AgentSkillsCN

code-mining

扫描代码仓库，挖掘值得分享的代码模式、架构决策、潜在缺陷以及实现洞见，并生成结构化的洞察简报。触发条件：“mine code”、“scan repos”、“find insights”、“what have we built”。

SKILL.md
--- frontmatter
name: code-mining
description: |
  Scan repos for post-worthy code patterns, architectural decisions, bugs,
  and implementation insights. Produces structured insight briefs.
  Triggers: "mine code", "scan repos", "find insights", "what have we built".
user-invocable: true

Code Mining

Scan project repositories for post-worthy insights. Uses git history, source files, documented bugs, and constants to find material that's grounded in real code.

MANDATORY: Follow This Runbook Exactly

Execute all 3 steps in order. Do not skip steps or substitute your own approach.

Step 1: Map Territory

Scan target repos. Default: scan all three product lines in parallel.

For each repo, run:

bash
git log --oneline -30    # Recent commits
git log --grep="fix" --oneline -15    # Bug fixes
git log --grep="refactor" --oneline -10    # Structural changes
git branch -a --sort=-committerdate | head -20    # Active branches

Priority files to read:

code
# Documented bugs (richest source, scan first)
polymaxr/polymaxr-lite/.cursor/rules/common-bugs.mdc
polymaxr/polymaxr-mm-bot/.cursor/rules/common-bugs.mdc

# Constants with reasons
polymaxr/polymaxr-lite-v2/risk/src/circuit_breakers.rs
polymaxr/polymaxr-lite-v2/risk/src/timing_filter.rs
polymaxr/polymaxr-lite-v2/strategy/src/endgame.rs

# Infrastructure decisions
megafi/server/src/common/contracts/contracts.service.ts
megafi/server/src/common/price/price.service.ts
megafi/server/src/common/blockchain/gas.service.ts

# Agent deployment lessons
ai60/agentic-org/docs/AGENT-PATTERNS.md
ai60/agentic-org/agents/common/sam-upwork/deploy.sh

Pass criteria:

  • •At least 2 repos scanned successfully
  • •At least 30 commits visible across repos
  • •At least 2 priority files read

Step 2: Trace Paths

For the most interesting findings from Step 1:

  1. •Read the actual source files touched by interesting commits. Don't stop at the commit message.
  2. •Look for constants with comments explaining "why." The number isn't the insight. The reason behind the number is.
  3. •Look for edge cases in conditionals. if balance < threshold tells you something went wrong at that threshold.
  4. •Look for error handling patterns. Retry logic, fallback chains, circuit breakers all encode production lessons.
  5. •Look for TODO/FIXME/HACK comments. These are honest admissions of known limitations.
  6. •Cross-reference: Did the same pattern or fix appear in multiple repos?

For each finding, note:

  • •Exact file path and line numbers
  • •The specific code or commit
  • •What the code actually does (not what you think it does)

Pass criteria:

  • •At least 5 findings with exact source references
  • •At least 2 findings that go beyond the commit message (required reading the actual code)

Step 3: Produce Insight Brief

For each finding that passes the depth filter, produce:

code
## Insight: [title]
Source: [repo], [file:line or commit SHA]
Category: [bug story | architecture decision | platform quirk | agent lesson | performance story | rewrite story]

What happened: [1-2 sentences, factual]
First why: [why it happened]
Second why: [why that was the case]
Structural principle: [the transferable lesson]
Non-obvious part: [what would surprise someone]
Post angle: [how to frame for AI builders, no org names]
Depth score: [1-10]

Only include insights with depth score 7+.

Final output:

code
Code Mining Brief
Date: [today]
Repos scanned: [list]
Insights found: [count]

[Insight briefs, ranked by depth score]

Recommended for next post: [which insight and why]

Pass criteria:

  • •At least 3 insights with depth score 7+
  • •Each insight has all fields filled (no blanks)
  • •Recommendation is specific and justified

Categories to Prioritize

  1. •Production bugs with lessons: Common-bugs.mdc entries, fix commits with non-obvious root causes
  2. •Architecture decisions with tradeoffs: Constants that encode hard-won knowledge
  3. •Platform quirks nobody talks about: API behaviors, precision requirements, settlement timing
  4. •Cross-repo patterns: Same lesson applied differently across products
  5. •Rewrites and pivots: v2 repos, branch history showing evolution
  6. •Agent deployment lessons: Context budgets, skill invocation, memory sync patterns

Fallback Behavior

  • •If a repo has no recent activity, note it and focus on others.
  • •If common-bugs.mdc doesn't exist in a repo, use git log --grep="fix" as substitute.
  • •If fewer than 3 insights reach depth score 7+, lower threshold to 6+ and flag them as "needs depth work."