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/land-a-job)<a href="https://agentmods.dev/commands/mohitagw15856/pm-claude-skills/land-a-job"><img src="https://agentmods.dev/badge/commands/mohitagw15856/pm-claude-skills/land-a-job/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/land-a-job"><img src="https://agentmods.dev/badge/commands/mohitagw15856/pm-claude-skills/land-a-job.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 | $0.00000 | $0.00421 |
| Opus 5 | $0.00000 | $0.00211 |
| Sonnet 5 | $0.00000 | $0.00084 |
| Haiku 4.5 | $0.00000 | $0.00042 |
Grade A, and why
land-a-job 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 Land a Job 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 — that shared context is the whole point. Open with a one-line plan of the 4 stages, then ask once for any essential missing inputs (the job description, the company, and the candidate's CV / background). Don't re-ask between stages.
Run each stage under a clear ## Stage N — <name> heading:
- Decode the role — apply the
jd-decoderskill to the job description: the real must-haves vs. nice-to-haves, hidden priorities, an honest fit assessment, and the exact phrases to mirror. - Research the company — apply the
company-briefskill to build a candidate's brief: how they make money, their trajectory, and the challenges this role would touch. - Tailor the application — apply the
job-applicationskill, using the decode + company brief, to produce an ATS-tailored CV summary and a cover letter aligned to what this employer actually wants. - Prep the interview — apply the
interview-prepskill to build a prep pack tailored to this role and round: likely questions, STAR answers from the candidate's background, a story bank, and sharp questions to ask.
Do not invent the candidate's experience, the company's facts, or metrics — work with what's given and mark assumptions. After the last stage, end with a 4-bullet "What you now have" recap linking each artifact to the stage that produced it, and a one-line "next step" (e.g. send it, or follow up).
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 · 34 tokens per session scan A c94ba1379c1d
land-a-job is a command published in the GitHub repository mohitagw15856/pm-claude-skills (1,357 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 421 tokens. 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.