Borrowing it
Nothing to install: this file belongs to irsdl/webhacklist. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/irsdl/webhacklist/master/.claude/skills/webseclist-collect-year/SKILL.mdgit clone --depth 1 https://github.com/irsdl/webhacklistWrote 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/irsdl/webhacklist/webseclist-collect-year)<a href="https://agentmods.dev/skills/irsdl/webhacklist/webseclist-collect-year"><img src="https://agentmods.dev/badge/skills/irsdl/webhacklist/webseclist-collect-year/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/irsdl/webhacklist/webseclist-collect-year"><img src="https://agentmods.dev/badge/skills/irsdl/webhacklist/webseclist-collect-year.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 | $0.00195 | $0.03409 |
| Opus 5.5 | $0.00078 | $0.01364 |
| Sonnet 5.5 | $0.00039 | $0.00682 |
| Haiku 4.5 | $0.00019 | $0.00341 |
Grade A, and why
webseclist-collect-year scanned grade A with 1 finding 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 15d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
Any file saved to disk while probing — a page fetched with `curl -o` to How it starts
The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Source security
Before handling third-party material, read and follow the shared source-security policy. It governs sandboxed reading/conversion, trusted-policy reviews and validation before any source-derived action. These requirements apply to this entire workflow, including retries, imports and bulk work; no unsafe host fallback is permitted.
Collect a year's web hacking techniques
Related sources
When researching, adding, updating or reviewing a reference, apply the shared research-story and related-source workflow. Inspect companion papers, series parts, slides, recordings, code, substantive analysis and reproductions. Maintain their relationships and separate credits; keep link-only media accessible. Preserve existing nominations and capture each written source separately. Removing an entry also requires checking its group and shared sources before pruning.
You are building <YEAR>-ai.md: a machine-assembled, unranked, explicitly
provisional collection of candidate research published in one calendar year.
It sits beside the hand-curated year lists and never replaces them.
Keep judging marks out of the published Markdown and website: no scores,
scorecards, score cutoffs or verdict labels, including in watchlists and exclusion
notes. They can put pressure on researchers and judges. Keep detailed scoring in
gitignored .local/ai-evaluation/. Public
ai-evaluation/ records contain only candidate identity, links and Added / Not
added, with dates and merit-revision identifiers in decision history. Publish titles, source links, author credits and factual
notes about dates, scope, source availability or prior research.
For submissions, follow the judging skill's Submission replies and
reconsideration and linked templates before responding to or closing an issue or PR.
Two failure modes make a run worthless, and both are easy to fall into:
- Citing the wrong thing. A news article about research is not the research. A roundup, a vendor advisory with no analysis, or a CVE record is not a technique writeup. The deliverable is the original source.
- Re-collecting what is already there. The curated list for the year, and any previous run of this skill, already hold links. Re-adding them inflates the file and hides the genuinely new finds. This is what the exclusion step exists to prevent — do not skip it because the year "looks empty".
What ships with it
3 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.
- 15d ago Changed 93e664d16db4
- 1mo ago Changed · +21 lines 2d87ec401ec7
- 1mo ago Changed · +2 lines 8941f3763f79
- 1mo ago Changed · +2 lines d13fb3191116
- 1mo ago Changed · -6 lines 3d71bb1a5b05
- 1mo ago Changed e0ff35bcc370
- 1mo ago First seen · 263 lines · 195 tokens per session scan A 9b20c3eacc3e
webseclist-collect-year is a skill published in the GitHub repository irsdl/webhacklist (907 stars, last pushed 5d ago), licensed MIT. It adds 195 tokens to every session and 3,409 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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