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/pip-responder)<a href="https://agentmods.dev/rules/mohitagw15856/pm-claude-skills/pip-responder"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/pip-responder/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/pip-responder"><img src="https://agentmods.dev/badge/rules/mohitagw15856/pm-claude-skills/pip-responder.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 | $0.00116 | $0.01145 |
| Opus 5 | $0.00058 | $0.00573 |
| Sonnet 5 | $0.00023 | $0.00229 |
| Haiku 4.5 | $0.00012 | $0.00114 |
Grade A, and why
pip-responder 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 7d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PIP Responder Skill
A PIP is sometimes a genuine turnaround offer and often paperwork for a decision already made — and the response strategy is nearly identical either way: perform visibly against the letter of the plan, document everything, and run a job search in parallel starting now. The mistake is choosing between hope and search; survivors of PIPs did both, because the search costs nothing if the turnaround works and everything if skipped. This skill builds that two-track plan without pretending to know which ending this one has.
What This Skill Produces
- The honest read — signals this PIP is recoverable vs. scripted, stated as probabilities not verdicts
- The two-track plan — meeting the PIP's letter visibly, and the search timeline mapped against the PIP clock
- The documentation system — what to save, where (never only on employer systems), from day one
- The templates — the measured written acknowledgment, weekly check-in updates, and the achievements file
Required Inputs
Ask for these if not provided:
- The PIP document — goals, metrics, duration, review cadence, and whether the goals are things a human can actually do
- The backstory — surprise or long-signaled? relationship with manager? recent org context (new manager, layoffs-by-another-name season)?
- Their honest self-assessment — is the criticism partly fair? (changes the perform-track, not the search-track)
- Financial runway and constraints — visa status especially, which changes the timeline math entirely and needs an immigration-aware plan
Framework: The Two-Track Rules
- Read the metrics for winnability: measurable goals a person could hit in the window = possibly genuine; vague goals ("improve communication"), moving targets, or metrics needing others' cooperation = likely scripted. Either way, both tracks run.
- Respond in writing, temperature zero: acknowledge professionally, ask clarifying questions that pin vague goals to measurable definitions ("so we agree success on #2 looks like X by [date]?"), correct factual errors flatly without adjectives. Never sign anything that says "I agree with this assessment" without noting disagreement is allowed — and that a lawyer exists for exactly this review.
- Make the perform-track visible: hitting goals silently doesn't count. Weekly written updates against each PIP item, sent to the manager, saved externally — they're simultaneously your best shot at surviving and your evidence file.
- The search-track starts today, quietly: update materials this week; the PIP clock (30/60/90 days) is the search deadline. Interviews are easier to explain from employed-and-searching than from terminated.
- Know the endgame options: some PIPs come with (or can be negotiated into) an exit package as an alternative — leaving on agreed terms with references intact is a legitimate win. Severance-agreement review belongs to a lawyer.
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.
- 7d ago First seen · 70 lines · 116 tokens per session scan A fb67ed0676a8
pip-responder is a cursor rule published in the GitHub repository mohitagw15856/pm-claude-skills (1,352 stars, last pushed 3d ago), licensed MIT. It adds 116 tokens to every session and 1,145 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.
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