job-recap

job-recap is a skill for Claude Code from AkbarDevop/ai-job-agent. It costs 177 tokens per session (3,141 once invoked), scanned A, original, MIT.

A weekly review tool for a job search, covering applications, outreach, interviews, and related work. It reads the job-search records and summarizes what happened during a selected period.

In plain words
What is it for?
Use it to produce weekly numbers, identify the main wins and risks, and choose concrete actions for the following week.
Why use it?
It replaces manually checking several files and repositories to understand progress, problems, and unfinished follow-ups.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: reads .claude/ paths.

Good fit Use it to produce weekly numbers, identify the main wins and risks, and choose concrete actions for the following week.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/akbardevop/ai-job-agent/job-recap
Install

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.

Any agent
npx skills add AkbarDevop/ai-job-agent --skill job-recap
Clone the repo
git clone --depth 1 https://github.com/AkbarDevop/ai-job-agent

Made for: Claude Code.

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.

agentmods badge for job-recap

README.md
[![agentmods](https://agentmods.dev/badge/skills/akbardevop/ai-job-agent/job-recap/github.svg)](https://agentmods.dev/skills/akbardevop/ai-job-agent/job-recap)
Your own site
<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.

agentmods 80×15 button for job-recap

Your own site · 80×15
<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>
Per session 177 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,141 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash 76bbf9fe86d8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

skills/job-recap/SKILL.md · 172 lines

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

  1. Resolve $AI_JOB_AGENT_ROOT (env var → default skills path → REPO_PATH marker → ~/ai-job-agent/).
  2. Parse $ARGUMENTS:
    • empty or 7dstart = today - 7 days
    • 14dstart = today - 14 days
    • 30dstart = today - 30 days
    • since YYYY-MM-DDstart = that date
    • anything else → echo the arg, default to 7d, tell the user
  3. Compute prior_start = start - (today - start) so we can show a delta against the prior window of equal length.
  4. 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):

  1. application-tracker.csv — filter to date ≥ start. Columns: date, company, role, status, location, source, applied_by, url, notes, contact, compensation, days_since, key.
  2. outreach-log.csv — filter to sent_at ≥ start and separately collect rows where last_follow_up_at ≥ start (so a follow-up sent this week to an old contact still counts as activity).
  3. reports/*.md — Glob the directory. Parse YAML frontmatter and filter by evaluated_at ≥ start. Pull out fit_score if present.
  4. interview-prep/*.md — Glob, filter by file mtime ≥ start.
  5. output/*.pdf — Glob, filter by mtime ≥ start. These are CV PDFs from /job-cv and /job-evaluate.
  6. 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.
  7. 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 — if today - mtime > 14d, flag the plan as stale.

Read the full file on GitHub · 172 lines

Changes

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.

  1. 12d ago First seen · 172 lines · 177 tokens per session scan A 76bbf9fe86d8

Subscribe to this mod's changes

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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