feedback-observing — community feedback-observing, construct-observer, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Ideal for Autonomous Agents like AutoGPT and LangChain requiring advanced feedback loop management and state-transition analysis. The empathy engine — hypothesis-first user research that captures what humans actually want. Ridden with Loa.

0xHoneyJar 0xHoneyJar
[0]
[0]
Updated: 3/5/2026

Agent Capability Analysis

The feedback-observing skill by 0xHoneyJar is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Ideal for Autonomous Agents like AutoGPT and LangChain requiring advanced feedback loop management and state-transition analysis.

Core Value

Empowers agents to detect operational patterns, emit state-transition FeedbackEvents, and optimize interactions through log analysis across all packs, utilizing error rates, duration outliers, and zero-invocation skills detection.

Capabilities Granted for feedback-observing

Automating feedback loop closure by analyzing agent interaction logs
Detecting operational state changes to optimize skill performance
Generating FeedbackEvents for healthy and concerning state transitions

! Prerequisites & Limits

  • Requires access to agent interaction logs across all packs
  • State-transition only emission may not capture gradual changes
  • Dependent on accurate detection of operational patterns and error rates
Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

feedback-observing

Install feedback-observing, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

Feedback Observing

Read agent interaction logs across all packs, detect operational patterns (error rates, duration outliers, zero-invocation skills), and emit state-transition FeedbackEvents when patterns change.

This skill closes the agent feedback loop — agents report back what worked and what failed.


Core Principle

State-transition only emission. Only emit FeedbackEvents when a skill's operational state changes (healthy → concerning, concerning → healthy). This prevents recursive signal inflation from repeated analysis of the same stable state.


Triggers

/feedback-observe                    # Analyze all packs' agent logs
/feedback-observe --pack observer    # Specific pack only
/feedback-observe --since 7d         # Time range (default: 30d)

When to Use

  • Periodically to check agent health across packs
  • After deploying new skills or modifying existing ones
  • When suspecting a skill is silently failing
  • As part of a daily/weekly maintenance routine

Workflow

Phase 1: Collect Logs

Glob: grimoires/*/agent-logs/*.jsonl

For each JSONL file:
  Parse each line (skip malformed lines, log warning)
  Filter by --pack if specified
  Filter by --since if specified (compare ts field)

Collect all log entries into a unified dataset.

If no log files found: Report "No agent logs found. Skills need to emit logs — see agent-log-format.md" and exit.

Phase 2: Detect Patterns

Group entries by pack and skill, then compute:

For each (pack, skill) pair:

  1. ERROR RATE
     error_count = entries where status="error"
     total_count = all entries
     error_rate = error_count / total_count

     IF error_rate > 0.20 → state = "concerning"
     IF error_rate <= 0.10 → state = "healthy"
     IF 0.10 < error_rate <= 0.20 → state = "warning"

  2. DURATION OUTLIERS
     Compute median duration_ms across all entries for this skill
     IF any entry has duration_ms > 2x median → flag as outlier

     IF outlier_count / total_count > 0.20 → state = "slow"

  3. ZERO-INVOCATION SKILLS
     Compare skills found in logs against manifest.json skills arrays
     Skills in manifest but NOT in logs → "dormant" (potential discoverability problem)

  4. RETRY PATTERNS
     Detect same skill invoked 3+ times within 5 minutes → "friction"

  5. MISSING ARTIFACT PATTERNS
     Entries where error contains "not found" or "missing" → "missing dependency"

Phase 3: Compare with Previous Report

Read previous report: grimoires/observer/agent-feedback/report-latest.md

IF previous report exists:
  Parse previous state per skill
  Compare current state with previous state

  ONLY emit FeedbackEvents for STATE TRANSITIONS:
    - healthy → concerning (degradation)
    - concerning → healthy (recovery)
    - new skill appeared as "dormant"
    - skill crossed duration outlier threshold for first time

  DO NOT re-emit for unchanged states.

IF no previous report:
  Treat all current states as new (emit for any non-healthy state)

Phase 4: Emit State-Transition FeedbackEvents

For each state transition detected, emit via the Loa event bus:

bash
1source .claude/scripts/lib/event-bus.sh 2 3emit_event "observer.state_transition" \ 4 '{ 5 "domain": "code", 6 "target": { 7 "type": "artifact", 8 "selector": "{pack}/{skill}" 9 }, 10 "signal": { 11 "direction": "{negative for degradation, positive for recovery}", 12 "weight": 0.5, 13 "specificity": 0.7, 14 "content": "{state_transition}: {description}", 15 "kind": "behavioral" 16 }, 17 "fingerprint": "{sha256 of '{pattern_type}:{pack}:{skill}:{window_start}:{window_end}'}" 18 }' \ 19 "observer/feedback-observing" \ 20 "" "" \ 21 "{pack}/{skill}"

The bus auto-generates id, time, specversion in the CloudEvents envelope.

