agents/_guardloop-rules.md to confirm the new rules
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.
- 1mo ago First seen · 226 lines · 12 tokens per session scan A 3137de7274d0
guardloop is a skill published in the GitHub repository samibs/skillfoundry (12 stars, last pushed 14d ago), licensed MIT. It adds 12 tokens to every session and 1,680 once invoked, about $0.0000 per session on Opus 5.5. 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.
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