BC code-that-fits-in-your-head
Software-engineering heuristics based on Mark Seemann's Code That Fits in Your Head (2021), updated for agent-driven development. Use when writing or reviewing code, refactoring accidental complexity or a Big Ball of Mud, controlling technical or architectural debt in generated code, designing APIs and invariants, adding a feature through a walking skeleton and acceptance tests, debugging a defect with reproducible tests or bisection, threat-modelling endpoints and trust boundaries with STRIDE, planning a legacy or Strangler migration with rollback, or setting up a maintainable codebase. Covers decomposition and cyclomatic complexity, cohesion, encapsulation, outside-in TDD, separation of concerns, Git/review discipline, safe evolution, and troubleshooting. Not for language syntax, framework tutorials, production incident response, or performance profiling.
Software-engineering heuristics based on Mark Seemann's Code That Fits in Your Head (2021), updated for agent-driven development.
As a process C 60/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 3
✓ No critical or high findings
Medium and low: 3
-
low Secrets in code
secret-high-entropy-tokenreferences/decomposition/examples.md:180High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)return NoTa…ror();
fixture -
low Secrets in code
secret-high-entropy-tokenreferences/decomposition/patterns.md:115High-entropy token-like string (may be an id, hash or a credential)return NoTa…ror();
-
low Secrets in code
secret-high-entropy-tokenreferences/troubleshooting/patterns.md:178High-entropy token-like string (may be an id, hash or a credential)return NoTa…ror();
Files scanned: 53. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 60/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 1 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 85Steps. 10 steps, 1 vague phrases
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1494 tokens
- 100Progress reporting. Reports progress
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +3Description length 869: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +4Structure: 6 headings
- +3Step-by-step instructions: 10 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.