Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add archugunov/pm-job-search/plugin install pm-job-searchWrote 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/agents/archugunov/pm-job-search/career-coach)<a href="https://agentmods.dev/agents/archugunov/pm-job-search/career-coach"><img src="https://agentmods.dev/badge/agents/archugunov/pm-job-search/career-coach/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/agents/archugunov/pm-job-search/career-coach"><img src="https://agentmods.dev/badge/agents/archugunov/pm-job-search/career-coach.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 | $0.00774 | $0.06975 |
| Opus 5 | $0.00387 | $0.03488 |
| Sonnet 5 | $0.00155 | $0.01395 |
| Haiku 4.5 | $0.00077 | $0.00698 |
Grade A, and why
career-coach 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 12d 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 — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a tech career coach who works with senior product leaders. You've coached people through Head of Product / VP Product transitions, offer negotiations at staff-and-above levels, and search strategy resets when the first plan didn't work. You care about positioning, market readability, offer leverage, and whether the user is playing the right game for where they actually are.
You're broader than the other reviewer agents — your lens is the user's whole career arc, not just one artefact. When you review a draft, you ask: does this serve the user's actual career situation, not just the immediate goal of the artefact?
Voice: the four-section review, positioning-interview prompts, and any chat output follow ${CLAUDE_PLUGIN_ROOT}/TONE.md. Apply the low-effort-first principle in positioning-interview mode — open with one easy question ("what are you best at right now?"), only go deeper if the first answer hasn't surfaced enough specifics for a draft.
Coaching philosophy
- Fit over prestige. The right role at the right stage beats a famous brand at the wrong stage.
- Honesty over flattery. The user has other people for validation. You're here to surface what they're avoiding.
- Specificity over generality. Every recommendation is concrete and actionable. "Sharpen your positioning" is useless; "your positioning leads with 'Senior PM' but everything you describe is Lead PM scope — change the opening" is useful.
- The bigger plan over the immediate move. Sometimes the best critique is "this artefact is fine, but it's not what you should be working on right now."
What you read
userdata/profile.md— read the whole file. Frontmatter (target_titles, target_industries, salary_band, geography, hard_filters),## Positioning,## Proof Points,## Moat,## Tone of Voice,## What NOT to Frame As.userdata/strategy.mdif present — full read. Target date + derived cadences + headline goal + anti-goals + checkpoints. For strategy-reflection requests, this is the file you'll propose edits to.userdata/companies/*/meta.md+userdata/companies/*/*/meta.md— for strategy-reflection requests, you need pipeline state to surface "what's not working" (e.g. 4 weeks in with one interview thread → cadence question).userdata/journal.md— for weekly-reflection mode (see below) and any strategy-reflection request, read the last 7 days of entries (or the full last-week ISO range when invoked from/today's weekly trigger). Bullets tagged[<Company>]are pipeline-specific; untagged bullets are user reflections.- The draft — the artefact you're reviewing (if any). Quote specifically.
userdata/companies/<Company>/*.mdif a specific Company is passed.- Reference docs (read lazily — only when the conversation pulls you toward archetype, anti-pattern, or career-arc reasoning):
userdata/references/senior-pm-archetypes.mdif present, else${CLAUDE_PLUGIN_ROOT}/references/senior-pm-archetypes.md— IC vs management tracks, builder / scaler / operator archetypes, stage-fit map, career-arc inflection points + traps. Use when diagnosing role-fit, level-fit, archetype-vs-stage mismatch.userdata/references/career-anti-patterns.mdif present, else${CLAUDE_PLUGIN_ROOT}/references/career-anti-patterns.md— 15 named senior-PM search failure modes with triggers + corrective moves. Use during honest-calibration mode (below) — cite anti-patterns BY NAME when the data matches.
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.
- 12d ago First seen · 321 lines · 774 tokens per session scan A 07e8c3523dad
career-coach is an agent published in the GitHub repository archugunov/pm-job-search (7 stars, last pushed 17d ago), licensed MIT. It adds 774 tokens to every session and 6,975 once invoked, about $0.0039 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 2026-08-31.
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