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by shingo imotasimota/agent-skills85 stars
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Auditing SKILL.md normalization and compliance: scans the 21-item checklist, classifies violations, produces fix snippets. Use when auditing SKILL.md compliance or ecosystem health.

Use this Skill: https://skilld.dev/gh/simota/agent-skills/gauge

This session only. Nothing lands on disk.

referenceself-evolution.md

≈2.2k tokens on demand. Your agent reads this file only when SKILL.md points to it.

Self-Evolution Subsystem (EVOLVE)

Purpose: Self-modification workflow, triggers, safety levels, and rollback rules for Gauge. Read when: Gauge is evaluating or performing self-evolution. Pattern source: architect/reference/self-evolution.md


Overview

Aspect Gauge EVOLVE (Self) Darwin (Ecosystem)
Scope Gauge own reference/ + detection patterns Entire agent ecosystem
Direction Inward (self-improvement of audit capabilities) Outward (ecosystem evolution)
Trigger Audit completion, detection failures, web research Project lifecycle, ecosystem health
Output Self-modification + evolution log Evolution proposals for other agents

EVOLVE handles Gauge's internal improvement loop — refining detection patterns, checklist criteria, and web sources. Darwin handles ecosystem-wide evolution. They complement — never overlap.


EVOLVE Workflow

RESEARCH → EVALUATE → CLASSIFY → UPDATE → VERIFY → PERSIST

Phase Purpose Key Actions
RESEARCH Gather improvement signals Collect audit results, detection failures, user corrections, web research findings
EVALUATE Assess improvement candidates Score relevance, verify source tiers, estimate impact
CLASSIFY Determine safety level Classify each proposed change as Level A/B/C/D
UPDATE Apply safe changes Execute Level A/B changes; propose Level C for human approval
VERIFY Regression check Run self-audit (Gauge audits own SKILL.md), verify detection pattern consistency
PERSIST Record changes Write journal entry, emit signal to Darwin/Nexus, update evolution log

Phase Details

RESEARCH collects:

  • Audit results from recent SCAN/CLASSIFY cycles (false positives, false negatives)
  • User corrections to Gauge's recommendations
  • Web research findings (using queries from web-sources.md)
  • Lore pattern insights (if received)
  • Detection pattern performance data

EVALUATE scores:

  • Relevance: Does this finding address a known detection gap?
  • Source quality: What tier(s) support this finding?
  • Impact: How many skills would be affected?
  • Risk: Could this change cause false positives/negatives?

CLASSIFY assigns safety levels (see Safety Framework below).

UPDATE executes bottom-up:

  1. Take snapshot of affected files
  2. Update reference/ files first (Level A/B)
  3. Only propose SKILL.md changes at Level C (human approval)
  4. Log all changes with before/after

VERIFY validates:

  • Self-audit: Gauge's own SKILL.md still passes all 19 items
  • Detection consistency: No previously PASS items now FAIL on known-good skills
  • Reference integrity: All referenced files exist
  • On failure: full rollback to snapshot

PERSIST records:

  • Journal entry in .agents/gauge.md
  • Evolution signal to Darwin (ecosystem awareness)
  • Update cumulative evolution log

Self-Evolution Triggers

ID Condition Scope Action
GT-01 Audit task completed Lightweight RESEARCH + EVALUATE only; record observations
GT-02 Same detection failure 3+ times Medium RESEARCH → EVALUATE → CLASSIFY; auto-execute Level A
GT-03 User corrects Gauge recommendation Medium RESEARCH → EVALUATE → CLASSIFY; review detection pattern
GT-04 30+ days since last full EVOLVE cycle Full All 6 phases; web research + full calibration
GT-05 Architect new agent notification Lightweight Scan new agent; record baseline
GT-06 Darwin ecosystem evolution signal Full Re-scan all agents; recalibrate patterns

Scope Definitions

  • Lightweight: RESEARCH + EVALUATE only. No modifications. Record observations in journal.
  • Medium: RESEARCH → EVALUATE → CLASSIFY. Generate proposal but only execute Level A changes automatically.
  • Full cycle: All 6 phases. Execute Level A/B changes, propose Level C for human approval.

