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 magnus919/agent-skills --skill implementation-planninggit clone --depth 1 https://github.com/magnus919/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/magnus919/agent-skills/implementation-planning)<a href="https://agentmods.dev/skills/magnus919/agent-skills/implementation-planning"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/implementation-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/magnus919/agent-skills/implementation-planning"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/implementation-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00127 | $0.02625 |
| Opus 5 | $0.00063 | $0.01313 |
| Sonnet 5 | $0.00025 | $0.00525 |
| Haiku 4.5 | $0.00013 | $0.00263 |
Grade A, and why
implementation-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 6d 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 — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Planning
Turn an approved requirement or specification into an executable delivery plan. This skill is a planning discipline — it produces a plan, not code, not a spec, and not a lifecycle orchestration.
When to load this skill
Load when the input is an approved requirement, specification, or decision and the task is to produce a concrete delivery plan that accounts for dependencies, sequencing, risk, and verification.
| Trigger | Example |
|---|---|
| An approved SPEC.md or product brief needs a delivery plan | "Plan the implementation for the payments checkout spec" |
| A cross-team or multi-repo feature needs work coordination | "Plan the rollout for the identity-migration change across three services" |
| A data migration needs a staged execution plan | "Plan the schema migration with rollback stages" |
| A risky or high-stakes change needs a rollout strategy | "Plan the staged rollout for the auth-provider replacement" |
| Multiple workstreams need dependency mapping and critical-path analysis | "Map dependencies and critical path for the platform upgrade" |
When not to use
- Pre-approval discovery or needs-finding — the input is not yet an approved requirement. Route to product-discovery.
- Authoring a specification from scratch — no approved spec exists yet. Route to spec-driven-development.
- Coding, implementation, or architecture design — the plan is done, now execute. Route to backend-engineering, frontend-engineering, or software-architecture-analysis.
- The neckbeard issue-to-PR delivery flow — this skill plans work, it does not execute the neckbeard lifecycle gates, delivery-packet sequencing, or phase orchestration. Delivery execution is a separate concern.
- The prerequisite decision is not approved — if the requirement or specification has not been approved, stop. Planning unapproved work is an explicit stop condition. Record the missing approval and escalate; do not produce a plan.
- The whole run needs a control-plane protocol — intent contracts, work classification, autonomy gating, review-as-triage, and failure routing around the plan and its execution. Route to bmad. This skill produces a delivery plan; bmad owns the protocol that runs intent-to-delivery work end to end.
What ships with it
6 files 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.
- 6d ago Changed · +8 lines f953b07857dd
- 9d ago First seen · 209 lines · 127 tokens per session scan A b6e6e49e761b
implementation-planning is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed yesterday), licensed MIT. It adds 127 tokens to every session and 2,625 once invoked, about $0.0006 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 2026-09-03.
Other skills, from other repositories
cocoharvest
Decompose an approved plan into parallel workstreams, assign specialist personas, classify stages as HITL or AFK (CocoLens), generate flow.json stages with checkpoints and dual-file state, and create per-stage prompt files. Includes adaptive parallelism, stall detection, shell identity injection, and consecutive…
plan
Enter the Plan phase of CocoBrew. Runs CocoSpec quality gate pre-flight, reads spec.md and discuss.md (if present), invokes Coco native plan mode as a mandatory gate, captures the approved plan to plan.md, creates initial flow.json template, and commits. Must have $spec completed first.
cocoplus-config
CocoPlus configuration SSOT — $cocoplus sync propagates cocoplus.toml into downstream artifacts; $cocoplus migrate-config converts legacy safety-config.json. Invoked via $cocoplus sync and $cocoplus migrate-config.
ops-demo
CocoOps demo mode activator — populates .cocoplus/ops/demo/ with realistic mock data and sets cocoplus.toml [demo] enabled = true. Invoked via $ops demo.
ops-dora
CocoOps DORA metrics — computes four DORA-adapted delivery metrics from Snowflake task history and git log. Extends the longitudinal delivery thesis through the skill-native CocoOps thesis workflow. Invoked via $ops dora.
ops-sprint
CocoOps sprint health — computes story velocity, burndown, and completion prediction from git log and sprint window config. Invoked via $ops sprint.