challenge-plans

challenge-plans

中文文档: README-zh.md

Adversarially review a plan before you execute it — across the AI coding CLIs you already have logged in. No API keys.

challenge-plans orchestrates the subscription CLIs on your machine (Claude Code, Codex, …) to cross-examine a plan and surface the flaws that cause rework — then aggregates only the objections that survive scrutiny into a single verdict. It runs as a CLI, an agent skill, or a CI gate, and slots into the superpowers plan lifecycle: writing-plans → challenge-plans → executing-plans.

Why challenge-plans

• 🔑 No API keys, no per-token charges — it drives the subscription CLIs you're already logged into (Claude Code, Codex). Bring at least one.
• 🧪 Evidence beats headcount — a minority objection with a reproduction can override a majority; correctness isn't decided by voting.
• 🤝 Cross-family verification — an objection only earns hard-gate authority (✓) when an independent model family reproduces it with line-anchored evidence. Single-model claims stay advisory.
• 🛡️ Guards 7 multi-agent failure modes — vote loss, option anchoring, premature hand-off, majority-over-minority, single-round complacency, false consensus, false convergence (how).
• 🌍 Speaks your language — --lang zh (or ja, de, …) localizes every finding; one flag, no separate build.

What it does

You don't have to write specs to use this. It works in two modes — review something you've already written, or choose among options. Both hand the content to AIs from different model families to cross-examine it, and keep only the objections backed by evidence that survive another family's check — not one model's hot take.

Review (run) covers four kinds of artifact:

• 📋 Any plan (--type plan) — a trip, a launch, a hire, a move. Feed it a Kyoto itinerary that crams six stops into Day 2 and books a non-refundable flight now: it tells you which day won't fit and which step you can't undo.
• 📝 A drafted spec or design doc (--type spec) — before you build. Give it "export user CSV asynchronously": it presses on what "done" means, who signs off, and which edge case is missing — so you don't build the wrong thing from a vague spec.
• 🔧 A code change (--type diff) — a lightweight review. Pipe in a git diff: it watches for the call site you changed a function but forgot to update, the missing test, the compatibility you quietly broke.
• 🧭 A decision you've already made (--type decision) — a tech-stack pick, a vendor, a hire. Tell it "we've decided to rewrite the project in TypeScript": it pushes back — did you fairly weigh the cheaper option (just clean up the env)? Is the reason sunk cost? Can you even undo this?

Choose (weigh) is the other mode, for when you're torn between options that are still open — a technical pick or an everyday-life call alike. Picking among Postgres / MongoDB / SQLite, or between three job offers: several AIs vote and rank them, but each model family's weight is capped (so one loud model can't fake a consensus) and the strongest dissent is surfaced — you get a ranking with reasons and an opposition, not a blunt one-word answer.

Quickstart

Easiest — hand it to your agent. Tell it:

Install challenge-plans from https://github.com/hiadrianchen/challenge-plans and run challenge-plans doctor.

It sets up the package and reports which backends are ready.

Or install it yourself (Python ≥ 3.10):

pip install challenge-plans      # or: pipx install challenge-plans  ·  uvx challenge-plans doctor
challenge-plans doctor           # which CLIs are logged in

# already installed? update with the matching one-liner:
#   pip install -U challenge-plans   ·   pipx upgrade challenge-plans
#   uvx runs a cached copy — use `uvx challenge-plans@latest …` (or `--refresh`) to get the newest

Bring at least one logged-in CLI — Claude Code (claude) or OpenAI Codex (codex); two different vendors unlock cross-family verification. To use it as an agent skill, drop SKILL.md where your agent discovers skills. (Developing? git clone … && pip install -e ..)

See it work

Here's the "any plan" scenario — one of the four above — on a rough Kyoto-trip plan (examples/plan-sample.md):

$ challenge-plans run examples/plan-sample.md --type plan --sink markdown

# challenge-plans · challenge · verdict: request_changes
- [high✓] Non-refundable flights locked before validating the trip  @L10  (irreversibility_or_high_cost, by claude:feasibility)
- [med ] Day 2 packs six sights across the city — likely undoable   @L4   (ignored_constraint, by gpt:risk)
- [med ] "A good trip" is never defined, so nothing can be judged   @L1   (missing_success_criteria, by claude:goal-alignment)

Each line is a tagged objection — anchored to a line, raised by one reviewer with a single job. The three findings above came from three reviewers:

• Feasibility (can it actually be done?) → caught the non-refundable flight locked in before the plan is even validated.
• Risk (what's most likely to go wrong / is irreversible?) → caught Day 2 cramming six sights across the city with no time budget.
• Goal-alignment (do the steps serve the goal?) → caught that "a good trip" is never defined, so nothing can be judged.

