name	program-intel
description	Summarize the historical bug patterns for a specific bug bounty program/team using the local disclosed-report corpus. Use when the user names a program (e.g. shopify, gitlab, nextcloud, nodejs, uber, tiktok) and wants to know what's been found there, recurring weak spots, hot endpoints/assets, and what to avoid duplicating. Helps focus hunting and avoid dupes.

program-intel

Profile a program from its disclosed reports so the user knows where to look and what's already been reported.

Resolve the handle. Programs are identified by Handle in each report. List what's available / find the closest handle:
```
grep -h "^- \*\*Handle\*\*:" reports/*.md | sort | uniq -c | sort -rn | grep -i "<name>"
```
High-coverage handles include: deptofdefense, ibb, nextcloud, security, shopify, nodejs-ecosystem, gitlab, curl, x, vkcom, uber, shopify-scripts, tiktok, brave.
Pull the program's reports:
```
python3 .claude/skills/_lib/search_reports.py "<broad term or *>" --handle <handle> --limit 60
```
(Or to enumerate all of a program's reports, grep filenames after finding their IDs.) Read the highest-severity / most-disclosed ones in full.
Synthesize an intel brief:
- Volume & severity mix — how many reports, breakdown by severity.
- Recurring vuln classes — what keeps getting found (rank by frequency).
- Hot assets / endpoints / features — subdomains, APIs, app areas that repeatedly show up.
- Notable chains — high-impact reports worth studying, with #<id>.
- Where to hunt next — under-tested adjacent surface, variations of past bugs, classes that appear elsewhere but not yet here.
- Dupe risk — patterns already well-covered (likely to be dupes now).

Cite report IDs and reports/<file> for every claim.
Distinguish "frequently found here" (mature, dupe-prone) from "found on similar programs but not here" (opportunity).
Note the date range of the reports — old patterns may be patched.

name	program-intel
description	Summarize the historical bug patterns for a specific bug bounty program/team using the local disclosed-report corpus. Use when the user names a program (e.g. shopify, gitlab, nextcloud, nodejs, uber, tiktok) and wants to know what's been found there, recurring weak spots, hot endpoints/assets, and what to avoid duplicating. Helps focus hunting and avoid dupes.

Profile a program from its disclosed reports so the user knows where to look and what's already been reported.

Resolve the handle. Programs are identified by Handle in each report. List what's available / find the closest handle:
```
grep -h "^- \*\*Handle\*\*:" reports/*.md | sort | uniq -c | sort -rn | grep -i "<name>"
```
High-coverage handles include: deptofdefense, ibb, nextcloud, security, shopify, nodejs-ecosystem, gitlab, curl, x, vkcom, uber, shopify-scripts, tiktok, brave.
Pull the program's reports:
```
python3 .claude/skills/_lib/search_reports.py "<broad term or *>" --handle <handle> --limit 60
```
(Or to enumerate all of a program's reports, grep filenames after finding their IDs.) Read the highest-severity / most-disclosed ones in full.
Synthesize an intel brief:
- Volume & severity mix — how many reports, breakdown by severity.
- Recurring vuln classes — what keeps getting found (rank by frequency).
- Hot assets / endpoints / features — subdomains, APIs, app areas that repeatedly show up.
- Notable chains — high-impact reports worth studying, with #<id>.
- Where to hunt next — under-tested adjacent surface, variations of past bugs, classes that appear elsewhere but not yet here.
- Dupe risk — patterns already well-covered (likely to be dupes now).

Cite report IDs and reports/<file> for every claim.
Distinguish "frequently found here" (mature, dupe-prone) from "found on similar programs but not here" (opportunity).
Note the date range of the reports — old patterns may be patched.

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