Fingerprint deduplication: The fingerprint field maps to the bus idempotency key. Same fingerprint = same finding for the same time window. The bus .idempotency/ store prevents duplicate processing.

Phase 5: Generate Report

Write to grimoires/observer/agent-feedback/report-{YYYY-MM-DD}.md:

markdown
1--- 2type: agent-feedback-report 3generated: "{RFC 3339 UTC}" 4period: "{start} to {end}" 5packs_analyzed: {N} 6skills_analyzed: {N} 7transitions_detected: {N} 8--- 9 10# Agent Feedback Report: {date} 11 12## Skill Health Matrix 13 14| Pack | Skill | Invocations | Error Rate | Median Duration | State | 15|------|-------|-------------|-----------|-----------------|-------| 16| observer | observing-users | 12 | 8% | 340ms | healthy | 17| artisan | inscribing-taste | 5 | 40% | 120ms | concerning | 18| ... | ... | ... | ... | ... | ... | 19 20## State Transitions (since last report) 21 22| Skill | Previous | Current | Evidence | 23|-------|----------|---------|----------| 24| artisan/inscribing-taste | healthy | concerning | Error rate 8% → 40% | 25 26## Duration Analysis 27 28| Skill | Median | P95 | Outliers | 29|-------|--------|-----|----------| 30| ... | ... | ... | ... | 31 32## Dormant Skills 33 34| Pack | Skill | In Manifest | Invocations | 35|------|-------|-------------|-------------| 36| ... | ... | yes | 0 | 37 38## Suggested Actions 39 40- {actionable recommendation per concerning skill}

Also copy to grimoires/observer/agent-feedback/report-latest.md (overwrite).

Create grimoires/observer/agent-feedback/ directory if it doesn't exist.


Error Handling

ErrorResolution
No log files foundReport empty state, exit 0
Malformed JSONL lineSkip line, log warning, continue
No previous reportTreat all states as new
Manifest not foundSkip dormant skill detection
--pack not found in logsReport "No logs for pack {name}"

Integration Points

  • Reads: grimoires/*/agent-logs/*.jsonl, grimoires/observer/agent-feedback/report-latest.md
  • Writes: grimoires/observer/agent-feedback/report-{date}.md, grimoires/shared/feedback/events/{date}.jsonl
  • Depends on: Skills emitting agent logs (Task 1.7)
  • Consumed by: /artisan-patterns cross-domain analysis, /stale confidence system

Validation

  • Reads all packs' agent logs via glob
  • Correctly computes error rate, duration outliers
  • Only emits FeedbackEvents on state transitions
  • Fingerprint prevents duplicate emissions for same window
  • Report includes all analyzed skills
  • report-latest.md is updated
  • --pack filter works correctly
  • --since filter works correctly
  • Handles empty/missing log directories gracefully

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is feedback-observing?

Ideal for Autonomous Agents like AutoGPT and LangChain requiring advanced feedback loop management and state-transition analysis. The empathy engine — hypothesis-first user research that captures what humans actually want. Ridden with Loa.

How do I install feedback-observing?

Run the command: npx killer-skills add 0xHoneyJar/construct-observer. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for feedback-observing?

Key use cases include: Automating feedback loop closure by analyzing agent interaction logs, Detecting operational state changes to optimize skill performance, Generating FeedbackEvents for healthy and concerning state transitions.

Which IDEs are compatible with feedback-observing?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for feedback-observing?

Requires access to agent interaction logs across all packs. State-transition only emission may not capture gradual changes. Dependent on accurate detection of operational patterns and error rates.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add 0xHoneyJar/construct-observer. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use feedback-observing immediately in the current project.

Related Skills

Looking for an alternative to feedback-observing or another community skill for your workflow? Explore these related open-source skills.

View All

widget-generator

Logo of f
f

f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.

149.6k
0
AI

flags

Logo of vercel
vercel

flags is a Next.js feature management skill that enables developers to efficiently add or modify framework feature flags, streamlining React application development.

138.4k
0
Browser

zustand

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
AI

data-fetching

Logo of lobehub
lobehub

The ultimate space for work and life — to find, build, and collaborate with agent teammates that grow with you. We are taking agent harness to the next level — enabling multi-agent collaboration, effortless agent team design, and introducing agents as the unit of work interaction.

72.8k
0
AI