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.
npx skills add AkbarDevop/ai-job-agent --skill job-recapgit clone --depth 1 https://github.com/AkbarDevop/ai-job-agentWrote 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/akbardevop/ai-job-agent/job-recap)<a href="https://agentmods.dev/skills/akbardevop/ai-job-agent/job-recap"><img src="https://agentmods.dev/badge/skills/akbardevop/ai-job-agent/job-recap/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/skills/akbardevop/ai-job-agent/job-recap"><img src="https://agentmods.dev/badge/skills/akbardevop/ai-job-agent/job-recap.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 | $0.00177 | $0.03141 |
| Opus 5 | $0.00088 | $0.01571 |
| Sonnet 5 | $0.00035 | $0.00628 |
| Haiku 4.5 | $0.00018 | $0.00314 |
Grade A, and why
job-recap 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Job Recap
Weekly retrospective for the job search. Reads every data source the pipeline writes to, slices it by the requested time range, and produces a single Friday-evening recap: numbers, wins, risks, and 3 concrete moves for next week.
Where /job-dashboard answers "what does the pipeline look like right now" and /job-patterns answers "why is the pipeline shaped that way", /job-recap answers "what actually happened in the last 7 days, and what should I do next week."
Repo location
$AI_JOB_AGENT_ROOT → ~/.claude/skills/ai-job-agent/ → REPO_PATH marker file → ~/ai-job-agent/.
Status emoji (consistent with /job-track and /job-patterns)
📄 applied · 📬 submitted · 💼 interview · 🎯 offer · ❌ rejected · 🚫 blocked · 🚪 withdrawn
Workflow
Step 1 — Resolve repo + parse the time range
- Resolve
$AI_JOB_AGENT_ROOT(env var → default skills path → REPO_PATH marker →~/ai-job-agent/). - Parse
$ARGUMENTS:- empty or
7d→start = today - 7 days 14d→start = today - 14 days30d→start = today - 30 dayssince YYYY-MM-DD→start = that date- anything else → echo the arg, default to 7d, tell the user
- empty or
- Compute
prior_start = start - (today - start)so we can show a delta against the prior window of equal length. - Print one line: "Recap window:
<start>→<today>(N days). Prior window for delta:<prior_start>→<start>."
Step 2 — Read all the data sources
Read in parallel; treat each missing/empty source as 0 (don't fabricate):
application-tracker.csv— filter todate ≥ start. Columns:date, company, role, status, location, source, applied_by, url, notes, contact, compensation, days_since, key.outreach-log.csv— filter tosent_at ≥ startand separately collect rows wherelast_follow_up_at ≥ start(so a follow-up sent this week to an old contact still counts as activity).reports/*.md— Glob the directory. Parse YAML frontmatter and filter byevaluated_at ≥ start. Pull outfit_scoreif present.interview-prep/*.md— Glob, filter by file mtime ≥ start.output/*.pdf— Glob, filter by mtime ≥ start. These are CV PDFs from/job-cvand/job-evaluate.- Git log — try
git -C $AI_JOB_AGENT_ROOT log --since="$start" --oneline. If the repo is not a git repo (fatal: not a git repository), treat the result as empty and don't error. config/search-plan.md— if it exists, read the file and pull the "Log" section's entries within range. Also note the file's mtime — iftoday - mtime > 14d, flag the plan as stale.
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 · 172 lines · 177 tokens per session scan A 76bbf9fe86d8
job-recap is a skill published in the GitHub repository AkbarDevop/ai-job-agent (54 stars, last pushed 4mo ago), licensed MIT. It adds 177 tokens to every session and 3,141 once invoked, about $0.0009 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 2026-08-30.
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