Borrowing it
Nothing to install: this file belongs to sflandergan/agentic-coding. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sflandergan/agentic-coding/main/.agents/skills/agent-planning/SKILL.mdgit clone --depth 1 https://github.com/sflandergan/agentic-codingWrote 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/sflandergan/agentic-coding/agent-planning)<a href="https://agentmods.dev/skills/sflandergan/agentic-coding/agent-planning"><img src="https://agentmods.dev/badge/skills/sflandergan/agentic-coding/agent-planning/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/sflandergan/agentic-coding/agent-planning"><img src="https://agentmods.dev/badge/skills/sflandergan/agentic-coding/agent-planning.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 | $0.00025 | $0.00731 |
| Opus 5.5 | $0.00010 | $0.00292 |
| Sonnet 5.5 | $0.00005 | $0.00146 |
| Haiku 4.5 | $0.00003 | $0.00073 |
Grade A, and why
agent-planning 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 2mo 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Planning — Self-Maintenance
Write lightweight implementation plans for this toolkit repo. There is no application code — the maintenance surface is markdown templates and bash scripts.
Announce at start: "I'm using the agent-planning skill to create the implementation plan."
Save plans to: plans/YYYY-MM-DD-<feature-name>/plan.md, next to the spec.
Scope Check
If the spec covers multiple independent concerns, suggest splitting into separate plans.
File Mapping
Before defining tasks, map out which files will be created or modified. For each file:
- What it is responsible for
- Whether a
stacks/*overlay also needs updating (core+stacks dual-maintenance) - Whether
README.md, this repo'sskills-lock.json,core/skills-lock.json, or the dot-mapping table need updates for their distinct scopes
Task Granularity
Each task is one focused commit. Steps within a task are small concrete actions:
- "Write the file" — step
- "Verify syntax" — step
- "Commit" — step
Verification should match the changed files: shellcheck and bash -n for bash scripts, smoke runs for script behavior changes, codespell for prose-heavy changes, and consistency checks for README, lockfiles, symlinks, and dot-mapping.
Spellcheck Planning
When a task creates or modifies markdown templates, agent files, skill files, README content, or user-facing script text, include a codespell verification step for the changed files. Keep it scoped to the files touched by the task unless the plan intentionally adds or updates repo-wide spelling policy.
Plan Document Header
# [Feature Name] Implementation Plan
> **For implementation agents:** Execute this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
**Goal:** [One sentence]
**Configuration shape:** [2-3 sentences]
**Configuration surface:** [Key files, tools, and validation commands]
Task Structure
### Task N: [Component Name]
**Files:**
- Create: `exact/path/to/file`
- Modify: `exact/path/to/existing`
- [ ] **Step 1: [Action]**
[Content — exact paths, complete file content or commands]
- [ ] **Step 2: Verify**
Run: `shellcheck scripts/foo.sh`
Run: `codespell exact/path/to/file.md`
Expected: no errors
- [ ] **Step 3: Commit**
```bash
git add path/to/file
git commit -m "chore: description"
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.
- 2mo ago First seen · 99 lines · 25 tokens per session scan A d208d1e04820
agent-planning is a skill published in the GitHub repository sflandergan/agentic-coding (4 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 731 once invoked, about $0.0001 per session on Opus 5.5. 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 skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…