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.
git clone --depth 1 https://github.com/d-mariano/spicyclaudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/d-mariano/spicyclaude/planner)<a href="https://agentmods.dev/commands/d-mariano/spicyclaude/planner"><img src="https://agentmods.dev/badge/commands/d-mariano/spicyclaude/planner.svg" alt="Measured on agentmods" height="20"></a>What 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 5.1 | $0.00018 | $0.04112 |
| Opus 5 | $0.00009 | $0.02056 |
| Sonnet 5 | $0.00004 | $0.00822 |
| Haiku 4.5 | $0.00002 | $0.00411 |
Grade A, and why
planner 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 7d ago.
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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a world class software engineer.
We are going to work on $1.
Make a detailed plan to accomplish this, based on $2.
Prioritize for rapid iteration and MVP development. Do NOT add scope.
Phase 0 — Load skills
Before pre-flight, load the skills that will shape the plan.
Available implementation skills (the catalog the per-task **Skills:** field draws from):
test-driven-development— for any production codepython-development— for Python projectsterraform-development— for Terraform projects
Steps:
- Always:
Skill(test-driven-development). The plan's Test Impact section and tautology-detection logic depend on it (Phase 2 citestest-driven-development/testing-principles.mddirectly). - Detect language now and load the matching skill:
ls pyproject.toml *.tf 2>/dev/null— ifpyproject.tomlis present,Skill(python-development); if*.tffiles are present,Skill(terraform-development). - Annotate every parent task's
**Skills:**field with the skills the implementer must load (subset of the catalog above). This is the contract/executereads — a missing field will cause execute to fail loud.
Phase 1 — Pre-flight survey (do this BEFORE drafting the plan)
Most planning bugs come from skipping this step. Read the relevant code and write a "Pre-flight findings" section that answers:
- Call-site survey. For each existing call site of code you're changing or extending: how does it currently behave on each axis you're changing? List heterogeneity explicitly. When extending a pattern across N call sites, the assumption that they're homogeneous is usually wrong — find the 1-of-N that breaks the pattern.
- Existing-test impact. For each existing test that touches state your changes affect (globals, singletons, fixtures, autouse): predict which break and how to fix them. Fixtures you add or modify will collide with existing tests that depend on the old fixture's behavior — name them.
- Same-file sibling-test enumeration. For every test file you're modifying, list ALL tests in the file in one of three buckets: CHANGED, UNCHANGED-but-named (so the implementer doesn't second-guess), UNCHANGED-irrelevant (don't enumerate). The cold-read implementer opens the file diff and wonders whether the tests you didn't touch should also have changed — the "do not touch these" list must be explicit, not implied.
- Stale-reference grep. For any symbol being renamed or removed, run
grep -rn <old-name>across the whole project and triage every hit. Imports and call sites change automatically with the rename; comments, docstrings, log messages, error strings, and test names rot silently. - Sibling-ticket coupling. For each sibling ticket this work blocks or unblocks: (a) name the API surface they depend on; confirm your surface meets their needs — if you can't confirm, flag as a pre-merge check with whoever owns the sibling; (b) deployment-window risk — what's the user-visible state of the system between this PR merging and the sibling landing? "It works" is a complete answer; so is "telemetry drops for tool X — acceptable pre-launch." Silence here is the bug.
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.
- 7d ago First seen · 210 lines · 0 tokens per session scan A 0b33af18e3d1
planner is a command published in the GitHub repository d-mariano/spicyclaude (5 stars, last pushed 1mo ago), licensed MIT. It adds 18 tokens to every session and 4,112 once invoked, about $0.0001 per session on Opus 5. 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.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.