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agent-anti-pattern

Audit agent runtimes and their tests for lifecycle inference, capability overload, context pollution, correlated verification, prompt-only control, false success, dead compatibility paths, test-only seams, and complexity without contribution. Use for whole-repository agent audits, slop or dead-code triage, test contribution reviews, large refactors, tool/context simplification, and cleanup requests that need proof before deleting code or tests.

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查看安装方法

包含的文件(3)

  • SKILL.md4.3 KB
  • agents/openai.yaml259 B
  • references/field-guide.md11.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Agent Anti-Pattern Audit

Treat a static hit as a candidate, never as a finding. Prove the failure mechanism, affected consumer, and safe correction before changing code.

Read references/field-guide.md before triage. It defines the source grades, six anti-pattern families, false-positive boundaries, finding record, and deletion gate.

Workflow

  1. Freeze the base commit, tracked scope, exclusions, and generated inventory.
  2. Reuse existing linters, dependency checks, slop scans, and test collectors. Do not create another scanner for a check they already perform.
  3. Trace each candidate through static callers plus entrypoints, registries, decorators, import-by-string, side-effect imports, subprocess workers, schemas, public exports, persisted data, and compatibility contracts.
  4. Apply AP-1 through AP-6 from the field guide. Record a harmful consequence or measured non-contribution; size, duplication, or unfamiliarity alone is insufficient.
  5. Map affected tests to behavior and invariants. Detect source-string false positives, broad-exception false greens, private-shape pinning, environment leakage, duplicated scenarios, and tests that exist only to keep dead production seams alive.
  6. Report every candidate as KEEP, SHRINK, DELETE, MEASURE, or DEFER. Stop for review before a whole-repository audit edits production or tests.
  7. Apply accepted cleanup in small causal groups. Preserve one canonical implementation and rerun objective gates after every group.

Verdicts

VerdictMeaning
KEEPA named behavior, safety, evidence, compatibility, or operational consumer justifies the surface.
SHRINKThe contract is real, but a wrapper, branch, payload, capability, or test can be narrowed.
DELETEThe complete deletion gate passes and no contribution remains.
MEASUREA suspected problem has a falsifiable hypothesis but insufficient evidence.
DEFERThe problem is real but ownership, migration, dependency, or approval blocks safe change.

GEODE Routing

Use the repository's existing surfaces when present:

  • .geode/skills/slop-audit/SKILL.md and scripts/slop_audit.py for discovery-only heuristics;
  • .claude/skills/codebase-audit/SKILL.md for general refactor workflow;
  • .claude/skills/anti-deception-checklist/SKILL.md after every cleanup diff;
  • Ruff, mypy, deptry, import-linter, architecture baseline, and pytest for deterministic verification.

Do not copy their instructions into this skill. Their results remain separate authorities because they scan different scopes and definitions.

Boundaries

  • Do not impose universal tool-count, context-token, file-size, parameter, or line-count thresholds. Treat published numbers as setup-specific unless the target contract makes them normative.
  • Do not infer dead code from zero textual imports. Check dynamic and external consumers first.
  • Do not infer duplication from a shared method name or protocol signature.
  • Do not delete tests together with code and call the resulting green suite proof. Name the surviving invariant.
  • Do not convert a safety, migration, evidence, redaction, or rollback surface into slop merely because it adds code.
  • Do not add a framework, store, registry, schema, dashboard, or automation without a named consumer and a repeated deterministic gap.
  • Fail closed to MEASURE or DEFER when evidence is incomplete.

Output

Report:

  1. base SHA, included/excluded inventory, and checks actually run;
  2. coverage by audit bucket, without claiming every line was manually read;
  3. findings with exact paths, evidence, falsifier, verdict, and smallest fix;
  4. explicit false positives and retained surfaces;
  5. cleanup order, verification, skipped/live gates, and remaining uncertainty.

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