Agent skill

Pre Bounty

by forefy in forefy/.context

Map a bug-bounty scope and rank targets by payout, crowding, and freshness.

MITAuto-check passedSecurity

Install Pre Bounty

skills CLI
$ npx skills add forefy/.context --skill pre-bounty -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install forefy/.context pre-bounty --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/forefy/.context.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/hunter-utils/pre-bounty .claude/skills/pre-bounty && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
pre-bounty
GitHub stars
152
Token cost
~2.1k tokens
SKILL.md length
1,195 words
Files
4 (incl. references)
Skills in repo
20
Repo updated
First seen
Licence
MIT

At a glance

Map a bug-bounty scope and rank targets by payout, crowding, and freshness.

  • Works in 5 steps: Gather the scope (the signal-richest step) → Mine bug history → Extract the boundary gotchas → …
  • Size up a program
  • SKILL.md covers The core thesis, Inputs this skill accepts, Workflow and Output spec, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Pre Bounty is an agent skill from forefy/.context. Map a bug-bounty scope and rank targets by payout, crowding, and freshness. Use to size up a program or decide where to hunt before committing time.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/sankey.md`, `references/scoring.md` and `references/sourcing.md`).

It sits in Security, covering Bug bounty. The repository describes itself as: AI Agent Skills, Goals and Dynamic Workflows for Security Auditing, Pentesting and Research. The licence is MIT.

When your agent uses it

  • Size up a program
  • Decide where to hunt before committing time

Example prompts

  • “/pre-bounty”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Gather the scope (the signal-richest step)
  2. Mine bug history
  3. Extract the boundary gotchas
  4. Score & rank every asset
  5. Deliver

What it can do on your machine

Read from SKILL.md and the folder at commit c8ff161. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Pre Bounty loads about 2.1k tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 40 tokens; SKILL.md has 1,195 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from forefy/.context at commit c8ff161, republished under its MIT licence (© forefy). 1,195 words, ~2,104 tokens.

Download SKILL.mdSave it as .claude/skills/pre-bounty/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
pre-bounty
description
Map a bug-bounty scope and rank targets by payout, crowding, and freshness. Use to size up a program or decide where to hunt before committing time.

Pre-bounty: scope recon & target prioritization

The core thesis

Most hunters converge on whatever is cheapest to start testing. That means the crowd is an artifact of the setup barrier, not of where the bugs are. So the edge is systematic: rank the scope by

opportunity ≈ (payout ceiling × freshness) ÷ crowd, with setup difficulty acting as a moat - a hard-to-reproduce environment keeps competitors out, so it is a positive when paired with a high ceiling.

The whole skill exists to compute that ranking from real program data and show it in a way the user can act on. A high max-payout asset that is trivial to set up and already swept (lots of resolved reports) is a worse target than a modest one nobody has tooled up for. Make that legible.

Inputs this skill accepts

Any of, in order of preference:

  • A program URL - hackerone.com/<program>, bugcrowd.com/<program>, app.intigriti.com/..., yeswehack.com/..., or a self-hosted /security / security.txt / VDP page.
  • A pasted scope table or asset list (domains, mobile apps, repos, APIs).
  • A rough description of API/repo access the user already has.

If you only get a program name, construct the URL. If the platform page is JavaScript-rendered (HackerOne, Bugcrowd, Intigriti all are), use the browser tools to read it - WebFetch returns an empty shell for these. read_page / get_page_text on the policy and scope tabs is the reliable path.

Workflow

Work the five stages in order. Stages 1–3 are parallelizable - fire the fetches and searches together.

1. Gather the scope (the signal-richest step)

Pull and record, per asset:

  • Asset name, type (domain / mobile / desktop / API / source / other) and whether it is in or out of scope.
  • Payout tier - most programs tag assets into tiers (HackerOne Core/Non-core; Bugcrowd P1–P5 targets; others "critical eligible" vs not). Capture the max reward reachable per asset - this is the ceiling.
  • Resolved-report count / share per asset if the platform shows it. This is your single best crowd proxy - an asset with 40% of all resolved reports is picked-over; one with <2% is open. HackerOne shows this on the scope table; Bugcrowd/Intigriti show submission stats less granularly (note when missing).
  • Last-updated date of each scope entry → freshness. Recently added or rescoped assets have had fewer eyes.
  • Program-wide reward table (per-severity bounty ranges + averages) and the severity mix of resolved reports if shown.

