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 skills add ohong/agent-skills --skill plangit clone --depth 1 https://github.com/ohong/agent-skillsWrote 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/skills/ohong/agent-skills/plan)<a href="https://agentmods.dev/skills/ohong/agent-skills/plan"><img src="https://agentmods.dev/badge/skills/ohong/agent-skills/plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ohong/agent-skills/plan"><img src="https://agentmods.dev/badge/skills/ohong/agent-skills/plan.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00038 | $0.01270 |
| Opus 5.5 | $0.00015 | $0.00508 |
| Sonnet 5.5 | $0.00008 | $0.00254 |
| Haiku 4.5 | $0.00004 | $0.00127 |
Grade A, and why
plan scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Vague acceptance criteria.** "The API works" is not verification. "`curl localhost:3000/api/users` returns 200 with a JSON array" is. Without concrete criteria, you can't test your hypotheses. How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mission Planning — The Orientation Phase
"Orientation is the Schwerpunkt. It shapes the way we interact with the environment — hence orientation shapes the way we observe, the way we decide, the way we act." — John Boyd
You are entering the Orientation phase — the most important phase of a mission. Boyd found that the quality of orientation determines everything downstream. A fighter pilot who understands the situation writes the outcome before the fight begins. An agent who understands the codebase writes correct code on the first attempt.
Do not rush this phase. Time spent orienting is the highest-leverage investment in the entire mission.
Task: $ARGUMENTS
Step 1: Observe — Gather raw information
If $ARGUMENTS is empty or vague, ask: "What would you like me to build? Describe the end state."
If a .mission/plan.md already exists, read it and ask: "An existing mission plan was found. Do you want to (a) replace it with a new plan, or (b) refine the existing plan?"
Step 2: Orient — Probe for the full picture
Before planning, build your orientation. Ask 3-7 focused questions covering:
- Scope boundaries: What's explicitly OUT of scope?
- Existing code: Are there patterns, conventions, or architecture I should follow?
- Verification: How will we know each piece works? (existing tests, manual check, specific commands?)
- Dependencies: Are there external services, APIs, or packages involved?
- Priority: If this runs long, what's the MVP vs. nice-to-have?
Do NOT proceed until the user answers. Do NOT guess at constraints. Your orientation is only as good as the information you build it from.
Step 3: Orient deeper — Research the codebase
Boyd's orientation has five inputs. Map them:
- Project conventions (cultural traditions) — Read
CLAUDE.md,README.md,package.json/pyproject.toml. What are the team's norms? - Existing architecture (previous experience) — Map the relevant codebase with focused reads. Use subagents only when the user requests them or the work has genuinely independent, parallel branches.
- New information — What did the user tell you? What did you discover that wasn't obvious?
- Build/test/lint commands — Identify the verification tools. These are how reality will talk to you.
- Analysis & synthesis — Does the task fit cleanly into the existing architecture, or does something need to change? If there's tension, name it now.
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.
- 1mo ago First seen · 107 lines · 38 tokens per session scan A 7093ce292864
plan is a skill published in the GitHub repository ohong/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 1,270 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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