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Agent Skill · 研究检索

ring%3adev-feedback-loop(Ring%3adev 反馈循环)

ring%3adev-feedback-loop 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

ring%3adev-feedback-loop 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为权限未标注;许可证信息还不明确,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 46,029。

总安装 815周安装 35GitHub Stars 180下载 286核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ring%3adev-feedback-loop(Ring%3adev 反馈循环)
来源仓库:https://github.com/lerianstudio/ring
仓库路径:skills/ring%3Adev-feedback-loop
安装命令:
npx skills add https://github.com/lerianstudio/ring --skill ring:dev-feedback-loop
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/lerianstudio/ring --skill ring:dev-feedback-loop

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。支持基于关键词、任务场景或来源线索进行信息检索与筛选。通过 GitHub 仓库获取技能定义,需结合原始 README 确认具体用法。安装前建议确认权限范围、维护状态及是否触发联网或文件操作。

ring%3adev-feedback-loop 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/ring%3Adev-feedback-loop

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/lerianstudio/ring --skill ring:dev-feedback-lo…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

ring%3adev-feedback-loop 怎么安装?

在终端执行 npx skills add https://github.com/lerianstudio/ring --skill ring:dev-feedback-loop。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ring%3adev-feedback-loop 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ring%3adev-feedback-loop 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

ring%3adev-feedback-loop 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ring%3adev-feedback-loop 用的人多吗?

公开数据是累计安装 815 次,近一周安装 35 次,下载 286 次,GitHub 180 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ring%3adev-feedback-loop 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
ring%3adev-feedback-loop

Ring%3adev 反馈循环 · 本页

815180权限未标注未明确未收录

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Dev Feedback Loop

Overview

See CLAUDE.md for canonical validation and gate requirements. This skill collects metrics and generates improvement reports.

Continuous improvement system that tracks development cycle effectiveness through assertiveness scores, identifies recurring failure patterns, and generates actionable improvement suggestions.

Core principle: What gets measured gets improved. Track every gate transition to identify systemic issues.

Step 0: TodoWrite Tracking (MANDATORY FIRST ACTION)

<cannot_skip>

  • MUST add feedback-loop to todo list before any other action
  • MUST execute TodoWrite IMMEDIATELY when skill starts
  • CANNOT proceed to Step 1 without TodoWrite execution </cannot_skip>

⛔ HARD GATE: Before any other action, you MUST add feedback-loop to todo list.

Execute this TodoWrite call IMMEDIATELY when this skill starts:

TodoWrite tool:
  todos:
    - id: "feedback-loop-execution"
      content: "Execute ring:dev-feedback-loop: collect metrics, calculate scores, write report"
      status: "in_progress"
      priority: "high"

Why this is mandatory:

  • TodoWrite creates visible tracking of feedback-loop execution
  • Prevents skill from being "forgotten" mid-execution
  • Creates audit trail that feedback was collected
  • Hook enforcer checks for this todo item

After completing all feedback-loop steps, mark as completed:

TodoWrite tool:
  todos:
    - id: "feedback-loop-execution"
      content: "Execute ring:dev-feedback-loop: collect metrics, calculate scores, write report"
      status: "completed"
      priority: "high"

Anti-Rationalization:

RationalizationWhy It's WRONGRequired Action
"TodoWrite slows things down"1 tool call = 2 seconds. Not an excuse.Execute TodoWrite NOW
"I'll remember to complete it"Memory is unreliable. Todo is proof.Execute TodoWrite NOW
"Skill is simple, no tracking needed"Simple ≠ optional. all skills get tracked.Execute TodoWrite NOW

You CANNOT proceed to Step 1 without executing TodoWrite above.


Severity Calibration

SeverityCriteriaExamples
CRITICALScore 0 (rejected), system failureUser rejected validation, complete workflow failure
HIGHScore < 70, threshold breachAssertiveness below 70, recurring pattern unresolved
MEDIUMScore 70-79, pattern emergingAcceptable but degraded, same issue twice
LOWScore 80-89, minor improvementsGood but not excellent, optimization opportunity

Report all severities. CRITICAL = mandatory post-mortem. HIGH = root cause analysis. MEDIUM = track pattern. LOW = document.


