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chrbaileychrbailey/promptspeak-mcp-server/skills/drift-review/SKILL.md

drift-review

Skill1 starsChanged 8 months agoMITView on GitHub

Analyze behavioral drift patterns across agents. Use when reviewing agent health, investigating anomalies, or during periodic governance reviews.

1 other project uses a version of this.

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    1. Pipelex/pipelex★ 949same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository3 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 1 is distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Call ps_state_drift_history with the agent ID

    line 14
  2. Distinctive:

    Always show data before recommending actions.

    line 35
  3. Very specific:

    Check audit logs with ps_audit_get if the user wants to understand what caused the drift.

    line 37

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

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Files
SKILL.md

Drift Review — PromptSpeak Governance

You are conducting a behavioral drift review across governed agents.

Workflow

  1. Get system overview — Call ps_state_system to see all agents, their circuit breaker status, and drift metrics.
  2. For each agent with drift above baseline:
    • Call ps_state_drift_history with the agent ID
    • Identify the drift trend (increasing, stable, decreasing)
    • Note which dimensions are drifting (goal displacement, tool usage patterns, confidence shifts)
  3. Present findings as a dashboard:
Agent: [agent_id]
  Status: [circuit breaker state]
  Drift: [current drift score] (baseline: [baseline])
  Trend: [↑ increasing / → stable / ↓ decreasing]
  Top concern: [most drifted dimension]
  1. Recommend actions based on severity:
    • Low drift (< 0.3 above baseline): Monitor, no action needed
    • Medium drift (0.3–0.7 above baseline): Suggest recalibrating baseline with ps_state_reset
    • High drift (> 0.7 above baseline): Recommend halting agent with ps_state_halt until root cause is identified
  2. If the user wants to act, execute the recommended tools.

Guidelines

  • Always show data before recommending actions.
  • Drift is not inherently bad — agents learning new tasks will drift. Context matters.
  • Check audit logs with ps_audit_get if the user wants to understand what caused the drift.
  • A halted agent (circuit breaker open) should be investigated before resuming with ps_state_resume.

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