Skip to content
FunCoding

Search

Search docs, Skills and MCP

closed-loop

Run one task through the V-model verification loop: CP-2 plan → CP-3 build → CP-3v component verify → CP-4 integration verify (full lane) → CP-5 acceptance. The worker never grades its own homework; evidence rows trace back to AC-n. Opt-in: invoke with /closed-loop or by asking for the closed loop, proper verification, or an evidence trail. Ordinary work does not run this.

AI 与智能体1.3kskills/closed-loop/SKILL.md

Install

Send this to Claude Code, Codex or Cursor. The agent checks the Skill for safety first and installs it only after you confirm.

读取 https://funcoding.ai/skills/huytieu/cog-second-brain/closed-loop/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

Closed-loop execute (V-model right arm)

Mechanical verification pipeline. Every verify step emits evidence rows tied to acceptance criterion IDs (AC-n).

When to use

The harness is opt-in. Run it when:

  • Invoked as /closed-loop <task> or /closed-loop <spec-path>.
  • The user asks for the closed loop, proper verification, or an evidence trail.
  • verification_harness: on in 00-inbox/MY-PROFILE.md and this is a build task.
  • Another skill that declares a normal+ lane reaches its verify step.

Do not run it on a request that did not ask for it. Notes, briefs, research, drafts, and ordinary edits are not harness runs, and a checkpoint ledger on those is pure overhead.

Phase 0 — Lane + run folder

bash .claude/lib/lane-classify.sh explain "<task>"
bash .claude/lib/checkpoint.sh init 04-projects/harness/runs/<YYYY-MM-DD-HHmm>
LaneCheckpoints
tinyCP-3 → CP-5 (if mutating)
normalCP-1 → CP-2 → CP-3 → CP-3v → CP-5
full+ CP-4 + claim-verifier + CP-6
bugroot-cause ledger (CP-0) before CP-3

Record: checkpoint.sh record <run-dir> CP-0 PASS|SKIP "<lane>"

Phase 1 — CP-1 Spec (acceptance criteria)

If spec exists, use its ## Acceptance criteria + traceability matrix. Else write:

04-projects/harness/runs/<id>/criteria.md using references/spec-template.md (criteria + matrix sections only).

Each criterion: falsifiable + AC-n ID + verify method.

Record: checkpoint.sh record <run-dir> CP-1 PASS "N criteria"

Phase 2 — CP-2 Plan

Map tasks → AC IDs in evidence/CP-2-plan.md. Update matrix status to pending.

Record: checkpoint.sh record <run-dir> CP-2 PASS

Phase 3 — CP-3 Build

Worker implements. Returns deliverable path only.

Phase 4 — CP-3v Component verify

retry=0
loop:
  spawn task-verifier (fresh context, read-only)
  merge EVIDENCE rows into evidence/ledger.md
  if PASS → break
  if FAIL:escalate → record CP-3v FAIL, escalate
  if FAIL:fixable && retry < 2 → fix-agent → retry++
  else → escalate

Copy verifier EVIDENCE rows into evidence/CP-3v-component.md.

Record: checkpoint.sh record <run-dir> CP-3v PASS|FAIL

Phase 5 — CP-4 Integration verify (full or multi-task)

Spawn integration-verifier (read-only). Append rows to ledger.

Skip for single-task normal.

Record: checkpoint.sh record <run-dir> CP-4 PASS|SKIP

Phase 6 — CP-5 Acceptance (post-condition)

For each mutation, observe artifact (curl, screenshot, re-fetch). Emit:

EVIDENCE AC-n | CP-5 | PASS | <observation> | <artifact>

UI/UX flow changes: the post-condition is visual. Screenshot every meaningful state with whatever browser tooling the environment has, then read the image and confirm no overflow, misalignment, clipping, wrong color, or broken responsive layout before PASS. The Observation must describe what you saw; the artifact is the screenshot/GIF in evidence/. Fix any visual defect and re-capture. See CLAUDE.md → Visual Verification.

Write evidence/CP-5-acceptance.md. Traceability closure: every AC in matrix has ≥1 PASS row in ledger.

Record: checkpoint.sh record <run-dir> CP-5 PASS|FAIL

Phase 7 — Record + handoff

  • Append to .claude/logs/loop-ledger.tsv
  • Update spec traceability matrix statuses to verified
  • full lane / big task: generate an HTML rollup from references/report-template.html → 04-projects/harness/runs/<id>/report.html, filled from criteria.md + evidence/ledger.md (criteria, AC traceability, verifier verdicts, post-condition observations). Self-contained; SendUserFile it or publish as an Artifact. Skip for normal/tiny.
  • Suggest /retro <run-dir> for CP-7

Integration

SkillLaneCP-4
ultragoalfull per phase (never downgraded)integration-verifier + north-star acceptance
team-brieffullclaim-verifier
comprehensive-analysis, auto-researchfullclaim-verifier on cited claims
content-factorynormalskip
review-cockpitnormalskip; CP-6 is the user's approval per card

Escalation template

ESCALATED — <task>
Lane: <lane> | Last CP: <CP-n>
Evidence bundle: 04-projects/harness/runs/<id>/evidence/
Open AC IDs: <list without PASS rows>
Decision needed: <one question>

Similar Skills

brand-guidelines
anthropics/skills180k

brand-guidelines

Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.

AI & agents

internal-comms
anthropics/skills180k

internal-comms

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

AI & agents

template-skill
anthropics/skills180k

template-skill

Replace with description of the skill and when Claude should use it.

AI & agents

mcp-builder
anthropics/skills180k

mcp-builder

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

AI & agents

algorithmic-art
anthropics/skills180k

algorithmic-art

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

AI & agents

academy-guide
anthropics/skills180k

academy-guide

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

AI & agents