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/commands/mohitagw15856/pm-claude-skills/adopt-ai-properly)<a href="https://agentmods.dev/commands/mohitagw15856/pm-claude-skills/adopt-ai-properly"><img src="https://agentmods.dev/badge/commands/mohitagw15856/pm-claude-skills/adopt-ai-properly/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/commands/mohitagw15856/pm-claude-skills/adopt-ai-properly"><img src="https://agentmods.dev/badge/commands/mohitagw15856/pm-claude-skills/adopt-ai-properly.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 | $0.00020 | $0.00422 |
| Opus 5 | $0.00010 | $0.00211 |
| Sonnet 5 | $0.00004 | $0.00084 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
adopt-ai-properly 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 13d 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.
What it actually says
Run the Adopt AI Properly workflow recipe for: $ARGUMENTS
This is a chain of skills. Run each stage in order and carry every stage's output forward as context for the next. Open with a one-line plan of the 4 stages, then ask once for essential missing inputs (regulatory exposure, current tool spend, which roles AI has changed most, the review framework in use). Don't re-ask between stages.
Run each stage under a clear ## Stage N — <name> heading:
- Set the rules — apply the
ai-usage-policyskill: the one-page policy with the data traffic-light (grounded in this org's real data classes), approved tools, disclosure lines, and the decision log for counsel. - Redesign the roles — apply the
role-redesign-for-aiskill to the role(s) AI changed most: the before/after task inventory with verification counted as work, capacity deliberately reallocated, and the junior-ladder answer — consistent with the policy from stage 1. - Fix the reviews — apply the
ai-assisted-performance-reviewskill: criteria that measure judgment, verification, outcomes, and leverage; calibration rules for uneven adoption; the three hard-case scripts — aligned to the charters from stage 2. - Prove what paid — apply the
ai-roi-auditskill across the tool spend: per-tool verdicts with the measurement method behind each number, the hidden-cost ledger, and baseline plans for the unknowns.
Close with a leadership one-pager: the policy headline, the role changes, the new review frame, and the renewal decisions — the packet that turns "we should figure out AI" into four signed-off documents.
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.
- 13d ago First seen · 18 lines · 20 tokens per session scan A 214e22eebcf3
adopt-ai-properly is a command published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 422 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 2026-08-30.
Other commands, from other repositories
copy-user
Copies a user's Chili Piper workspace and team memberships (and, optionally, product licenses) to another existing user — for onboarding onto an existing territory or replacing a departing rep.
check-availability
Checks why a rep or team is showing no available slots — diagnoses calendar connectivity, working hours, meeting limits, and distribution membership to find the specific blocker.
replay
Summarize one Agent Monitor session by id — header plus a concise transcript recap.
discover
Run a structured discovery flow from problem framing through opportunity mapping and validation planning.
manage-scheduling-links
Manages scheduling links (round-robin, admin one-on-one, group, ownership) — list, create, update, delete — with a dry-run plan and confirmation before any write.
checklist
Generate a custom checklist for the current feature based on user requirements.