Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/cbmono/ai-setup/plangit clone --depth 1 https://github.com/cbmono/ai-setupWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00000 | $0.02634 |
| Opus 5 | $0.00000 | $0.01317 |
| Sonnet 5 | $0.00000 | $0.00527 |
| Haiku 4.5 | $0.00000 | $0.00263 |
Grade A, and why
plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Draft an implementation plan, grill it with independent adversarial reviewers, and — once the user approves — save the refined plan as a checked-in artifact that rides with the stacked PRs.
Use this for any non-trivial task where a weak plan would compound into a bad implementation.
/plan grills an approach — before any code exists; it attacks the reasoning. To grill a diff you've already written, use /grill instead. (Same adversarial fan-out, different target.)
Steps
-
Derive a slug for the plan file:
- Grep the current branch name and the last 5 commit subjects for
\b[A-Z]{2,}-\d+\b. If a Jira-style key matches (e.g.AUTH-1234), slug = that key. - Otherwise, build a 3–5 kebab-case word summary of
$ARGUMENTS, prefixed with a verb when one fits —feat-rotate-oauth-keys,fix-auth-race,chore-bump-deps,refactor-payments-module. If no prefix fits, drop it (migrate-to-rsc). - If
$ARGUMENTSis empty or too thin to summarise, ask the user for a slug before continuing.
- Grep the current branch name and the last 5 commit subjects for
-
Draft a plan based on
$ARGUMENTS(the task). Structure:- Goal — one sentence.
- Files to touch — concrete paths.
- Steps — GitHub-checkbox list (
- [ ] step); each small enough that progress is observable as work lands. - Edge cases — concurrency, retries, partial failure, untrusted inputs.
- Out of scope — explicit non-goals.
- Acceptance criteria — how we'll know it's done.
Keep the plan tight — 10–20 bullet points, not an essay. Don't save it to disk yet.
-
Grill the plan adversarially. A plan reviewed by the same model that wrote it self-anchors — it confirms its own assumptions instead of attacking them. Instead, fan out independent reviewer subagents, each a fresh context with one adversarial lens. Synthesis and the decision to loop back stay with you (the main loop).
Size the grill to the plan — this is the cost dial; don't throw 8 Opus reviewers at a one-file fix. Announce the setup in one line and let the user retune before launching. Invoking
/planis your opt-in to run the Workflow, so don't ask whether — only let them change the model or lens set:- Small (bug fix, refactor, ≤ ~3 files / ≤ ~5 steps) → the core 4 lenses (★) on Sonnet.
- Large (greenfield, many files, new abstractions) → all 8 lenses on Opus.
- e.g. "Small plan → core 4 lenses on Sonnet. Say 'opus' or 'all 8' to widen, otherwise I'll launch."
- Frontend plans (the plan adds/changes UI —
.tsx/.jsx/.vue/.svelte/.htmlor component/page/form files, same signal as/grill) → invoke thetest-locatorsskill first, then add thelocatorslens below. Skip it for backend-only plans.
The lenses (core 4 marked ★) — each reviewer gets exactly one:
- ★ assumptions — Which single assumption, if wrong, breaks the whole plan? What did the author inherit without confirming?
- ★ failure_modes — What fails silently? Which new codepath has no error handling or a swallowed error?
- ★ dependencies — Does each step really depend on the prior one, or is the order arbitrary? Is every acceptance criterion concrete and testable, not "works correctly"?
- ★ alternatives — Was a simpler approach dismissed too fast? Is there a 1-file fix behind a 5-file plan?
- scope_drift — What snuck in beyond the goal? What belongs in "Out of scope" but isn't there?
- testing_gaps — What edge case passes the plan's checks but still breaks prod? What does each acceptance criterion actually prove?
- missing_risks — What risk is suspiciously absent? (rollback path, data migration, observability gap)
- business_fit — Does the approach serve the actual goal, or has it drifted into a neat solution to the wrong problem?
- locators (frontend plans only) — Apply the
test-locatorsskill: does the plan ensure new interactive/asserted elements get stabledata-testid/data-test(business-meaningful, not position/CSS-based)? If the plan adds UI but never mentions test locators, that's the finding.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 139 lines · 0 tokens per session scan A cc81038143c7
plan is a command published in the GitHub repository cbmono/ai-setup (2 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,634 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.