Pressure Resistance

See shared-patterns/shared-pressure-resistance.md for universal pressure scenarios.

Feedback-specific note: Feedback MUST be collected for every completed task, regardless of outcome or complexity. "Simple tasks" and "perfect scores" still need tracking.

Common Rationalizations - REJECTED

See shared-patterns/shared-anti-rationalization.md for universal anti-rationalizations.

Feedback-specific rationalizations:

ExcuseReality
"It was just a spike/experiment"Spikes produce learnings. Track what worked and what didn't.
"Perfect score, no insights"Perfect scores reveal what works. Document for replication.
"Reporting my own failures reflects badly"Unreported failures compound. Self-reporting is professional.
"Round up to passing threshold"Rounding is falsification. Report exact score.

Red Flags - STOP

See shared-patterns/shared-red-flags.md for universal red flags.

If you catch yourself thinking any of those patterns, STOP immediately. Collect metrics for every task.


Self-Preservation Bias Prevention

Agents must report accurately, even when scores are low:

Bias PatternWhy It's WrongCorrect Behavior
"Round up score"Falsifies data, masks trendsReport exact: 68, not 70
"Skip failed task"Selection bias, incomplete pictureReport all tasks
"Blame external factors"Avoids actionable insightsDocument factors + still log score
"Report only successes"Survivorship biasSuccess and failure needed

Reporting protocol:

  1. Calculate score using exact formula (no rounding)
  2. Report score regardless of value
  3. If score < 70, mandatory root cause analysis
  4. Document honestly - "I made this mistake" not "mistake was made"
  5. Patterns in low scores = improvement opportunities, not blame

Self-interest check: If you're tempted to adjust a score, ask: "Would I report this score if someone else achieved it?" If yes, report as-is.

Mandatory Feedback Collection

<cannot_skip>

  • Feedback collection for EVERY completed task
  • No exemptions for task complexity, outcome, or time pressure
  • "Nothing to report" is still data that must be recorded </cannot_skip>

Non-negotiable: Feedback MUST be collected for every completed task, regardless of:

FactorStill Collect?Reason
Task complexity✅ YESSimple tasks reveal patterns
Outcome quality✅ YES100-score tasks need tracking
User satisfaction✅ YESApproval ≠ process quality
Time pressure✅ YESMetrics take <5 min
"Nothing to report"✅ YESAbsence of issues is data

Consequence: Skipping feedback breaks continuous improvement loop and masks systemic issues.


Repeated Feedback Detection

When the same feedback appears multiple times:

RepetitionClassificationAction
2nd occurrenceRECURRINGFlag as recurring issue. Add to patterns.
3rd occurrenceUNRESOLVEDEscalate. Stop current work. Report blocker.

Recurring feedback indicates systemic issue not being addressed.

Escalation format:

## RECURRING ISSUE - Escalation Required

**Issue:** [Description]
**Occurrences:** [Count] times across [N] tasks
**Pattern:** [What triggers this issue]
**Previous Responses:** [What was tried]

**Recommendation:** [Systemic fix needed]
**Awaiting:** User decision on root cause resolution

Threshold Alerts - MANDATORY RESPONSE

<block_condition>

  • Task score < 70 → Root cause analysis required
  • Gate iterations > 3 → STOP, request human intervention
  • Cycle average < 80 → Deep analysis required </block_condition>

When thresholds are breached, response is REQUIRED:

AlertThresholdRequired Action
Task score< 70Document what went wrong. Identify root cause.
Gate iterations> 3STOP. Request human intervention. Document blocker.
Cycle average< 80Deep analysis required. Pattern identification mandatory.

You CANNOT proceed past threshold without documented response.

Blocker Criteria - STOP and Report

always pause and report blocker for:

Decision TypeExamplesAction
Score interpretation"Is 65 acceptable?"STOP. Follow interpretation table.
Threshold override"Skip analysis for this task"STOP. Analysis is MANDATORY for low scores.
Pattern judgment"Is this pattern significant?"STOP. Document pattern, let user decide significance.
Improvement priority"Which fix first?"STOP. Report all findings, let user prioritize.

Before skipping any feedback collection:

  1. Check if task is complete (feedback required for all completed tasks)
  2. Check threshold status (alerts are mandatory)
  3. If in doubt → STOP and report blocker

You CANNOT skip feedback collection. Period.


