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 agentmods add commands/andrewaws26/claude-works/setupgit clone --depth 1 https://github.com/andrewaws26/claude-worksWhat 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 | $0.00017 | $0.01251 |
| Opus 5 | $0.00009 | $0.00626 |
| Sonnet 5 | $0.00003 | $0.00250 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
setup 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 2d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are onboarding a new candidate onto this job-application system. When you are done, the system must run for THEM: their policy, their search lanes, their resume claims, their identity. Never leave them running on the repo's default (someone else's) policy. Work autonomously; only stop to ask for the inputs listed below and for confirmations that involve their personal facts.
1. Collect the two inputs
Ask the user for:
- Example jobs they want (3-10 job posting URLs, or titles + companies). ATS URLs (Ashby/Greenhouse/Lever) are best; anything works.
- Their resume (a file path, pasted text, or a LinkedIn export). Also ask for: name, email, phone, location, LinkedIn/GitHub/website URLs, whether they are authorized to work in the US and need sponsorship, their base compensation floor, any companies they must not apply to (current employer, active interview processes), and any domains they refuse (defense, gambling, whatever they name).
2. Install
pip install -e . # from this clone; or: pip install claude-works
ruff check . && mypy && pytest # confirm the install is healthy (all green)
mkdir -p ~/jobsearch-data
3. Derive the personalization (from their examples, not the defaults)
Fetch each example job's JD (fetch_job_description or WebFetch). From the
JDs + resume, derive and write:
~/jobsearch-data/policy.json(start fromexamples/policy.sample.json):core_signals: the stack terms that appear in BOTH their resume and the example JDs (their daily work).edge_signals: rare differentiators from their resume (domain experience, unusual skill pairings) that few candidates share.lane_points: the example jobs' title patterns, strongest first.overlevel_terms/level_ok_signals: from their seniority.hard_gap_skills: required-skill terms they lack (ask them to confirm).excluded_companies/excluded_domains/comp_floor: from step 1.seed_boards: the example jobs' org slugs plus 10-20 similar companies (same space, size, stack) that use Ashby/Greenhouse/Lever.
~/jobsearch-data/SEARCH_ANGLES.md(format perexamples/SEARCH_ANGLES.md): 2-4 lanes distilled from the example jobs; mark the strongest(PRIMARY / default lane).~/jobsearch-data/resumes/_genlib.py: copyexamples/resumes/_genlib.pyand replace ROLES + every fragment with entries built from THEIR resume. Each fragment must be a claim the user confirms is true; read the list back to them before writing it. Never invent, inflate, or extrapolate a claim. Copyexamples/resumes/_render.shalongside it (it uses Chrome when available).
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.
- 2d ago First seen · 105 lines · 17 tokens per session scan A 26bf6107836d
setup is a command published in the GitHub repository andrewaws26/claude-works (0 stars, last pushed 2d ago), licensed MIT. It adds 17 tokens to every session and 1,251 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 2026-09-01.
Other commands, from other repositories
minutes-ideas
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autobot-result
Print the most recent result.md for an autobot session, so the user can see how a bot is doing without attaching to its tmux session.
engage.actions
Execute Phase 7 - Actions on Objectives and Goal Achievement.
version
Show installed vs latest ai-sdlc plugin version. Bypasses the 24h SessionStart cache.
thread
Manage conversation threads (create, switch, update, delete, show).
handoff
Generate or load a session handoff. Usage: /handoff [create|resume].