Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/ledq/resumery/onboard)<a href="https://agentmods.dev/skills/ledq/resumery/onboard"><img src="https://agentmods.dev/badge/skills/ledq/resumery/onboard.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 | $0.00088 | $0.01890 |
| Opus 5 | $0.00044 | $0.00945 |
| Sonnet 5 | $0.00018 | $0.00378 |
| Haiku 4.5 | $0.00009 | $0.00189 |
Grade A, and why
onboard 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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/onboard: build the bank from the user's raw material
Turn a pile of raw sources into the structured bank the tailoring pipeline runs on. Code ingests, an agent structures, and you host the one conversational part: a short interview. The user's answers and their sources are the only places facts come from.
Voice. Describe what is happening to the user's career material in their terms: "I've read your three files", "your two resumes disagree on when you left Acme", "saved, your experience bank is ready". Give a file path where the user acts on it (where their bank lives, where to drop files). One short progress line per stage.
0. Cold-start guard
If bank/experience_bank.md already exists, stop: this command builds a bank where none
exists, and running it against a populated bank would clobber curated work. Tell the user
what exists and ask how they want to proceed (they can move the current bank aside first
if a rebuild is really intended).
1. Resolve the sources
bank/sources/ is the drop box AND the permanent home: everything the user gives us lands
there, forever, unedited. From $ARGUMENTS (and, if empty, the conversation):
- Path arguments (files or a folder) → expand a folder to the supported files inside
it (pdf, md, txt, tex), then:
python3 ops/bank_ingest.py bank <file ...> - No arguments → ingest whatever is already in the drop box:
Exit 4 with nothing ingested → the box is empty: tell the user their options (drop files intopython3 ops/bank_ingest.py bankbank/sources/and rerun, give paths, or paste their resume text right here). - Pasted text (in the argument or earlier in this conversation) → Write it VERBATIM to
bank/sources/pasted-<YYYY-MM-DD>.md(one copy, change nothing), then run the no-arguments form.
Invite more than one source: old resume versions, a CV, notes; the union holds more true material than any single file, and this is the moment to collect it.
Then read bank/.ingest/manifest.json and act on per-source status: low_yield,
not_text, or extract_failed means that source is unusable as text; name the file,
say what happened, and offer the paste route for it. Proceed when at least one source is
usable.
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 · 138 lines · 88 tokens per session scan A 964bc6e7b580
onboard is a skill published in the GitHub repository ledq/resumery (1 stars, last pushed 1mo ago), licensed MIT. It adds 88 tokens to every session and 1,890 once invoked, about $0.0004 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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