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/update-memory)<a href="https://agentmods.dev/skills/aksheyw/career-command-center-template/update-memory"><img src="https://agentmods.dev/badge/skills/aksheyw/career-command-center-template/update-memory.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 | $0.00032 | $0.00448 |
| Opus 5 | $0.00016 | $0.00224 |
| Sonnet 5 | $0.00006 | $0.00090 |
| Haiku 4.5 | $0.00003 | $0.00045 |
Grade A, and why
update-memory 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 5d 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
You are updating the customization memory — the learning log that makes every future application smarter.
STEP 1: Read the current memory file
Read ${CLAUDE_PLUGIN_ROOT}/references/CUSTOMIZATION_MEMORY.md fully (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 first. Understand the existing patterns before adding anything.
STEP 2: Gather information
Ask the user (or extract from their input):
- Which company and role?
- What was the outcome? (Resume screen passed / rejected / screened / interviewed / offered)
- What resume strategy was used? (Which company-type template?)
- What was the cover letter hook?
- Was there a referral involved?
- For rejections: any feedback or signal about why?
- For successes: what specific framing or metric seemed to resonate?
STEP 3: Identify the learning
Based on the outcome, generate a specific, actionable learning:
- NOT "good resume" — say WHAT specifically seemed to work
- NOT "bad fit" — say which framing missed the mark
- Connect to company-type patterns already in the file
STEP 4: Update the file
Write the updated CUSTOMIZATION_MEMORY.md to ${CLAUDE_PLUGIN_ROOT}/references/CUSTOMIZATION_MEMORY.md.
Add the new entry under the correct company-type section. Update:
- "Successful Customizations" if outcome was positive
- "Unsuccessful Patterns" if outcome was negative or no response
- "Screen Rate" with the updated percentage if calculable
STEP 5: Surface patterns
After updating, analyze across all logged applications:
- Which company types are yielding screens?
- Which cover letter hooks got the most traction?
- Which resume strategies need improvement?
- Are there any keywords or framings that appear in multiple successful applications?
Output a brief "What We Know So Far" summary with 3-5 actionable insights.
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.
- 5d ago First seen · 52 lines · 32 tokens per session scan A f2aafcedc091
update-memory is a skill published in the GitHub repository aksheyw/career-command-center-template (1 stars, last pushed 7d ago), licensed MIT. It adds 32 tokens to every session and 448 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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