Token导航 LogoToken导航TokenDH.com
研究检索只读github未标认证来源可访问许可证需确认审计提醒

crucible-meta-governance坩埚元治理

Agent Skill

crucible-meta-governance 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。

总安装

192

周安装

8

GitHub Stars

37

下载量

64
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:crucible-meta-governance(坩埚元治理)
来源仓库:https://github.com/terrylica/cc-skills
仓库路径:skills/crucible-meta-governance
安装命令:
npx skills add https://github.com/terrylica/cc-skills --skill crucible-meta-governance
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/terrylica/cc-skills --skill crucible-meta-governance

简介

crucible-meta-governance 提供六类元决策模式,指导何时坚持、何时转向、何时推进。

  • 适用于复杂创作或研究项目中的方向抉择,避免在无望路径上浪费资源。
  • 核心原则是物理约束优先——当 brute force 无效时,应重构假设以适应现实限制。
  • 每次决策后必须更新日志并更新自身模式库,形成闭环进化机制提升未来判断力。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Meta-governance — 6 decision-layer patterns

Self-Evolving Skill: If any pattern here misled decisions, update the section AND append to references/evolution-log.md. Don't defer.

These patterns are meta-level — they're about the investigation itself, not its content. Invoke when a decision must be made: pivot vs persist, kill vs narrow, ship vs hold.


1. Physical-constraint-first pivot

When brute force yields null, extract the execution constraint and redesign the hypothesis class to fit it. Don't iterate on a hypothesis that ignores reality.

Session example: 17 directional-signal null campaigns → user pivoted:

"What's the best strategy for a highly random walk market?" "I can only trade on a traditional MT5 broker that allows hedging positions."

From this came the synthetic straddle (BUY_STOP + SELL_STOP pending orders, OCO). Constraint-driven design unlocked the strategy class. The math (diffusive displacement in random walks: E[|ΔS|] > 0) was always available; what was missing was honoring the execution venue.

Ask yourself:

  • What execution venue is the user actually on?
  • What types of orders are possible?
  • What's the realistic slippage / commission / spread?
  • What position-sizing constraints apply?

If the hypothesis doesn't survive these questions, pivot the hypothesis, not the statistics.


2. Incremental artifact promotion (/tmp → repo early)

Move findings from /tmp/ to the persistent repo (audits/YYYY-MM-DD-slug/) as soon as a result survives two independent tests, not "when done".

Session anti-pattern: reproducers written in /tmp/ during exploration, causing reproducibility loss on reboot. The moment a result passed Gate C (OOS) it should have been promoted — not after the 4-gate suite completed.

Promotion triggers (at least one required):

  • Result passed shuffled-null z > 3 AND hasn't been contradicted
  • An agent synthesized a verdict that supersedes an earlier one
  • A reproducer script ran successfully twice

Mechanics:

mkdir -p findings/evolution/audits/$(date +%Y-%m-%d)-slug
cp /tmp/reproducer.py /tmp/artifact.json findings/evolution/audits/.../
# Write CLAUDE.md navigator + verdict.md
# Append to evolution.jsonl

What's impermanent gets lost.


3. Gate-failure scopes not kills

When a signal fails one of the serial gates (see Skill B §2), downgrade its scope, don't kill it outright.

Failed gateAction
Gate A (directional breakdown)Learn which side — often simplify to one-side
Gate B (mirror symmetry)Note asymmetry; record as "direction-biased" feature
Gate C (OOS time-split)Kill. No scope-narrowing rescues in-sample overfit.
Gate D (cross-asset)Downgrade to <asset>-specific; keep
Gate E (per-year)Flag bad years as "regime-unfavorable"; explore regime filters

NGRAM3FU-STRADDLE-001 failed Gate D (XAUUSD, GBPUSD) but passed A/B/C/E. Status downgraded to eur-only, NOT killed. A year later, if XAUUSD develops different microstructure, it could be retested — this is the resurrect_if: trigger (see Skill D).

