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/strelov1/freehire-cliWrote 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/strelov1/freehire-cli/market-fit)<a href="https://agentmods.dev/commands/strelov1/freehire-cli/market-fit"><img src="https://agentmods.dev/badge/commands/strelov1/freehire-cli/market-fit/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/strelov1/freehire-cli/market-fit"><img src="https://agentmods.dev/badge/commands/strelov1/freehire-cli/market-fit.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 | $0.00013 | $0.00298 |
| Opus 5 | $0.00006 | $0.00149 |
| Sonnet 5 | $0.00003 | $0.00060 |
| Haiku 4.5 | $0.00001 | $0.00030 |
Grade A, and why
market-fit 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 9d 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
Measure market fit for: $ARGUMENTS
Follow the freehire-market-fit skill. In short:
- If no skills were named, take them from
freehire --json profile | jq '.skills'. - Ground every slug against
freehire --json facets— canonical, lowercase. A slug the market does not use scores nothing rather than erroring, so an ungrounded run quietly under-reports. - Run
freehire --json market-fit --skills <set>with the facet flags that define the role they are aiming at (--category,--seniority,--region,--remote, …). Without a filter you are measuring the whole market, which is rarely the question. - Report: the coverage percent, how many vacancies were in scope, which must-have skills they hold, and the gaps.
State what was measured. Coverage means "this share of the filtered vacancies name at least one of these skills" — it is not a pass rate and not an employability score.
Give every gap its number ("adding Kubernetes reaches another 1,240 vacancies under this filter"). A gap without a number is an opinion.
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.
- 9d ago First seen · 28 lines · 13 tokens per session scan A 2d2ba31892bd
market-fit is a command published in the GitHub repository strelov1/freehire-cli (5 stars, last pushed 2d ago), licensed MIT. It adds 13 tokens to every session and 298 once invoked, about $0.0001 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.
Other commands, from other repositories
resume
Generate a tailored resume and cover letter from a job description, score both, create DOCX files, and update the tracker.
resume-team
Run the role-separated, fail-closed Resume Team workflow against a job description.
writing-coach
Human-voice writing coach — rewrite resumes and cover letters with brevity, burstiness, plain language, and authentic impact. Blocks AI-sounding prose.
find-jobs
Search live job boards for roles that match the master resume, then rank them by fit.
cover-letter
Create a one-page cover letter for a job description and generate the final DOCX.
job-fit
Run the deterministic, digest-bound candidate-fit gate before any resume tailoring.