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 rules/johnnichev/selectools/selectools-agent-coregit clone --depth 1 https://github.com/johnnichev/selectoolsWhat 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 | $0.00456 |
| Opus 5 | $0.00000 | $0.00228 |
| Sonnet 5 | $0.00000 | $0.00091 |
| Haiku 4.5 | $0.00000 | $0.00046 |
Grade A, and why
selectools-agent-core 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.
What it actually says
Agent Core Rules
Execution Flow (in order)
- Input guardrails validate/redact user message
- Memory loads history, provider called (or fallback chain)
- Cache checked — hit returns cached response
- Provider formats prompt + calls LLM, cache stores result
- Output guardrails validate LLM response
- Parser extracts TOOL_CALL, reasoning extracted
- Policy engine evaluates tool call (allow/review/deny)
- Coherence check verifies tool matches user intent
- Tool executes (parallel if multiple), output screening applied
- Trace records step, audit logger writes, usage tracks costs
- Loop continues or returns AgentResult
AgentResult Always Contains
.content— final text response.trace— AgentTrace with typed timeline.reasoning— why agent chose tools.usage— aggregated token/cost stats
Integration Points for New Features
When adding a feature that touches the agent loop:
- Add config fields to
agent/config.py - Add new
StepTypetotrace.pyif recording trace steps - Add observer events to
observer.pyif emitting lifecycle events - Guard observer calls with
if run_id:for consistency - Use
_notify_observers()helper, never call observer methods directly - Wrap observer calls in try/except to prevent crashing agent
Thread Safety
FallbackProviderobserver wiring usesthreading.Lock+ refcountbatch()usesThreadPoolExecutor— each thread gets isolated historyabatch()usesasyncio.gatherwith copied agent instances- Direct concurrent
arun()on same agent shares_history(known limitation)
Defensive Patterns
response_msg.content or ""— providers can return None contentelif response_format is None:— prevent parser intercepting structured output_memory_add_many()— ensures on_memory_trim observers firerouting_onlypath must still fireon_iteration_end
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 · 48 lines · 0 tokens per session scan A 3ec136e3fa8a
selectools-agent-core is a cursor rule published in the GitHub repository johnnichev/selectools (11 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 456 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 2026-08-30.
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