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 commands/samibs/skillfoundry/guardloopgit clone --depth 1 https://github.com/samibs/skillfoundryWrote 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/commands/samibs/skillfoundry/guardloop)<a href="https://agentmods.dev/commands/samibs/skillfoundry/guardloop"><img src="https://agentmods.dev/badge/commands/samibs/skillfoundry/guardloop.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 | $0.00000 | $0.01658 |
| Opus 5 | $0.00000 | $0.00829 |
| Sonnet 5 | $0.00000 | $0.00332 |
| Haiku 4.5 | $0.00000 | $0.00166 |
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 yesterday.
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 — 220 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.
- yesterday First seen · 220 lines · 0 tokens per session scan A ef29233b35d6
guardloop is a command published in the GitHub repository samibs/skillfoundry (12 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,658 tokens. 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 2026-09-03.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.