Every objection is tagged from a fixed menu of ways a plan breaks — which keeps findings concrete and de-duplicable. Here's what each one catches, in this trip:

• irreversibility_or_high_cost — booking a non-refundable flight before validating the plan
• ignored_constraint — six sights in one day, no time or energy budget
• missing_success_criteria — never saying what "a good trip" means
• dependency_or_sequencing_gap — a 10:00 train right after "last-minute shopping"
• unaddressed_risk — going in mid-July with no rain/heat backup
• unstated_assumption — assuming the famous kaiseki place has a table
• no_fallback — no plan B if that restaurant is booked out
• goal_misalignment — a "relaxing" trip scheduled dawn to midnight

--profile fast runs one reviewer, standard all three (one round); deep runs the panel for multiple rounds — each round is shown what's already been found and hunts for what the last one missed — and stops when a round surfaces nothing new (convergence).

Usage

As a skill, you don't memorize flags — just ask your agent. Say "review this plan with challenge-plans", "is this spec ready to build?", or even "how do I use challenge-plans?" — it picks the mode and command for you and brings back the surviving objections.

Running it directly? Here's the map:

decision vs plan vs weigh — which one? They sit at different points in a choice's life. weigh is before you've chosen: ≥2 open options, it ranks them. --type decision is after you've chosen one: it stress-tests that single call (did you skip an alternative, is the evidence real, can you undo it). --type plan is the steps you'll take once chosen: it checks whether they can actually be executed. Same trip — weigh picks fly-vs-train, decision audits "we're flying," plan reviews the day-by-day itinerary.

--profile fast|standard|deep trades speed for depth. [sev✓] = an independent model family reproduced it with line-anchored evidence (cross-model confirmation — not a mechanically-run test), so it may hard-gate; [sev?] = unconfirmed, advisory. Ready-to-run samples live in examples/. Not pip-installed? Prefix with PYTHONPATH=src python3 -m challenge_plans.cli ….

Output in your language

The codebase is English, but reviewers can answer in any language — add --lang:

challenge-plans run plan.md --type plan --lang zh     # findings in Chinese
challenge-plans weigh options.yaml --lang ja          # deliberation in Japanese

It switches only the human-readable prose; JSON keys, enum values, and line anchors stay verbatim, so parsing and CI gates are unaffected (equivalent to setting CHALLENGE_PLANS_LANG). Your agent can pass --lang <user-language> to localize the whole review.

How it works

Multiple persona/CLI challengers steelman then attack the artifact; a cross-family Verifier must reproduce a high/critical objection with line-anchored evidence before it can hard-gate; findings are de-duplicated and resolved into one 6-state verdict — with an incomplete panel never passing as approve.

Why a tool, and not a one-file prompt / skill.md / MCP? Because the hard part isn't asking a model to "review adversarially" — it's the guarantees around that, which are control-flow, not wording: a different vendor must reproduce a finding before it can gate (not the same model judging itself); the verdict is computed in code from surviving evidence, not asserted by a model; a truncated/timed-out reply is caught instead of silently passing; and it rides your subscription instead of billing per token. In one line — the prompts produce evidence; the Python produces the verdict. The module-by-module map (what each of the 9 scripts does and why), the two modes, the deliberation flow, and the 7 failure modes are in docs/how-it-works.md. It also composes with superpowers and grill-me — see there.

Backends — what you need

Drives whatever subscription coding CLI you already have logged in — Claude Code (claude), OpenAI Codex (codex), or Kimi Code (kimi), via their headless mode, on your existing subscription. No API keys, no per-token charges from this tool. It needs at least one logged-in CLI; challenge-plans doctor names each backend's state, whether it's in your default panel, and the exact fix (install, or log in).

How much you get depends on how many different vendors you have logged in: