agentsclimarketplace

Agent system improvement

Skill SylphxAI/skills/skills/agent-system-improvement

Public agent skills from SylphxAI — standards, product procedures, and one-command sync for Codex, Claude Code, and Grok Build

Install
npx -y skills add SylphxAI/skills --skill agent-system-improvement

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 18 days oldThe repository was created 18 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Improve a recurring agent-system failure through a causal Learning Change Record, controlled intervention, replay or holdout evidence, promotion criteria, and recurrence monitoring. Use when prompts, Skills, context assembly, tools, evals, coordination, or agent workflows repeatedly produce the same error, cost, delay, or weak outcome. Do not use for a one-off product-code bug, live incident response, ordinary task planning, or evaluation of one already-defined Skill candidate alone.

SKILL.md

3.3 KB, as published. Nobody here has run it

Agent System Improvement

Turn repeated agent behavior into a tested system change, not another reminder. Read references/learning-system-methods.md before choosing the intervention and evaluation design.

Method

  1. Define the recurring outcome, affected population, impact, baseline rate, observation window, and evidence quality. Separate one incident from a repeatable class.
  2. Trace the behavior through the whole agent system: objective, instruction, injected Skills, context and memory, model, tools, permissions, state, evaluator, coordination, and feedback. Do not assume the prompt is the cause.
  3. Form competing causal hypotheses and identify the observation that would distinguish each one. Check whether the system is optimizing the wrong objective before adding more instruction.
  4. Choose the smallest intervention at the owning layer. Change policy, procedure, context, tool contract, evaluator, or feedback only where the causal mechanism lives.
  5. Freeze the baseline, candidate, expected effect, countermetrics, rollback, and promotion threshold. Use replay, a held-out task set, shadow execution, a bounded experiment, or live comparison appropriate to the claim.
  6. Compare outcome, critical failures, cost, latency, and transfer across task and model families. Reject improvements that merely move the failure or overfit the observed examples.
  7. Promote, revise, or revert. Record the exact changed artifact and install or deployment boundary. Monitor the original recurrence signal and retire superseded instructions or compensating workarounds.

Output

Produce one Learning Change Record containing:

  • recurring behavior, impact, population, baseline, and evidence;
  • system map and competing causal hypotheses;
  • selected owning cause and disconfirming evidence;
  • intervention, exact candidate, expected mechanism, and alternatives rejected;
  • evaluation design, countermetrics, critical failures, and results;
  • promote, revise, or revert verdict with rollback;
  • recurrence signal, review trigger, owner, and retained uncertainty.

Boundaries

  • Use critical-analysis when the primary job is diagnosing an uncertain cause.
  • Use skill-eval-designer for a complete exact-candidate Skill evaluation; this skill owns why the agent system should change and whether the broader intervention solved the recurring problem.
  • Use optimization-objective-review when the objective, reward, KPI, or evaluator may be creating the behavior.
  • Use incident-standard to contain and recover a live incident. A post-incident recurring system change may then use this skill.
  • Do not keep a review or delivery task open merely to observe indefinite improvement. Promote against the frozen threshold and monitor afterward.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.