smarter-agent

A public skill for minimal, self-checking, simulation-driven agent improvement.

Agents often “learn” by adding another instruction and checking that the new sentence exists. That can bloat the prompt, duplicate an existing rule, or leave the actual decision unchanged.

smarter-agent combines three disciplines:

  1. Minimization — choose the correct durable home, remove superseded wording, and keep the smallest sufficient behavioral contract without cutting safety or context.
  2. Self-checking — falsify the candidate with conflict scans, positive/negative/boundary controls, actual decision outputs, and independent review.
  3. Simulation-driven self-improvement — run baseline and candidate instruction snapshots against the same time-ordered evidence, compare observable decision vectors, then patch and replay.

The full loop:

  1. define the old failure and expected behavioral delta;
  2. compare the proposal with a smaller candidate;
  3. audit every overlapping instruction surface;
  4. run a hindsight-free two-arm counterfactual simulation;
  5. test positive, negative, boundary, and minimization controls;
  6. exercise actual tool order, stopping, severity, authorization, side effects, and completion claims;
  7. require an independent adversarial PASS / PARTIAL / FAIL review;
  8. patch only demonstrated gaps, minimize again, and replay;
  9. preserve an auditable validation artifact.

It also forces an honest distinction between manually validating one improvement and enforcing validation across every mutation path.

Why

A rewritten prompt can look perfect and still:

  • lose to a conflicting instruction elsewhere;
  • overfit the motivating incident;
  • leak hindsight into the test;
  • preserve the same premature stopping decision;
  • broaden a safety or authorization rule;
  • bypass review when changed through a different writer;
  • be declared effective without independent evidence.

The core question is:

If the original incident arrived now, with no hindsight, would these instructions force the desired investigation and prevent the old premature conclusion—without breaking the valid control case?

Install

Hermes Agent

hermes skills install AnimaApp/smarter-agent/skills/smarter-agent --yes

This full identifier is deterministic and works immediately after publication. It installs only the canonical skill bundle and its linked support files. The shorter hermes skills install smarter-agent form depends on source-index discovery and may lag a new repository.

Or copy skills/smarter-agent/ into your Hermes skills directory.

Claude Code, Codex, Cursor, and other skill-aware agents

Copy skills/smarter-agent/ into the host's skills directory, or point the host at this repository. The skill remains opt-in unless the host's own skill system chooses it for a matching task. Instruction-file agents can use the repository's AGENTS.md; copying that file into another project intentionally makes the trigger active in that scope.

Host paths differ, but the canonical skill is always:

skills/smarter-agent/SKILL.md

Use

Load or invoke smarter-agent whenever a meaningful change is made to:

  • skills or agent instructions;
  • durable memory/profile conventions;
  • system prompts or routing policies;
  • safety/authorization boundaries;
  • classification, evidence, escalation, or completion rules.

Then fill in skills/smarter-agent/templates/validation-report.md. A specific change is behaviorally verified only when its behavioral verdict is PASS; unavailable independent review means behavioral PARTIAL. Runtime-enforcement coverage is reported separately, so one manually validated change cannot be mistaken for universal gating.

Repository contents

What it does not claim

This repository provides the behavior and verification protocol. Installing it does not prove that your runtime intercepts every skill, memory, API, CLI, sync, cron, or direct-filesystem mutation. See the runtime coverage audit in the skill and skills/smarter-agent/references/runtime-gating.md.

Validation

python3 scripts/check_bundle.py
python3 -m unittest discover -s tests -v

The checker validates structural completeness. It is not a substitute for replaying the changed agent behavior.

License

MIT