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 megamen32/LastHumanCommit --skill worker-researchgit clone --depth 1 https://github.com/megamen32/LastHumanCommitWrote 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/megamen32/lasthumancommit/worker-research)<a href="https://agentmods.dev/skills/megamen32/lasthumancommit/worker-research"><img src="https://agentmods.dev/badge/skills/megamen32/lasthumancommit/worker-research/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/megamen32/lasthumancommit/worker-research"><img src="https://agentmods.dev/badge/skills/megamen32/lasthumancommit/worker-research.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 | $0.00106 | $0.01278 |
| Opus 5.5 | $0.00042 | $0.00511 |
| Sonnet 5.5 | $0.00021 | $0.00256 |
| Haiku 4.5 | $0.00011 | $0.00128 |
Grade A, and why
worker-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Worker Research
Find the shortest verified route to the next business proof. Do not map the whole repository.
Tool order
-
Search existing reusable knowledge before rediscovering it:
python3 <this-skill-directory>/scripts/code_map.py \ --root "$PWD" search <business-noun> <symbol>Resolve
scripts/code_map.pyfrom this skill directory. Treat every hit as a lead: runcheck, then confirm the decisive location with one targetedrgor source read. -
Use
rg --files, thenrg -n -Cas the default fresh search. Trace from the real consumer inward. Search exact endpoint names, commands, config keys, symbols, and user-visible strings before broad concepts. -
Use context-mode for large files, logs, test output, or three or more related searches. Ask it focused questions and return only derived evidence. It is a context-saving processor and index, not the durable source of truth.
-
Use an existing Graphify graph when the decision depends on three or more components, indirect callers, ownership, or cross-language flow. Verify every decisive graph edge against current source with
rg. Do not build or refresh a graph for a simple symbol lookup. -
Stop when Lead has the production path, owning locations, first blocker, cheapest patch route, proof, and decision-relevant unknowns.
In practice: rg is the fastest and most authoritative locator; Graphify is
useful orientation for multi-hop structure but can be stale or over-broad;
context-mode is highly effective for preserving context on large output but
does not by itself prevent future rediscovery.
Bugfix route
For a defect, preserve this order in the research receipt:
telemetry -> reproduction -> smallest failing test -> root cause -> patch -> regression.
- Use telemetry to locate the failing boundary; do not infer the fix from a stack trace, alert, or log alone.
- Reproduce the same failure through the real consumer path with the smallest deterministic probe available.
- Add or specify the smallest failing test that proves the accepted behavior, not an implementation detail.
- Patch only the verified root cause when mutation is authorized.
- Re-run the failing proof, proportional regression checks, and the cheapest claim-matching business canary.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 118 lines · 106 tokens per session scan A 9a10d76afd3d
worker-research is a skill published in the GitHub repository megamen32/LastHumanCommit (2 stars, last pushed yesterday), licensed MIT. It adds 106 tokens to every session and 1,278 once invoked, about $0.0004 per session on Opus 5.5. 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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