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 aksheyw/career-command-center-template/plugin install career-ccWrote 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/skills/aksheyw/career-command-center-template/track-application)<a href="https://agentmods.dev/skills/aksheyw/career-command-center-template/track-application"><img src="https://agentmods.dev/badge/skills/aksheyw/career-command-center-template/track-application.svg" alt="Measured on agentmods" 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 | $0.00035 | $0.01574 |
| Opus 5 | $0.00017 | $0.00787 |
| Sonnet 5 | $0.00007 | $0.00315 |
| Haiku 4.5 | $0.00003 | $0.00157 |
Grade A, and why
track-application 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are managing the user's job application pipeline.
STEP 1: Read current memory
Read ${CLAUDE_PLUGIN_ROOT}/references/CUSTOMIZATION_MEMORY.md to understand current patterns and what has been learned. This is the user's real (git-ignored) file. If it does not exist yet, seed it by copying ${CLAUDE_PLUGIN_ROOT}/references/CUSTOMIZATION_MEMORY.example.md to that path, then proceed.
STEP 2: Determine the action
Based on the user's input:
If logging a new application:
- Extract: company name, role title, date applied, source (referral / direct / recruiter)
- Determine company type (AI / Consumer / Enterprise / Fintech / Telco)
- Confirm: was the resume and cover letter generated using this plugin?
- Ask: any customizations made that worked well?
If updating an existing application:
- Extract: company, new status, outcome notes
- If rejected: what was the stated reason? What can we learn?
- If progressing: what round, who is interviewing?
If checking pipeline:
- Summarize all open applications by status
- Flag any applications needing follow-up (no response after 7 days)
- Show conversion rates by company type
STEP 3: Update CUSTOMIZATION_MEMORY.md
Read the current CUSTOMIZATION_MEMORY.md, then propose specific additions to the "Successful Patterns" or "Unsuccessful Patterns" sections based on the outcome.
Write the updated file back to: ${CLAUDE_PLUGIN_ROOT}/references/CUSTOMIZATION_MEMORY.md
Format additions as:
#### [Company Type] - [Company Name] - [Outcome]
Date: [date]
Resume strategy: [what was emphasized]
Cover letter hook: [what opener was used]
Result: [screen / reject / offer / pending]
Learning: [what this tells us for future applications]
STEP 4: Persist the structured tracker (machine-readable — required)
CUSTOMIZATION_MEMORY.md holds the prose learnings. It is NOT queryable, so it cannot drive the patterns (rejection analysis) or followup (cadence) skills. Those skills read a structured tracker instead. Maintain it here.
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 · 110 lines · 35 tokens per session scan A 1d372a123de1
track-application is a skill published in the GitHub repository aksheyw/career-command-center-template (1 stars, last pushed 9d ago), licensed MIT. It adds 35 tokens to every session and 1,574 once invoked, about $0.0002 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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