Use when the user has a coding or engineering prompt and wants it refined into a detailed, executable plan before any code is written — the planning stage of a prompt → plan → execute → debug pipeline. It decomposes the prompt from first principles (objective, end state, environment, building blocks, tools, packages), breaks the work into PR-sized tasks each tied to a component with its files, tests, and dependencies, orders them topologically, splits each into atomic subtasks, then a separate principal-engineer agent critiques the plan for alignment, coverage, sizing, and executability; it revises until the critique passes, emitting a plan.md layout and a structured tasks.json that a junior engineer or a smaller model can execute correctly. Not for executing, scaffolding, or debugging the plan (those are downstream loops), and not for research proposals or experiment plans.
Installation
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Use when the user has a coding or engineering prompt and wants it refined into a detailed, executable plan before any code is written — the planning stage of a prompt → plan → execute → debug pipeline. It decomposes the prompt from first principles (objective, end state, environment, building blocks, tools, packages), breaks the work into PR-sized tasks each tied to a component with its files, tests, and dependencies, orders them topologically, splits each into atomic subtasks, then a separate principal-engineer agent critiques the plan for alignment, coverage, sizing, and executability; it revises until the critique passes, emitting a plan.md layout and a structured tasks.json that a junior engineer or a smaller model can execute correctly. Not for executing, scaffolding, or debugging the plan (those are downstream loops), and not for research proposals or experiment plans.
compatibility
Requires Python 3.9+.
metadata
{"version":"0.1.0"}
Plan Loop
A planning loop: it turns a prompt into a plan detailed and correct enough to hand to a lower-tier
model. The artifact is the plan — plan.md (the layout) + tasks.json (PR-sized tasks, each with
files, tests, dependencies, and atomic subtasks). The feedback signal is two-part, like the repo's other
evaluator loops: an objective gate (tools/validate_plan.py — schema shape, an acyclic dependency
graph, a valid topological order, full component coverage) and a qualitative gate (a separate
principal engineer agent that critiques alignment, decomposition, testability, and whether a junior
could execute each task without guessing). You build the plan from first principles, validate it,
critique it, and revise until the critique passes. This is the plan stage of a larger
prompt → plan → execute → debug pipeline; it stops once the plan is ready to delegate.
When to use
Use to convert a feature/bug/refactor prompt into an executable plan grounded in a real repository —
when the goal is a hand-off artifact a downstream executor (or a smaller model) can implement
task-by-task. The plan is only as good as its weakest task for a literal-minded implementer, so the loop
optimizes for executability, not prose.
Default: ground the plan in the <repo> you are given and let the principal-engineer critique drive the
revisions. Escape hatch: if a key decision can't be resolved from the prompt or the repo, record it as an
open_question for the human rather than guessing. Not for writing the code (a downstream execute loop),
and not for research/experiment proposals (use research-proposal).
Setup
Resolve bindings interactively. If loop.run.yaml exists, load it, confirm the values in one line, and
skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is available) infer a likely
value per binding and recommend it; on other hosts ask each as a quoted prompt. Then write
loop.run.yaml (format: examples/run.example.yaml) and confirm before creating any other files.
binding
meaning
default
how to infer
<prompt>
the task to plan — a file path or inline text
—
the user's request
<repo>
the project the plan targets; read for ground-truth env + conventions (never edited)
.
the repo being worked on
<plan_file>
the human-readable plan layout (markdown)
<sandbox_root>/plan.md
—
<tasks_file>
the structured tasks, validated against schemas/plan.schema.json
<sandbox_root>/tasks.json
—
<pr_loc>
target lines of code per task (PR-sized; a task may exceed it)
100-400
—
<sandbox_root>
where plan, tasks, and the ledger live
./sandbox
—
<budget>
max refine cycles
5
—
<skill_dir> is this skill's installed folder; substitute the real path when writing loop.run.yaml.
The objective gate runs each cycle:
It prints one JSON object {ok, errors, warnings, stats} — ok must be true (no errors) before a plan
is considered ready.
The loop
Copy this checklist and tick items off.
Build the plan (iteration 0 — first principles):
Ground. Read <prompt> and inspect <repo> for ground truth: language, package manager,
runtime/OS, the test command, and existing modules/conventions to reuse. Check what you can; never
assume what you can read.
