PM Skills is a collection of plain-Markdown instructions that teach AI assistants structured methods for handling professional, personal, and life-admin tasks. People use it with Claude, ChatGPT, Gemini, Cursor, Codex, and other supported agents for work such as writing product requirements, reviewing documents, or planning difficult situations.
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.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-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/rules/mohitagw15856/pm-claude-skills/model-migration-plan)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/model-migration-plan"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/model-migration-plan/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/rules/mohitagw15856/pm-claude-skills/model-migration-plan"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/model-migration-plan.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 | $0.00094 | $0.01219 |
| Opus 5 | $0.00047 | $0.00609 |
| Sonnet 5 | $0.00019 | $0.00244 |
| Haiku 4.5 | $0.00009 | $0.00122 |
Grade A, and why
model-migration-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 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Migration Plan Skill
A model swap changes every output of your feature at once. This skill plans the migration like the risky deploy it is: eval first, shadow second, canary third — with numbers, not vibes, deciding each promotion.
What This Skill Produces
- A phased migration plan (eval → shadow → canary → full) with promotion criteria per phase
- Prompt adaptation notes — what typically shifts between models and what to re-tune
- Rollback triggers and the mechanics of rolling back fast
- A cost/latency delta forecast for the new model
Required Inputs
Ask for (if not already provided):
- Current and target model (and why: deprecation, quality, cost, latency)
- The feature's traffic and blast radius — requests/day, who sees the output, what a bad output costs
- Existing evals — a regression suite (see
prompt-regression-suite) or at minimum golden examples; if none exist, phase 0 is building one - The deadline, if the migration is forced by a deprecation date
Migration Phases
Phase 0 — Baseline. Freeze a regression suite against the current model. Without a baseline, "the new model is fine" is unfalsifiable. Record current cost, latency (p50/p95), and quality scores.
Phase 1 — Offline eval. Run the suite against the target model with the prompt as-is, then with adapted prompts. Promotion criteria: pass rate ≥ baseline, no canary failures, cost/latency within budget. Expect to iterate here — most "model regressions" are prompt-fit issues.
Phase 2 — Shadow. Mirror a sample of real traffic to the new model; log, never serve. Compare distributions: refusal rate, output length, format-violation rate, judge scores on a sample. Duration: long enough to cover weekly traffic patterns.
Phase 3 — Canary. Serve the new model to [1-5]% of traffic behind a flag, tagged in analytics. Watch the same metrics plus user-visible signals (regenerate rate, thumbs-down, support tickets). Widen in steps; each step has the same promotion criteria.
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 First seen · 82 lines · 94 tokens per session scan A d48a323dbeab
model-migration-plan is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,345 stars, last pushed 2d ago), licensed MIT. It adds 94 tokens to every session and 1,219 once invoked, about $0.0005 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.
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