Tla Check

TLA+ Check

Outputs

• TLA+ spec(s): *.tla
• TLC config(s): *.cfg
• TLC run artifacts: .tla-check/runs//... (logs, json trace if any)

Non-Negotiables (Honesty Rules)

• Never say "proved correct". Say "no counterexample found" and state the bounds/model used.
• Always surface modeling assumptions you introduced to remove ambiguity.
• If liveness is in scope, explicitly state fairness assumptions used in the run (WF_/SF_), or explicitly say "none (safety-only run)".
• Actively guard against vacuous success before calling a run "pass":
• Show that at least one non-stuttering transition is reachable.
• If using CONSTRAINT / ACTION_CONSTRAINT, list each one and the behavior it excludes.
• Reject properties that are tautological or trivially weakened.
• If any vacuity check is inconclusive, report "inconclusive coverage" instead of "pass".

Workflow (NL -> Spec+CFG -> TLC -> Iterate)

1) Pin Down Scope and Bounds (Ask, Don't Guess)

Ask for (and record) answers:

• What are the state variables?
• What are the actions/steps?
• What safety properties must never break? (invariants)
• What liveness properties must eventually happen? (temporal properties)
• If liveness is in scope, what fairness model applies to which actions? (WF_/SF_)
• What environment/failure model is in-scope? (message loss, crashes, reordering, clock skew, retries)
• What bounds make the model finite? (small sets for nodes, messages, values, time, etc.)

If the user doesn't specify bounds, propose minimal ones (and label them as "proposed"):

• 2-3 nodes, 2-3 values, short message buffers, small time domain.

2) Write the Minimal Spec Skeleton (Then Grow It)

Use a consistent structure:

• CONSTANTS for bounded sets (e.g., Nodes, Values).
• VARIABLES for state.
• Vars == > as a single canonical variable tuple name. Use the same casing (Vars) everywhere.
• TypeOK (type invariant) to keep the model honest.
• Init and Next (with UNCHANGED for untouched vars).
• For safety checks: Spec == Init /\\ [][Next]_Vars.
• For liveness checks: extend Spec with explicit fairness assumptions, e.g. /\\ WF_Vars(SomeAction) or /\\ SF_Vars(SomeAction).
• Named invariants as separate operators so they can be listed in the .cfg.

Prefer modeling the design over implementation details. If the design is fuzzy, model the uncertainty explicitly with nondeterminism and constraints.

Requirement Ledger (Prevent Hallucinated Coverage)

Maintain a compact checklist that maps each natural-language requirement to one of:

• A named invariant/operator in the spec (and listed in the .cfg)
• A temporal property (and listed in the .cfg)
• A precondition in one or more actions
• Explicitly "not modeled yet"

When reporting results, include this ledger (or a short version) so it's obvious what passed vs what was never encoded.

3) Write the TLC .cfg (Make the Model Check Run)

Baseline config (edit as needed):

SPECIFICATION Spec
\* Or:
\* INIT Init
\* NEXT Next

CONSTANTS
  \* Example:
  \* Nodes = {n1, n2, n3}
  \* Values = {v1, v2}

INVARIANT
  TypeOK
  \* Add safety invariants here

CHECK_DEADLOCK TRUE

Deadlock policy:

• Keep CHECK_DEADLOCK TRUE by default.
• If terminal states are intentional, define an explicit terminal condition in the spec and report deadlock outcomes as either "expected terminal completion" or "unexpected stall".

If you introduce CONSTRAINT / ACTION_CONSTRAINT, call it out as a coverage tradeoff and report what behavior it removes.

4) Run TLC Deterministically (Via Bundled Script)

Prereqs:

• java on PATH
• jq on PATH
• tla2tools.jar available and pointed to by TLA2TOOLS_JAR (or pass --jar)

Run (from the tla-check skill directory):

scripts/tlc_check.sh --spec path/to/Foo.tla --cfg path/to/Foo.cfg

This writes a run directory under the spec folder:

• .tla-check/runs//summary.json
• .tla-check/runs//tlc.stdout
• .tla-check/runs//tlc.stderr
• .tla-check/runs//counterexample.json (only if TLC produced one)

5) Iterate (Tight Loop)

If TLC fails:

• Explain the failure using the dumped trace (focus on state deltas and the violated property).
• Patch the spec/config minimally.
• Re-run and compare.

If TLC passes:

• Report: bounds, invariants/properties checked, fairness assumptions used (or "none"), deadlock interpretation, and what's still unmodeled.
• Confirm vacuity checks passed; otherwise report "inconclusive coverage."
• Example: "Checked with 3 nodes, 2 values, bounded message buffer of size 2; no counterexample found."

Resources

scripts/

• scripts/tlc_check.sh: run TLC with -dumpTrace json, capture logs, emit summary.json
• scripts/tlc_trace_summary.sh: summarize a counterexample.json into step-by-step diffs (optional helper)

references/

• references/spec_skeleton.md: minimal skeleton patterns and cfg snippets

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: younes-io
• Source: younes-io/agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.