Safety Framework

Change Safety Levels

Level Scope Examples Approval
A: Fully Autonomous Additive changes to reference/ only New web source entry, new query template, journal entries None required
B: Autonomous + Verify reference/ updates, detection pattern refinement Detection pattern improvement, weight adjustment, PARTIAL criteria update Self-verification (VERIFY phase mandatory)
C: Human Approval Required Checklist item changes Add 17th item, remove item, redefine PASS/FAIL criteria, priority reclassification Human confirmation
D: Absolutely Forbidden Safety mechanisms themselves Own Safety Level changes, trigger condition relaxation, change budget increase, _common/*.md modifications Never allowed

Runaway Prevention Mechanisms

ID Mechanism Condition Action
RP-01 Change volume limit 3 changes/session, 10 changes/month Block further changes until budget resets
RP-02 Diminishing returns detection 3 consecutive cycles with no improvement EVOLVE paused until next external trigger
RP-03 Oscillation prevention Change → revert → re-change pattern detected Full stop, human review required
RP-04 Chain prevention 3 consecutive EVOLVE cycles without intervening audit task Defer remaining to next audit completion
RP-05 Rollback guarantee Before any UPDATE phase Full snapshot taken; VERIFY failure triggers complete restoration

Budget Tracking

Session budget:  3 changes (reference/ or detection patterns)
Monthly budget:  10 changes total
Reset:           Session = per conversation | Monthly = calendar month
Tracking:        Record in evolution log (date, change description, budget remaining)

Templates

Evolution Proposal

## GAUGE_EVOLUTION_PROPOSAL
- Date: YYYY-MM-DD
- Trigger: [GT-XX]
- Research summary: [key findings]
- Proposed changes:
  | # | Target file | Change type | Safety level | Description |
  |---|-------------|-------------|--------------|-------------|
  | 1 | reference/xxx.md | Add/Update | A/B | [description] |
- Source support:
  | Source | Tier | Relevance |
  |--------|------|-----------|
  | [URL] | T{1-4} | [why relevant] |
- Budget check:
  - Session remaining: [X] changes
  - Monthly remaining: [Y] changes
- Risk assessment: [low/medium/high]

Verification Report

## GAUGE_VERIFY_REPORT
- Date: YYYY-MM-DD
- Self-audit: [PASS/FAIL] (own SKILL.md, 19/19 items)
- Detection consistency: [PASS/FAIL] — [notes]
- Reference integrity: [all files exist: YES/NO]
- Result: [ACCEPT/ROLLBACK]
- Rollback executed: [YES/NO/N/A]

Evolution Log Entry

## GAUGE_EVOLUTION_LOG
- Date: YYYY-MM-DD
- Trigger: [GT-XX]
- Scope: [Lightweight/Medium/Full]
- Changes applied:
  - [file]: [summary of change]
- Budget consumed: [session: X / monthly: Y]
- Budget remaining: [session: X / monthly: Y]
- Next scheduled: [date or condition]

Integration Points

Partner Direction Mechanism Purpose
Darwin Darwin → Gauge Ecosystem evolution signal triggers GT-06 Ecosystem changes prompt full re-scan
Darwin Gauge → Darwin Health data in PERSIST phase Ecosystem fitness scoring input
Architect Architect → Gauge New agent notification triggers GT-05 New agent initial compliance scan
Architect Gauge → Architect P0 violation report Critical non-compliance triggers redesign
Lore Lore → Gauge Pattern insights trigger GT-02/GT-03 Cross-agent knowledge informs detection
Nexus Gauge → Nexus Routing update after checklist evolution Nexus routing stays current
Journal Gauge ↔ Journal .agents/gauge.md read/write Self-observation and pattern detection

Decision Flowchart

Audit task completed?
  └→ Run RESEARCH + EVALUATE (GT-01, Lightweight)
       └→ Issues found?
            ├→ No: Record in journal, done
            └→ Yes: Check trigger table
                 └→ Matching trigger?
                      ├→ Lightweight: Record observations only
                      ├→ Medium: CLASSIFY, execute Level A only
                      └→ Full: CLASSIFY → UPDATE → VERIFY → PERSIST
                           └→ Safety level of changes?
                                ├→ A: Execute immediately
                                ├→ B: Execute + mandatory VERIFY
                                ├→ C: Propose to human, await approval
                                └→ D: REJECT, log attempt as violation

Source: SKILL.md on GitHub

1 warning13d4 checks · Risk MEDIUM
  • Gen Agent Trust Hub13d

    The Gauge skill audits other AI agent skills for compliance and contains a self-evolution mechanism to update its own instructions via web research. It is vulnerable to indirect prompt injection from audited files and involves dynamic instruction modification.

  • Socket13d

    No alerts

  • Snyk13d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 35ffd55. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 days ago.

Activeupdated 2 weeks ago

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