See references/sourcing.md for exactly where each platform surfaces these.

2. Mine bug history
  • Check the program's hacktivity / disclosed reports. Many programs disclose nothing publicly - say so plainly and fall back to the crowd proxy.
  • Web-search public CVEs and researcher writeups for the target's products. The recurring bug class tells you what actually lands (e.g. trust-boundary RCE, config-precedence, deep-link/IPC on native clients). Record specific CVE IDs where found.
  • Note remediation / dedup signals - if the policy says a bug class is under a wholesale fix, reports there will close as duplicates. Flag those assets as saturated regardless of payout.
3. Extract the boundary gotchas

The fine print is where hunters waste days. Capture:

  • Adjacent in/out pairs - cases where a near-identical bug is in-scope on one asset and out on another (e.g. first-party MCP in-scope vs OSS MCP out; first-party connector in vs third-party-in-directory out).
  • Excluded vulnerability classes (DoS, clickjacking on non-sensitive pages, missing cookie flags, dependency confusion, self-XSS, rate-limiting, etc.).
  • Platform-standard deviations and payout nerfs - especially "one bounty per systemic issue" and discretionary PII-leak severity, which kill the "report many instances" strategy.
  • Testing constraints (e.g. leaked-key "authenticate then immediately deauth only", required X-HackerOne-Handle header / researcher email).

Render this as a compact in ✅ / out ❌ table - see the output spec.

4. Score & rank every asset

Apply the rubric in references/scoring.md. For each asset produce: { setup tier + time estimate, crowd, payout ceiling, freshness, ROI verdict, opportunity score 0–100 }, then sort best→worst. Verdict buckets: Prime · Good · Recon · Skip · Dead. Be honest that the two highest-ceiling assets are often near the bottom because the crowd is already there - surfacing that inversion is the point.

5. Deliver

Produce the artifacts below. Lead with the ranking; it is what the user asked for even when they phrased it as "analyze the scope".

Show full SKILL.md (524 more words)Show less

Output spec

The output is tables + the two widgets. Prose is connective tissue, not the product. Put every fact that fits in a cell in a cell - never restate in a paragraph what a table or widget already shows. See "Prose discipline" below for the hard budget; violating it is the most common failure of this skill.

Deliver, in this order:

  1. Program header - one line. bounty since · total paid · paid/90d · resolved · response-efficiency · # in-scope assets, then a single sentence on how hunted the program is. No more.
  2. Boundary gotchas - a table. The in ✅ / out ❌ adjacent pairs as a two-column table. Follow with at most 3–4 bullets for the strategy-killing nerfs (systemic-dedup, testing constraints, saturated classes) - one line each, no sub-bullets, no paragraphs.
  3. Severity economics - a table. Columns: severity · share of resolved · avg bounty · range, one row per severity. Then one italic sentence: "where the crits actually land", grounded in the CVE corpus - noting crit rates are usually ~1% and the money is repeatable Highs.
  4. Ranked list widget, best→worst - rank, verdict chip, payout ceiling, setup time, crowd %, freshness, and a one-line "why" per asset. Render with the ranked-list widget in references/sankey.md. The widget carries the per-asset detail; do not narrate the list row by row afterward.
  5. Ranked Sankey widget - the three-stage asset → replication setup → ROI verdict flow, assets ordered best (top) → worst (bottom), thread width = report volume, labels carrying payout ceiling + freshness. Fill the one data array in the references/sankey.md template - do not hand-roll new diagram code. Render via the visualize show_widget tool (call its read_me once first, as that tool requires).
  6. Verdict - a tight closer under both widgets. Only what the tables can't say: name the top 1–3 picks (one clause each on why now), state the inversion in one sentence (which high-ceiling assets sit at the bottom and why), and flag any technique-development target. Cap this whole section at ~120 words.
Prose discipline (the point of this update)
  • Tables and widgets are load-bearing; prose only connects them. If a sentence repeats a number or verdict already in a cell/chip, cut it.
  • Budget: header = 1 line; each table gets ≤1 lead-in line and ≤1 follow-up line; the closing verdict ≤120 words. No section is a paragraph block.
  • No row-by-row narration of the ranked list - that is the widget's job.
  • One sources line at the very end (CVE/writeup links), not inline essays.
  • When in doubt, move the sentence into a table cell or delete it.

