agentleFS
mtarcuremtarcure/claude-vibe-squad/.agents/skills/interface-ambiguity-check/SKILL.md

interface-ambiguity-check

Skill220 starsChanged 18 days agoMITView on GitHub

Use before implementing against an API, schema, file format, or contract you did not author, especially when units, null/empty/absent behavior, ordering, errors, retries, or idempotency are unstated—pin each load-bearing assumption with documentation, observed producer/consumer behavior, or a probe. Use requirements elicitation for an unclear stakeholder goal; this check resolves a concrete boundary’s semantics.

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run each element through the ambiguity battery: type and units.

    line 22
  2. Distinctive:

    When documentation and observed behavior disagree, the observed behavior wins for implementation — and the disagreement itself is a finding …

    line 25
  3. Distinctive:

    Every boundary element appears in the table with an evidence class.

    line 45
  4. Distinctive:

    No load-bearing element remains classified assumed.

    line 46

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Interface Ambiguity Check

Two parties meeting at a boundary each hold a private interpretation of it. Before writing code against an interface, make every load-bearing assumption explicit, classify it as documented, observed, or guessed — and eliminate the guesses.

When to use

  • Implementing against an API, schema, TSV/JSON format, function signature, or protocol authored by someone else (including a past task).
  • Two independently-built components are about to integrate.
  • A packet says "integrate with X" or "consume Y's output" without a contract document.

Inputs

  • The interface artifact: schema, header row, signature, endpoint doc, example payload.
  • Real producer/consumer behavior: actual payloads, actual call sites, the parser's source.

Steps

  1. List every element crossing the boundary: each field, parameter, return value, status code, file, and side effect.
  2. Run each element through the ambiguity battery: type and units; required vs optional; null vs empty vs absent; ordering and uniqueness guarantees; case, encoding, and delimiter rules; error signaling (exception, code, sentinel, silence); idempotency and retry semantics; which side validates; versioning and evolution rules.
  3. Classify each answer: documented (cite where), observed (cite the real payload, call site, or parser line you inspected), or assumed (unpinned).
  4. Pin every load-bearing assumption: read the counterpart's source, inspect a real sample, or run a cheap probe. When the boundary's owner must decide — surface the question (in a packet context: ## NEEDS FROM CHRONO, or blocked per specialist rules) instead of implementing a guess whose wrong answer is expensive to unwind.
  5. When documentation and observed behavior disagree, the observed behavior wins for implementation — and the disagreement itself is a finding to report, not to silently absorb.
  6. Encode pinned answers where the machine can hold them: a strict parser, an assertion, a test that uses a real captured sample rather than a hand-invented one.
  7. Record deliberately unpinned residuals as named risks in the artifact, with the trigger that would surface each.

Outputs

  • A boundary table: element → answer → evidence class → citation.
  • Enforcement in code or tests for the answers that matter.
  • An explicit residual list; an empty one is a claim, not a default.

Failure modes

  • Happy-path sampling — pinning semantics from one well-formed example; the ambiguity lives in the edge rows.
  • Doc trust over behavior — implementing what the doc says while the parser does otherwise; drift between them is common and directional.
  • Convenient interpretation — resolving ambiguity toward whichever reading is easiest to implement, silently.
  • Symmetric-serialization assumption — assuming what one side writes is exactly what the other side accepts.
  • Over-asking — escalating questions the artifact itself answers; questions spend operator attention, so exhaust steps 4–5's evidence paths first.

Worked example

A task must add rows to a lane-scoped TSV registry. Ambiguity battery on the lanes column: is the delimiter comma, pipe, or either? Is all a literal lane name or a wildcard? Documentation is silent. Observed: the consuming parser splits with re.split(r"[|,]", ...) and expands all to the full lane set — both cited by file:line in the artifact. The answers are enforced by running the downstream validator over the new rows rather than trusting the reading. Residual recorded: unknown lane tokens are silently dropped by the intersection with the known-lane set, so a typo in lanes fails open — flagged as a risk, not fixed, since the parser is out of scope.

Acceptance

  • Every boundary element appears in the table with an evidence class.
  • No load-bearing element remains classified assumed.
  • Doc-vs-behavior disagreements are surfaced explicitly.
  • Pinned answers are machine-enforced where feasible; residuals are named with triggers.

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