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 yeaight7/agent-powerups --skill writing-plansgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/writing-plans)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/writing-plans"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/writing-plans/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/yeaight7/agent-powerups/writing-plans"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/writing-plans.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 | $0.00022 | $0.00884 |
| Opus 5 | $0.00011 | $0.00442 |
| Sonnet 5 | $0.00004 | $0.00177 |
| Haiku 4.5 | $0.00002 | $0.00088 |
Grade A, and why
writing-plans 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 yesterday.
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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Turn a spec into a detailed, executable multi-step implementation plan. Prevents implementation drift and gives any engineer — or agent — enough detail to build without guessing.
When to Use
- Before implementing any multi-subsystem or non-trivial feature.
- When a spec or requirements document exists and needs to become actionable tasks.
- When the implementation requires coordinating multiple files or components.
If the spec covers multiple independent subsystems, break it into separate plans — one per subsystem. Each plan should produce working, testable software on its own.
Inputs
- Spec or requirements document.
- Access to the codebase (for file paths and existing patterns).
Workflow
-
Scope check — Identify all affected subsystems. If multiple independent subsystems, suggest separate plans.
-
Design file structure — Before defining tasks, map which files will be created or modified and what each is responsible for. Units should have clear boundaries, focused responsibility, and well-defined interfaces.
-
Write the plan header:
# [Feature Name] Implementation Plan **Goal:** [One sentence describing what this builds] **Architecture:** [2-3 sentences about approach] **Tech Stack:** [Key technologies/libraries] --- -
Break work into bite-sized tasks — Each step should take 2–5 minutes:
- "Write the failing test" — step
- "Run it to verify it fails" — step
- "Implement the minimal code to pass the test" — step
- "Run tests and confirm pass" — step
- "Commit" — step
-
Write each task using this structure:
### Task N: [Component Name] **Files:** - Create: `exact/path/to/file.py` - Modify: `exact/path/to/existing.py` - Test: `tests/exact/path/to/test.py` - [ ] **Step 1:** [Action] ```code # Actual code here ``` - [ ] **Step 2:** Run: `<exact command>` Expected: `<exact output>` -
No placeholders — These are plan failures; never write them:
TBD / TODO / implement later / fill in details "Add appropriate error handling" / "handle edge cases" "Write tests for the above" (without actual test code) "Similar to Task N" (repeat the code — engineer may read tasks out of order) Steps that describe without showing how (code blocks required for code steps)
What ships with it
1 file 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.
- yesterday First seen · 106 lines · 22 tokens per session scan A 239e17a73a14
writing-plans is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 2d ago), licensed Apache-2.0. It adds 22 tokens to every session and 884 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 2026-09-14.
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Use when a pipeline conductor session is being seeded, or when inspecting/debugging one. Operating procedure for the kirocrew-pipeline-conductor agent - run one issue/PR pipeline on one repository as a supervised fleet. Auto-pick items, preflight every candidate to one deterministic claim verdict, stand up one worker…
babysit
Use when a user asks to babysit, monitor, keep checking, keep an eye on, or report when a pull request, CI run, ticket, deployment, or other changing target reaches an outcome.
goal-loop
Bootstrap a goal-driven self-improving AutoNudge loop from a goal plus an anchor directory, then run the board autonomously until the Definition of Done is met.
goal-ledger-conductor
Deprecated alias of the goal-conductor skill, removed next release. The work-ledger conducting procedure - decompose a goal into items, bind each to a session, read reported status as data, verify claims with the acceptance evaluator - now lives in goal-conductor. Read that skill instead of this one.