Principle: scope-narrowing preserves optionality. Hard kills lose negative knowledge.


4. Agent-lens disagreement as signal

When parallel agents DISAGREE, the disagreement itself is diagnostic.

Session example: 4 agents reported "lower rejection at bottom → 67.8% UP" as a signal. Agent 5 (hidden-signal hunter, critic) flagged it as label leakage. The disagreement pointed precisely at the bug.

When agents disagree:

  1. Don't average or vote — map WHAT they disagree about
  2. Check: does one agent's evidence involve an implicit assumption the other rejects?
  3. Disagreement about mechanism → investigate mechanism (may be label leakage, confound, or real but lens-bound effect)
  4. Disagreement about significance → check each agent's multiple-testing burden

Anti-pattern: picking the agent that gives the answer you want. If the critic-agent disagrees with the proposer-agents, the critic is usually right.


5. Context-budget discipline

Conversation and data context are scarce. Reserve them for the most ambiguous questions; compress known-good findings ruthlessly.

Hierarchy of compression:

  • Raw bars (not for agents; 67 MB)
  • Token-rendered bar sequences (60 KB; good for one agent)
  • Stats tables (60 KB; consumable by 5 parallel agents) — PREFERRED
  • Ledger entries (1 KB; tracks findings)

When context feels tight:

  1. Emit a fresh audit folder with artifacts; future sessions load that, not the transcript
  2. Drop detailed raw data from agent prompts; use markdown summaries
  3. If you must hand off mid-session, write a handoff file in .planning/ (not plugin scope; see project root)

Signal: context is BLOCKED when: you find yourself re-reading the same file twice in one session; or agents ask for re-briefings; or you can't remember what was decided 10 turns ago. Compress to an audit folder.


6. Supersede-not-rewrite

When a later finding replaces an earlier one, add a new ledger entry with supersedes: "OLD-ID"; update the old entry with superseded_by: "NEW-ID". Never rewrite or delete.

Why:

  • Future auditors need the trail, not the final answer
  • A superseded finding may contain negative knowledge (why it failed) that informs future work
  • Deletions create "mysterious silences" that agents can't interpret

Canonical chain from session:

NGRAM3FU-STRADDLE-001                     preliminary-positive
  ↓ supplemented by
NGRAM3FU-STRADDLE-001-GATES               gates-validated (Gate D failed → eur-only)
  ↓ supplemented by
NGRAM3FU-STRADDLE-001-FULL-HISTORY        confirmed at 7.18M bars
  ↓ supplemented by
NGRAM3FU-STRADDLE-001-FILTERED            Phase-L filter validated
  ↓ supplemented by
NGRAM3FU-STRADDLE-001-FULL-STACK          Phase-L + Phase-M final

Note supplements vs supersedes: supplement EXTENDS; supersede REPLACES. Pick the right relationship.

Anti-pattern: editing an old ledger entry because the finding "got better". That's rewriting history. Add a new entry.


Confirmation counts

PatternConfirmedNotes
1. physical-constraint pivot1The session-defining pivot (directional → straddle)
2. artifact promotionMultipleEvery /tmp → audit folder move
3. gate-failure scopes1NGRAM3FU-STRADDLE Gate D → eur-only
4. disagreement as signal2Act-2 label leakage catch; Phase L agent variance
5. context-budgetImplicitUsed every time we preferred stats tables over raw
6. supersede-not-rewrite5NGRAM3FU-STRADDLE chain, 5 entries

Post-Execution Reflection

After invoking this skill:

  1. Did a pattern save you from a bad decision? Increment confirmed count; note in references/evolution-log.md.
  2. Did a pattern produce the wrong call? Demote it; record context + link to where it misled.
  3. A new decision pattern emerged that isn't here? Draft a section.
  4. A pattern could be better-phrased for future agents? Edit the text directly; log why.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.06%
按下载量换算22

Claude

28.26%
按下载量换算18

Cursor

20.16%
按下载量换算13

Gemini CLI

8.57%
按下载量换算5

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills