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/wrannaman/agentic-engineering/plannpx skills add wrannaman/agentic-engineering --skill plangit clone --depth 1 https://github.com/wrannaman/agentic-engineeringWhat 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 | $0.00020 | $0.01992 |
| Opus 5 | $0.00010 | $0.00996 |
| Sonnet 5 | $0.00004 | $0.00398 |
| Haiku 4.5 | $0.00002 | $0.00199 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Write comprehensive implementation plans assuming the engineer has zero context for our codebase and questionable taste. Document everything they need to know: which files to touch for each task, code, testing, docs they might need to check, how to test it. Give them the whole plan as bite-sized tasks. DRY. YAGNI. Frequent commits.
Assume they are a skilled developer, but know almost nothing about our toolset or problem domain. Assume they don't know good test design very well.
Announce at start: "I'm using the plan skill to create the implementation plan."
Save plans to: <harness-dir(.claude|.codex)>/plans/YYYY-MM-DD-.md
(User preferences for plan location override this default)
CRITICAL: This skill is research-first, then informed questions. Do exploration before asking questions so you can present trade-offs with clear recommendations. Don't ask questions you can answer through research.
Scope Check
If the spec covers multiple independent subsystems, it should have been broken into sub-project specs during brainstorming. If it wasn't, suggest breaking this into separate plans — one per subsystem. Each plan should produce working, testable software on its own.
Configuration
Learnings Directory: <git-root>/.llm/learnings/
If this directory does not exist in the current project, skip the learnings step entirely. Do NOT read ~/.agentic-eng/config.toml or any other config files to find an alternative path.
Process
ALL steps below are MANDATORY and SEQUENTIAL. Do NOT skip steps. Do NOT proceed to the next step until the current step is fully complete. Each step produces a required output — if a step has no output, the subsequent steps cannot succeed.
Step 1: Prime Context
Load all available context before exploring the codebase. KB and local learnings can be checked in parallel:
- KB (if
kbMCP is available):list_kb_documents→read_kb_document_by_pathfor relevant docs →read_kb_document_by_keywordsfor missing topics (retry up to 3×). Skip if unavailable.
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 · 189 lines · 20 tokens per session scan A ecaabb6a6781
plan is a skill published in the GitHub repository wrannaman/agentic-engineering (2 stars, last pushed 4mo ago), licensed MIT. It adds 20 tokens to every session and 1,992 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.
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