Assertiveness Score Calculation

Base score of 100 points, with deductions for inefficiencies:

Penalty Matrix

EventPenaltyMax PenaltyRationale
Extra iteration (beyond 1)-10 per iteration-30Each iteration = rework
Review FAIL verdict-20-20Critical/High issues found
Review NEEDS_DISCUSSION-10-10Uncertainty in implementation
Unmet criterion at validation-10 per criterion-40Requirements gap
User REJECTED validation-100 (score = 0)-100Complete failure

Score Calculation Formula

score = 100 - min(30, extra_iterations*10) - review_fail*20 - needs_discussion*10 - min(40, unmet_criteria*10) | User rejected → score = 0

Score Interpretation

Score RangeRatingAction Required
90-100ExcellentNo action needed
80-89GoodMinor improvements possible
70-79AcceptableReview patterns, optimize
60-69Needs ImprovementRoot cause analysis required
< 60PoorMandatory deep analysis
0FailedFull post-mortem required

Step 1: Collect Cycle Metrics

MANDATORY: Execute this step for all tasks, regardless of:

  • Score value (even 100%)
  • User satisfaction (even immediate approval)
  • Outcome quality (even perfect)

Anti-exemption check: If you're thinking "perfect outcome, skip metrics" → STOP. This is Red Flag at line 75 ("Perfect outcome, skip the metrics").

After task completion, gather from agent_outputs in state file:

Structured Data Fields (NEW)

The state file now contains structured error/issue data for direct analysis:

GateStructured FieldsUse For
Gate 0implementation.standards_compliance, implementation.iterationsImplementation standards patterns
Gate 1devops.standards_compliance, devops.verification_errors[]DevOps standards + build/deploy failures
Gate 2ring:sre.standards_compliance, ring:sre.validation_errors[]SRE standards + observability gaps
Gate 3testing.standards_compliance, testing.failures[], testing.uncovered_criteria[]Testing standards + test failures + coverage
Gate 4review.{reviewer}.standards_compliance, review.{reviewer}.issues[]Review standards + issues by category/severity

All gates have standards_compliance with:

  • total_sections, compliant, not_applicable, non_compliant
  • gaps[] - array of non-compliant sections with details

Reading Structured Data

# From state file, extract standards compliance from all gates:
all_standards_gaps = [
  ...agent_outputs.implementation.standards_compliance.gaps,
  ...agent_outputs.devops.standards_compliance.gaps,
  ...agent_outputs.ring:sre.standards_compliance.gaps,
  ...agent_outputs.testing.standards_compliance.gaps,
  ...agent_outputs.review.code_reviewer.standards_compliance.gaps,
  ...agent_outputs.review.business_logic_reviewer.standards_compliance.gaps,
  ...agent_outputs.review.security_reviewer.standards_compliance.gaps
]

# Gate-specific errors/issues:
devops_errors = agent_outputs.devops.verification_errors
sre_errors = agent_outputs.ring:sre.validation_errors
test_failures = agent_outputs.testing.failures
uncovered_acs = agent_outputs.testing.uncovered_criteria
review_issues = [
  ...agent_outputs.review.code_reviewer.issues,
  ...agent_outputs.review.business_logic_reviewer.issues,
  ...agent_outputs.review.security_reviewer.issues
]

# Aggregate standards compliance metrics:
total_standards_sections = sum(all_gates.standards_compliance.total_sections)
total_compliant = sum(all_gates.standards_compliance.compliant)
overall_compliance_rate = total_compliant / total_standards_sections * 100

Iteration Penalty Calculation

# Total extra iterations across all gates:
extra_iterations = (
  max(0, implementation.iterations - 1) +
  max(0, devops.iterations - 1) +
  max(0, ring:sre.iterations - 1) +
  max(0, testing.iterations - 1) +
  max(0, review.iterations - 1)
)

Step 2: Calculate Assertiveness Score

Apply formula: Base 100 - deductions (extra iterations, review failures, unmet criteria) = Final Score / 100. Map to rating per interpretation table.

Step 3: Analyze Prompt Quality (Agents Only)

After calculating assertiveness, analyze prompt quality for all agents that executed in the task.