Decompose. Lay out the components from first principles — objective; end state /
definition of done; non-goals; environment; building blocks; interfaces/contracts
between blocks; tools; packages; data/assets; reuse (existing code + installed
skills); open questions. Recursively split any component too big to reason about in one piece.
Tasks (PR-sized). Break the work into tasks, each ~<pr_loc> and equivalent to one PR. For
each record: which components it serves, a description, building_blocks/tools/packages, the exact
files it creates/modifies, tests that prove it, acceptance_criteria, and estimated_loc.
Order. Fill each task's depends_on, then compute a topological order (every task after its
dependencies).
Subtasks. Split each task into atomic subtasks a junior can do with no further decisions —
"create file X", "define function f(args) -> T", "wire f into Y".
Write + validate. Write <plan_file> and <tasks_file>, then run tools/validate_plan.py;
fix every error and weigh every warning before the first critique. Log the baseline ledger row.
Refine (repeat until the plan passes or <budget>):
Critique. Spawn the principal engineer (spawn-or-degrade, roles/principal-engineer.md) with
the prompt, the repo, plan.md, tasks.json, the latest validate_plan.py output, and the list
of installed skills on this host. It returns a structured critique
(schemas/critique.schema.json): verdict, score, issues (blocking/major/minor, each with a fix),
coverage gaps, and suggested skills.
Revise. Apply the fix for every blocking and major issue — re-decompose, re-size, re-order,
add tests, pin packages, add concrete signatures/paths/data shapes so a junior can't go wrong.
Re-run tools/validate_plan.py. Append a ledger row.
Stop when the critique returns pass (no blocking or major issues) and the validator is
clean — the plan is ready to delegate. Else loop, up to <budget>; if the score plateaus with only
minor issues, stop and record them as open notes.
On stop, the deliverable is <plan_file> + <tasks_file> (plus any open_questions for the human),
built to be executed task-by-task in order by a downstream execute loop or a lower-tier model.
Plan artifacts
<tasks_file> (tasks.json) — the machine-executable plan; full contract in schemas/plan.schema.json.
Compact shape:
<plan_file> (plan.md) — the human-readable layout: objective, end state, non-goals, environment,
the components, a task table (id · title · serves · depends_on · est. LOC) in order, risks/open
questions, and a one-line "how to execute" pointer to tasks.json. It mirrors tasks.json; tasks.json is
the source of truth the executor consumes.
Ledger
<sandbox_root>/ledger.tsv, tab-separated, never commas in free text. Header
iter phase verdict score blocking major change:
iter phase verdict score blocking major change
0 build - - - - first-principles decomposition: 6 components, 8 tasks, validator ok
1 critique revise 72 1 2 PE: T3 bundles 2 PRs; T5 has no real test; nothing covers config loading
2 revise - - - - split T3 -> T3a/T3b; added retry test to T5; added T9 config loader; reordered
3 critique pass 90 0 0 PE: solid; one minor naming nit recorded as an open note
Report the final plan at the cycle the critique passed (or the best score reached at <budget>).
Constraints
Plan only — do not implement. Write no production code, scaffolding, or files in <repo>; the
output is plan.md + tasks.json. Execution and debugging are downstream loops.
Ground every claim in the real repo/environment. Do not invent packages, files, APIs, or commands;
verify against <repo>. A genuine unknown is an open_question for the human, not a guess — a plan
that confidently states something false is worse than one that flags the gap.
Keep tasks PR-sized and self-contained. Each task links to ≥1 component and has tests and
acceptance criteria; each subtask is atomic. Optimize for a literal implementer who will not fill gaps.
Keep the critic independent. The principal engineer reviews the plan it did not write
(spawn-or-degrade gives real isolation on Claude Code); never let the author pass its own plan.
Edit only inside <sandbox_root> (plan, tasks, ledger); <repo> is read-only context. Run the
loop to a passing critique or <budget> without pausing to ask whether to continue.
Roles
roles/principal-engineer.md — the adversarial plan critic. Spawn-or-degrade: a real isolated subagent
on Claude Code (the Agent/Task tool), else adopt the role inline. It is read-only, judges against a
fixed rubric, and returns JSON validated against schemas/critique.schema.json. Pass it the host's
installed-skill list so it can recommend reuse; if that list is unavailable, it simply skips
suggested_skills.