Notes on judgment

  • Estimate, but flag estimates. Setup-time and opportunity scores are your synthesis; the payout ceiling, crowd counts, and dates are hard data from the program. Keep the two visibly separate so the user can trust the spine.
  • Scale to the ask. "Quick take on this program" → the ranked list is enough. "Full workup" → all four artifacts plus the CVE history.
  • Stay in authorized-recon lane. This skill reads public program data and public vulnerability history to prioritize; it does not test, exploit, or probe live targets. Reproduction/testing happens later, under program rules.
  • This skill pairs with a reporting skill for the next phase (filing a found bug). Don't try to do both; hand off.

© forefy, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in skills/hunter-utils/pre-bounty of forefy/.context.

  • SKILL.md
  • references/sankey.md
  • references/scoring.md
  • references/sourcing.md

Open the folder on GitHubat commit c8ff161

Compare with similar skills

Pre Bounty next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Pre Bounty compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Pre Bounty this skillforefy/.context152—~2.1kAutomated safety check: PassMIT
Web3 Smart Contract Auditawarexone/Agentic-Bug-Hunter5.3k3 repos~4.5kAutomated safety check: PassMIT
Bug Bounty Hunting Methodologyawarexone/Agentic-Bug-Hunter5.3k2 repos~4.7kAutomated safety check: PassMIT
Metabigor OSINT Reconj3ssie/metabigor1.8k—~2.4kAutomated safety check: PassMIT
Wooyun Legacytanweai/wooyun-legacy1.8k—~1.9kAutomated safety check: PassCustom licence
Client Request Signature Reversalawarexone/Agentic-Bug-Hunter5.3k—~4.7kAutomated safety check: PassMIT

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Categories

Questions about Pre Bounty

What does Pre Bounty do?

Map a bug-bounty scope and rank targets by payout, crowding, and freshness. context. Map a bug-bounty scope and rank targets by payout, crowding, and freshness.

When should I use Pre Bounty?

Pre Bounty fits situations like: size up a program; decide where to hunt before committing time.

How do I install Pre Bounty in Claude Code?

Run `npx skills add forefy/.context --skill pre-bounty -a claude-code`. Or copy the skill folder (skills/hunter-utils/pre-bounty in forefy/.context) into .claude/skills/pre-bounty in your project. Claude Code loads it when a task matches its description.

How do I install Pre Bounty in Codex?

Run `npx skills add forefy/.context --skill pre-bounty -a codex`. Or copy the skill folder (skills/hunter-utils/pre-bounty in forefy/.context) into .agents/skills/pre-bounty in your project. Codex loads it when a task matches its description.

Can I use Pre Bounty in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add forefy/.context --skill pre-bounty -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pre-bounty, .gemini/skills/pre-bounty, .github/skills/pre-bounty and .opencode/skills/pre-bounty in your project.

What does Pre Bounty need to run?

SKILL.md names no scripts, command-line tools or credentials: Pre Bounty is instructions for the agent only.

Does Pre Bounty access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Pre Bounty safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Pre Bounty use?

Pre Bounty is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Pre Bounty use?

About 2.1k tokens (SKILL.md is roughly 8.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.2k tokens, read only when the agent opens those files.

What are the alternatives to Pre Bounty?

Skills that share tags, products or a category with Pre Bounty: Web3 Smart Contract Audit (awarexone/Agentic-Bug-Hunter, 5.3k stars), Bug Bounty Hunting Methodology (awarexone/Agentic-Bug-Hunter, 5.3k stars), Metabigor OSINT Recon (j3ssie/metabigor, 1.8k stars) and Wooyun Legacy (tanweai/wooyun-legacy, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Pre Bounty?

forefy (a GitHub user) maintains it in forefy/.context, which has 152 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on October 4, 2026.

Source: forefy/.context on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.