3.1 Load Agent Outputs

Read agent_outputs from state file (docs/ring:dev-cycle/current-cycle.json or docs/ring:dev-refactor/current-cycle.json):

Agents to analyze (if executed, not null):
  - implementation: ring:backend-engineer-golang | ring:backend-engineer-typescript
  - devops: ring:devops-engineer
  - ring:sre: ring:sre
  - testing: ring:qa-analyst
  - review: ring:code-reviewer, ring:business-logic-reviewer, ring:security-reviewer

3.2 Dispatch Prompt Quality Reviewer

<dispatch_required agent="prompt-quality-reviewer"> Analyze prompt quality for all agents executed in this task. </dispatch_required>

Task tool:
  subagent_type: "ring:prompt-quality-reviewer"
  prompt: |
    Analyze prompt quality for agents in task [task_id].

    Agent outputs from state:
    [agent_outputs]

    For each agent:
    1. Load definition from dev-team/agents/ or default/agents/
    2. Extract rules: MUST, MUST not, ask_when, output_schema
    3. Compare output vs rules
    4. Calculate score
    5. Identify gaps with evidence
    6. Generate improvements

    Return structured analysis per agent.

3.3 Write Feedback Files

Directory: docs/feedbacks/cycle-YYYY-MM-DD/

One file per agent, accumulating all tasks that used that agent.

File: docs/feedbacks/cycle-YYYY-MM-DD/{agent-name}.md

# Prompt Feedback: {agent-name}

**Cycle:** YYYY-MM-DD
**Total Executions:** N
**Average Score:** XX%

---

## Task T-001 (Gate X)

**Score:** XX/100
**Rating:** {rating}

### Gaps Found

| Category | Rule | Evidence | Impact |
|----------|------|----------|--------|
| MUST | [rule text] | [quote from output] | -X |

### What Went Well

- [positive observation]

---

## Task T-002 (Gate X)

**Score:** XX/100
...

---

## Consolidated Improvements

### Priority 1: [Title]

**Occurrences:** X/Y tasks
**Impact:** +X points expected
**File:** dev-team/agents/{agent}.md

**Current text (line ~N):**

[existing prompt]

**Suggested addition:**

[new prompt text]


### Priority 2: [Title]

...

3.4 Append to Existing File

If file already exists (from previous task in same cycle), append the new task section before "## Consolidated Improvements" and update:

  • Total Executions count
  • Average Score
  • Consolidated Improvements (re-analyze patterns)

Step 3.5: Pattern Analysis from Structured Data (NEW)

Analyze structured error/issue data to identify recurring patterns:

3.5.1 Standards Compliance Patterns

# Group gaps by section name
standards_gaps_by_section = group(all_implementation_gaps, by: "section")

# Identify recurring gaps (same section fails across tasks)
recurring_standards_gaps = filter(standards_gaps_by_section, count >= 2)

# Output pattern
For each recurring gap:
  - Section: [section name]
  - Occurrences: [N] tasks
  - Common reason: [most frequent reason]
  - Recommendation: [agent prompt improvement]

3.5.2 Review Issue Patterns

# Group review issues by category
issues_by_category = group(all_review_issues, by: "category")

# Group by severity for prioritization
issues_by_severity = group(all_review_issues, by: "severity")

# Identify top recurring categories
top_categories = sort(issues_by_category, by: count, descending).take(5)

# Output pattern
For each top category:
  - Category: [category name]
  - Occurrences: [N] issues across [M] tasks
  - Severity breakdown: [CRITICAL: X, HIGH: Y, MEDIUM: Z]
  - Most common: [most frequent description pattern]
  - Fix rate: [fixed_count / total_count]%

3.5.3 Test Failure Patterns

# Group failures by error_type
failures_by_type = group(all_test_failures, by: "error_type")

# Identify flaky tests (same test fails multiple times)
flaky_tests = filter(all_test_failures, count_by_test_name >= 2)

# Output pattern
For each error type:
  - Type: [assertion|panic|timeout|compilation]
  - Occurrences: [N] failures
  - Fix iterations: avg [X] iterations to fix

3.5.4 Cross-Gate Pattern Correlation

# Correlate: Do standards gaps predict review issues?
correlation_standards_review = correlate(
  implementation.standards_compliance.gaps[].section,
  review.*.issues[].category
)

