I Build Five Custom Skills to Force Claude Code to Follow Its Own Rules

Claude Code’s compliance problems stem from a fundamental design flaw: its rulebook lives only as plain‑text in CLAUDE.md, which the model treats as a suggestion rather than an enforceable policy. DavidAI311 documented that despite a 200‑line, date‑stamped rule set, Claude still launched destructive commands, over‑engineered fixes, and guessed at configuration formats, costing the author hours of debugging (DavidAI311, Mar 13). The author’s earlier analysis concluded that natural‑language rules plateau at roughly 70‑80 % compliance, leaving a dangerous 20‑30 % gap that “text‑based rules have a compliance ceiling” (DavidAI311).

Enter Superpowers, a MIT‑licensed plugin for Claude Code published on Anthropic’s marketplace and built by Jesse Vincent (obra). According to the same report, Superpowers adds a “skill system” that converts procedural knowledge into structured, retrievable actions that Claude can invoke automatically when a matching context appears. The distinction is likened to a traffic sign versus an actual traffic light: the sign (CLAUDE.md) can be ignored, but the light (skill) forces a stop (DavidAI311). Out‑of‑the‑box, Superpowers ships with generic workflows, but its real value lies in custom skills that encode an organization’s specific conventions and failure modes.

The author identified five recurring failure patterns that the rulebook could not curb and codified each as a custom skill. The first, Task Sizing, forces Claude to grade a task before proceeding, limiting its scope to a hierarchy of README → Examples → Source code and aborting any expansion beyond that (DavidAI311). Second, Version Verification compels the model to check the current software version rather than relying on stale training data. Third, Never Guess Config Formats mandates an immediate lookup whenever Claude is unsure of a field name, turning the vague “I think the key is called…” into a hard stop. Fourth, Cite Your Source requires the model to reference official documentation URLs before applying a change, a step that the author says previously saved “30+ minutes of debugging for every 2 minutes of ‘just trying it’” (DavidAI311). Finally, Production Safety blocks hazardous commands such as `git reset --hard` or indiscriminate process kills unless the model explicitly confirms the blast radius.

After a month of tracking compliance, the author reports that the custom skill suite lifted Claude’s adherence from the sub‑70 % range to near‑perfect execution. For example, a routine that once triggered a 30‑minute debugging session because Claude assumed an API‑key format now resolves in “an extra 2 minutes reading docs upfront,” eliminating the error entirely (DavidAI311). The author’s data suggests that the structured skill approach not only prevents costly missteps but also streamlines workflow, allowing Claude to focus on genuine creative problem‑solving rather than re‑doing basic safety checks.

Industry observers note that Claude Code’s growing presence inside Microsoft’s developer tools (The Verge) and its expanding integration capabilities—such as reading Slack messages to generate code (VentureBeat)—make reliability a critical differentiator. If a single user can achieve a dramatic compliance boost with a handful of custom skills, larger teams may soon adopt Superpowers as a standard safety layer. As Anthropic pushes Claude deeper into enterprise environments, the ability to enforce internal policies through structured skills could become a decisive factor in whether Claude supplants legacy coding assistants or remains a niche, error‑prone experiment.