ctf-methodology
Skill26 starsChanged 31 days agoMITView on GitHub
CTF challenge triage and time management — identify the category and intended technique from the handout artifacts in the first 10 minutes, budget points vs. time, and know when to park a challenge. Load at the start of any CTF/jeopardy challenge, when handed an unknown file, URL, or "nc host port" with no context. Signals: flag{...} / CTF{...} format, challenge tarball, pwn/ web/crypto/rev/forensics/misc categories, "nc ", Docker handout, points value.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- NoorQureshi/roninword for word, first seen Oct 11, 2026
- NoorQureshi/security-skillsword for word, first seen Oct 11, 2026
Other versions2 projects use a version of this.
Word for word · 2
- NoorQureshi/security-skills★ 27100% same
- NoorQureshi/ronin★ 26100% same
Edited · 0
None found.
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository207 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- ad-adcsSkill
- ad-dacl-abuseSkill
- ad-delegation-abuseSkill
- ad-kerberoastingSkill
- ad-pivot-arsenalSkill
- ai-agent-tool-abuseSkill
- ai-insecure-output-handlingSkill
- ai-jailbreakSkill
- ai-llm-dosSkill
- ai-mcp-securitySkill
- ai-model-extractionSkill
- ai-prompt-injectionSkill
- ai-rag-poisoningSkill
- ai-sensitive-data-disclosureSkill
- ai-supply-chainSkill
- ai-system-prompt-leakageSkill
- api-auth-attacksSkill
- api-bolaSkill
- api-fuzzingSkill
- api-graphqlSkill
- api-grpcSkill
- api-mass-assignmentSkill
- api-mongo-agg-facet-bypassSkill
- api-testing-checklistSkill
- api-versioningSkill
- automation-browserSkill
- automation-nuclei-templatesSkill
- automation-recon-pipelineSkill
- cloud-azureSkill
- cloud-container-escapeSkill
- cloud-docker-api-abuseSkill
- cloud-docker-registrySkill
- cloud-gcpSkill
- cloud-iam-privescSkill
- cloud-imds-ssrfSkill
- cloud-kubernetesSkill
- cloud-s3-exposureSkill
- code-review-cicdSkill
- code-review-cppSkill
- code-review-dangerous-sinksSkill
- code-review-dotnetSkill
- code-review-goSkill
- code-review-iacSkill
- code-review-javaSkill
- code-review-methodologySkill
- code-review-nodejsSkill
- code-review-phpSkill
- code-review-pythonSkill
- code-review-rubySkill
- code-review-rustSkill
- code-review-secrets-detectionSkill
- code-review-soliditySkill
- crypto-oracle-attacksSkill
- crypto-rsa-attacksSkill
- ctf-cryptoSkill
- ctf-forensicsSkill
- ctf-pwnSkill
- ctf-revSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 66
Warmup/100-point challenges never require deep tooling: If you're writing a heap exploit for a warmup, you missed something — look for the …
- Distinctive: line 76
Every open challenge has: a category assignment, a loaded playbook, a written list of tried/ruled- out ideas, and an explicit keep/park …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
CTF methodology: triage and time budgeting
When it applies
You're working jeopardy-style CTF challenges (authorized by definition — the organizer hands you the
targets). This skill is the entry point: it classifies the challenge, picks the category playbook
(ctf-web, ctf-pwn, ctf-crypto, ctf-rev, ctf-forensics), and keeps you from sinking three
hours into a 100-point guessy challenge.
Why it works
CTF challenges are designed puzzles with an intended path, and the handout almost always telegraphs it: the category, the files provided, the service type, the description's wordplay. Reading those signals first converts "stare at an unknown blob" into a 2-minute routing decision, and a point/time budget converts sunk-cost grinding into deliberate moves.
Method
- Read the handout like a spec, not a flavor text. Category, title, and description are hints:
puns name the technique ("relationships" → RSA common modulus, "padding" → padding oracle),
an unusual port or protocol is the challenge. Note the flag format (
flag{,CTF{) so you can grep for it later. - Inventory the artifacts (2 minutes). For each provided file:
Archive/disk image →file <f> ; exiftool <f> ; strings -n 6 <f> | head -50 ; checksec --file=<bin>ctf-forensics. ELF/PE/APK with a flag-check →ctf-rev. ELF +nc+ libc →ctf-pwn..py/.sagewith math →ctf-crypto. URL only →ctf-web. - Fingerprint the service.
ncbanner grab; for HTTP,curl -sS -D- <url>and read headers, cookies, comments, and linked assets — server header (Flask/PHP/Express) narrows the bug class immediately (recon-techstack-fingerprinting). - Classify difficulty honestly. Provided source + small binary + common category = intended easy; read the source for the bug before touching tools. No source + large binary + kernel/heap/ custom crypto = intended hard; expect the full chain and budget accordingly.
- Budget by points and momentum. A working heuristic: cap exploration at ~15–20 minutes per untried idea, and re-evaluate a challenge when you've spent more time than its points justify relative to what remains open. Points are worth the same everywhere — three quick 100s beat one stalled 400.
- Move on deliberately, not silently. Park a challenge when: you've enumerated the obvious surface twice with no new information, every hypothesis needs information the challenge doesn't provide (guessy), or a teammate/other challenge offers better points-per-hour. Write down the state (what you tried, what's ruled out, next idea) so re-entry is cheap — solves often come from a fresh look or a later organizer hint.
- Watch for released hints and solve counts. Organizers drop hints when a challenge stalls event-wide; a high solve count means the intended path is simple and you're overthinking — go back to the description and the most obvious reading.
Gotchas
- The category label lies occasionally — "web" challenges that end in SSTI-to-RCE, "crypto" that's really encoding, "misc" that's actually forensics. Re-classify as evidence appears instead of forcing the labeled playbook.
- Warmup/100-point challenges never require deep tooling. If you're writing a heap exploit for a warmup, you missed something — look for the joke, the encoding, the default creds.
- Guessy challenges (stego with no cover text, obscure cipher with no key hint) burn unlimited time. Timebox hard and return after everything tractable is done.
- Dynamic/shared instances — other players can leave artifacts in shared services (uploaded shells, changed passwords); a freshly spawned instance is worth the restart.
- Don't skip the flag format grep —
strings, pcap searches, and memory dumps often yield the flag directly togrep -a 'flag{'before any "real" solve.
Verify success
Every open challenge has: a category assignment, a loaded playbook, a written list of tried/ruled- out ideas, and an explicit keep/park decision — no challenge is being worked by vibes. Scoreboard confirms submitted flags.
References
Routes into ctf-web, ctf-pwn, ctf-crypto, ctf-rev, ctf-forensics; technique depth in the
domain skills they chain to (web-testing-checklist, exploit-pwn-chain, crypto-rsa-attacks,
reverse-eng-binary-triage, defense-dfir-triage).
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