# Output insight
If correlation > 0.5:
  "Standards gap in [section] correlates with review issues in [category]"
  → Recommendation: Strengthen agent prompt for [section]

Step 4: Threshold Alerts

AlertTriggerActionReport Contents
Score < 70Individual task assertiveness < 70Mandatory root cause analysisFailure events, "5 Whys" per event, Corrective actions, Prevention measures
Iterations > 3Any gate exceeds 3 iterationsSTOP + human interventionIteration history, Recurring issue, Options: [Continue/Reassign/Descope/Cancel]
Avg < 80Cycle average below 80Deep analysis reportScore distribution, Failure patterns (freq/cause/fix), Improvement plan
Recurring PatternSame issue category in 3+ tasksPattern alertCategory, frequency, suggested prompt fix

Report formats: RCA = Score → Failure Events → 5 Whys → Root cause → Corrective action | Gate Blocked = History → Issue → BLOCKED UNTIL human decision | Deep Analysis = Distribution → Patterns → Improvement Plan

Step 5: Write Feedback Report

Location: docs/dev-team/feedback/cycle-YYYY-MM-DD.md

Required sections:

SectionContent
HeaderDate, Tasks Completed, Average Assertiveness
Task SummaryTable: Task ID, Score, Rating, Key Issue
By GateTable: Gate, Avg Iterations, Avg Duration, Pass Rate
By PenaltyTable: Penalty type, Occurrences, Points Lost
PatternsPositive patterns (what works) + Negative patterns (what needs improvement)
RecommendationsImmediate (this sprint), Short-term (this month), Long-term (this quarter)
Next ReviewDate, Target assertiveness, Focus areas

Step 6: Generate Improvement Suggestions

Improvement types based on pattern analysis:

TargetWhen to SuggestFormat
AgentsSame issue type recurring in reviewsAgent name → Issue → Suggestion → Specific addition to prompt
SkillsGate consistently needs iterationsSkill name → Issue → Suggestion → Specific change to skill
ProcessPattern spans multiple tasksProcess area → Issue → Suggestion → Implementation

Step 7: Complete TodoWrite Tracking (MANDATORY FINAL ACTION)

⛔ HARD GATE: After all steps complete, you MUST mark feedback-loop todo as completed.

Execute this TodoWrite call to finalize:

TodoWrite tool:
  todos:
    - id: "feedback-loop-execution"
      content: "Execute ring:dev-feedback-loop: collect metrics, calculate scores, write report"
      status: "completed"
      priority: "high"

Verification before marking complete:

  • Step 1: Metrics collected from state file
  • Step 2: Assertiveness score calculated
  • Step 3: Prompt quality analyzed (if agents executed)
  • Step 4: Threshold alerts checked
  • Step 5: Feedback report written to docs/dev-team/feedback/
  • Step 6: Improvement suggestions generated

You CANNOT mark todo as completed until all steps above are done.


Execution Report

Base metrics per shared-patterns/output-execution-report.md.

MetricValue
Tasks AnalyzedN
Average AssertivenessXX.X%
Threshold AlertsX
Root Cause AnalysesY
Improvement SuggestionsZ
Report Locationdocs/dev-team/feedback/cycle-YYYY-MM-DD.md

Anti-Patterns

Never:

  • Skip feedback collection for "simple" tasks
  • Ignore threshold alerts
  • Accept low scores without analysis
  • Generate suggestions without data
  • Blame individuals instead of process

Always:

  • Track every gate transition
  • Calculate score for every task
  • Investigate scores < 70
  • Document root causes
  • Generate actionable improvements
  • Review trends over time

Feedback Loop Integration

IntegrationProcess
RetrospectivesShare metrics → Discuss trends → Prioritize improvements → Assign actions
Skill UpdatesDocument gap → Update skill → Track improvement → Iterate
Agent UpdatesIdentify behavior → Update prompt → Track change → Validate

平台分布

Codex

36.92%
按下载量换算106

Claude

32.38%
按下载量换算93

Cursor

18.94%
按下载量换算54

Gemini CLI

9.32%
按下载量换算27

安全审计

暂无安全审计结果可展示。

权限和风险

权限未标注

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安装前确认

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