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 skills/bananasjim/openplan-skill/openplan-alignnpx skills add BANANASJIM/openplan-skill --skill openplan-aligngit clone --depth 1 https://github.com/BANANASJIM/openplan-skillWhat 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 | $0.00078 | $0.00516 |
| Opus 5 | $0.00039 | $0.00258 |
| Sonnet 5 | $0.00016 | $0.00103 |
| Haiku 4.5 | $0.00008 | $0.00052 |
Grade A, and why
openplan-align 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 2d 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.
What it actually says
OpenPlan Align
Use this after $openplan-core when the next safe action depends on understanding the goal/intent.
Alignment Flow
- Restate the user's request in one paragraph.
- Separate facts from assumptions.
- Walk the 7-layer funnel only as far as needed.
- Identify blind spots and human decisions.
- Recommend the next route: ask via the human-facing coordinator, research, design, implement, review, or stop.
7-Layer Funnel
| Layer | Question | Enough When |
|---|---|---|
| L1 WHY | Why does this need to exist? | Job-to-be-done is explicit |
| L2 WHO/WHERE | Who uses it and in what constraints? | Context and stakeholders are clear |
| L3 WHAT | What is in and out of scope? | Boundaries are explicit |
| L4 HOW macro | What modules or artifacts are involved? | Interfaces and ownership are named |
| L5 RISK | What might fail or be unknown? | Risks have a research or mitigation path |
| L6 VERIFY | How will success be checked? | Testable criteria exist |
| L7 DETAIL | What exact implementation detail matters? | Work is actionable |
Do not force all seven layers for a small request. Use the smallest depth that prevents a wrong next action.
Output
For lightweight alignment:
Goal/Intent:
Known facts:
Assumptions:
Blind spots:
Human decisions:
Recommended next route:
For formal alignment, use references/alignment-contract.md.
Rules
- When the user gives a solution, identify what problem it solves unless already clear.
- Ask at most 1-3 questions only if this agent is the human-facing coordinator; otherwise list
Human decisions required. - Do not make the human's tradeoff decision.
- If enough is clear, proceed with a stated assumption instead of blocking.
- If evidence is missing, recommend research rather than inventing facts.
For Claude Code, use assets/claude-code-command.md as a slash command or prompt seed.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 56 lines · 78 tokens per session scan A 24dc7bae5e3f
openplan-align is a skill published in the GitHub repository BANANASJIM/openplan-skill (4 stars, last pushed 3mo ago), licensed MIT. It adds 78 tokens to every session and 516 once invoked, about $0.0004 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.
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