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Add action/expectation/result structure to all skill definitions

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Each step in every SKILL.md now carries labeled Action, Expectation,
and Result blocks so the agent can mechanically execute, verify, and
branch at each stage. Format chosen after comparing three variants
(indented blocks, inline keywords, tables) on a prove-validity
simulation; indented blocks scored highest on routing completeness
and checkability.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Angelica Moreira 2026-03-11 19:51:59 +00:00
parent d349b93d1d
commit 9d674404c8
10 changed files with 364 additions and 48 deletions
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37
.github/skills/optimize/SKILL.md vendored
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@ -7,7 +7,17 @@ Given a set of constraints and an objective function, find the optimal value. Z3
# Step 1: Formulate the problem
The formula uses the `(minimize ...)` or `(maximize ...)` directives followed by `(check-sat)` and `(get-model)`.
Action:
Write constraints and an objective using `(minimize ...)` or
`(maximize ...)` directives, followed by `(check-sat)` and `(get-model)`.
Expectation:
A valid SMT-LIB2 formula with at least one optimization directive and
all variables declared.
Result:
If the formula is well-formed, proceed to Step 2. For multi-objective
problems, list directives in priority order for lexicographic optimization.
Example: minimize `x + y` subject to `x >= 1`, `y >= 2`, `x + y <= 10`:
```smtlib
@ -23,6 +33,17 @@ Example: minimize `x + y` subject to `x >= 1`, `y >= 2`, `x + y <= 10`:
# Step 2: Run the optimizer
Action:
Invoke optimize.py with the formula or file path.
Expectation:
The script prints `sat` with the optimal assignment, `unsat`, `unknown`,
or `timeout`. A run entry is logged to z3agent.db.
Result:
On `sat`: proceed to Step 3 to read the optimal values.
On `unsat` or `timeout`: check constraints for contradictions or simplify.
```bash
python3 scripts/optimize.py --file scheduling.smt2
python3 scripts/optimize.py --formula "<inline smt-lib2>" --debug
@ -30,11 +51,17 @@ python3 scripts/optimize.py --formula "<inline smt-lib2>" --debug
# Step 3: Interpret the output
- `sat` with a model: the optimal assignment satisfying all constraints.
- `unsat`: the constraints are contradictory; no feasible solution exists.
- `unknown` or `timeout`: Z3 could not determine optimality.
Action:
Parse the objective value and satisfying assignment from the output.
The script prints the objective value and the satisfying assignment.
Expectation:
`sat` with a model containing the optimal value, `unsat` indicating
infeasibility, or `unknown`/`timeout`.
Result:
On `sat`: report the optimal value and assignment.
On `unsat`: the constraints are contradictory, no feasible solution exists.
On `unknown`/`timeout`: relax constraints or try **simplify**.
# Parameters