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/kimgoetzke/coding-agent-configs/fetch-plannpx skills add kimgoetzke/coding-agent-configs --skill fetch-plangit clone --depth 1 https://github.com/kimgoetzke/coding-agent-configsWrote 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/kimgoetzke/coding-agent-configs/fetch-plan)<a href="https://agentmods.dev/skills/kimgoetzke/coding-agent-configs/fetch-plan"><img src="https://agentmods.dev/badge/skills/kimgoetzke/coding-agent-configs/fetch-plan.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 | $0.00049 | $0.00779 |
| Opus 5 | $0.00024 | $0.00390 |
| Sonnet 5 | $0.00010 | $0.00156 |
| Haiku 4.5 | $0.00005 | $0.00078 |
Grade A, and why
fetch-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 4d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fetch a Saved Plan
Use this skill to reopen an existing plan created with the planning-mode/planning skill's folder structure.
Storage layout
- Plans live under
{repo root}/.ai/planning/ - Each plan folder is named
{yyyy-mm-dd} {task name} - A selected plan may contain:
plan.mdprogress.mdfindings.mdquestions.md
Phase 1: List plans only
- Find
{repo root}/.ai/planning/- If you don't find anything, try running
ls "{repo root}/.ai/planning/"via Bash to list the plan folders since Glob fails to match directories on Windows
- If you don't find anything, try running
- List plan folders only; do not read any files inside them yet
- Sort folders newest first
- Show the folder names exactly as stored, in a numbered list
Example:
2026-03-20 refactor auth flow2026-03-18 add retry metrics
If no folders exist, tell the user no saved plans were found and suggest creating one with the planning-mode or planning skill.
Phase 2: Ask the user to choose
Prompt the user to reply with either:
- the list number, or
- the folder name
Selection rules:
- If the input is a number, resolve it against the numbered list
- If the input is text, try exact folder-name match first
- If no exact match exists, allow a single unambiguous case-insensitive partial match
- If the input is invalid or ambiguous, explain why and ask again
- Do not read any plan documents before a valid selection is resolved
Phase 3: Load the selected plan
After the user selects a plan, read the available planning documents from that folder.
Read in this order:
plan.mdprogress.md(if it exists)findings.mdquestions.md- Any other files in the folder, if relevant
Only read files that exist. If plan.md is missing, say the plan is incomplete and continue with any other available docs.
Phase 4: Report implementation status
Summarise the plan's implementation status using only the loaded docs.
Base the status on:
plan.mdphase statuses and current phaseprogress.mdlatest actions and test results (only for multi-phase plans)findings.mdblockers, issues, and decisionsquestions.mdunresolved questions or missing user answers
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.
- 4d ago First seen · 96 lines · 49 tokens per session scan A 33270510dba9
fetch-plan is a skill published in the GitHub repository kimgoetzke/coding-agent-configs (2 stars, last pushed 14d ago), licensed MIT. It adds 49 tokens to every session and 779 once invoked, about $0.0002 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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