Agent skill

Operate Sqlmap

by cyberful in cyberful/cyberful

Use sqlmap to confirm and characterize suspected SQL injection with faithful requests and bounded evidence.

AGPL-3.0Auto-check passedSecurity

Install Operate Sqlmap

skills CLI
$ npx skills add cyberful/cyberful --skill operate-sqlmap -a claude-code

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

GitHub CLI
$ gh skill install cyberful/cyberful operate-sqlmap --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/cyberful/cyberful.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cyberful/builtin/skills/operate-sqlmap .claude/skills/operate-sqlmap && 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
operate-sqlmap
GitHub stars
134
Token cost
~1.1k tokens
SKILL.md length
453 words
Files
3 (incl. references)
Skills in repo
85
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Use sqlmap to confirm and characterize suspected SQL injection with faithful requests and bounded evidence.

  • Works in 7 steps: Run a low-noise detection pass against… → Pin DBMS only when evidence supports it. → Select techniques from the suspected… → …
  • GraphQL-variable
  • SKILL.md covers Prepare a canonical request, Escalate detection deliberately, Preserve request fidelity and Establish the minimum proof, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Operate Sqlmap is an agent skill from cyberful/cyberful. Use sqlmap to confirm and characterize suspected SQL injection with faithful requests and bounded evidence. Trigger for URL, form, JSON, XML, header, cookie, multipart, GraphQL-variable, authenticated, stored/second-order, or custom request-file injection hypotheses after manual differential evidence or code tracing identifies a plausible SQL dataflow.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `agents/openai.yaml` and `references/sqlmap-fieldbook.md`).

It sits in Security, covering Web application vulnerabilities, GraphQL and SQL. It works with GraphQL and SQL. The repository describes itself as: Cyberful is an open-source AI Red Team for discovering, exploiting, verifying, and remediating vulnerabilities. The licence is AGPL-3.0.

When your agent uses it

  • GraphQL-variable
  • Stored/second-order
  • Custom request-file injection hypotheses after manual differential evidence
  • Code tracing identifies a plausible SQL dataflow

Example prompts

  • “/operate-sqlmap”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Run a low-noise detection pass against one parameter.
  2. Pin DBMS only when evidence supports it.
  3. Select techniques from the suspected query shape: boolean, error, union, stacked, time.
  4. Increase level only for additional parameter locations/headers that matter.
  5. Increase risk only when the generated predicates are appropriate for the operation.
  6. Use explicit prefix/suffix or boundary tuning when source/query evidence shows the syntactic context.
  7. Inspect payload and comparison logs before adding tampers.

What it can do on your machine

Read from SKILL.md and the folder at commit ec598a6. 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

Operate Sqlmap loads about 1.1k tokens when it runs, and up to ~2.1k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 453 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~92
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.1k

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 cyberful/cyberful at commit ec598a6, republished under its AGPL-3.0 licence (© cyberful). 453 words, ~1,097 tokens.

Download SKILL.mdSave it as .claude/skills/operate-sqlmap/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
operate-sqlmap
description
Use sqlmap to confirm and characterize suspected SQL injection with faithful requests and bounded evidence. Trigger for URL, form, JSON, XML, header, cookie, multipart, GraphQL-variable, authenticated, stored/second-order, or custom request-file injection hypotheses after manual differential evidence or code tracing identifies a plausible SQL dataflow.
metadata.domain
security-tooling
metadata.subdomain
sql-injection
metadata.triggers
sqlmap confirmation, suspected SQL injection, second-order SQL injection, authenticated SQL injection test, database injection proof
metadata.tags
sqlmap, SQLi, injection, database, request-replay

Operate SQLMap

Sqlmap is a hypothesis amplifier. Start from a faithful known-good request and preserve the application state, encoding, and parameter occurrence that made the candidate observable.

Prepare a canonical request

Prefer a raw request file when the target uses authentication, JSON/XML/multipart bodies, duplicate parameters, nonstandard methods, CSRF tokens, virtual hosts, or fragile headers. Remove transport artifacts that should be regenerated, but retain semantically required headers and cookies.

Identify:

  • exact injection marker/parameter occurrence;
  • baseline response and stable comparison signal;
  • authentication and tenant context;
  • prerequisite workflow/state;
  • replay safety and idempotency;
  • suspected DBMS/query context from code or errors.

Read references/sqlmap-fieldbook.md for detection tuning, second-order handling, tamper selection, and false-negative analysis.

Escalate detection deliberately

  1. Run a low-noise detection pass against one parameter.
  2. Pin DBMS only when evidence supports it.
  3. Select techniques from the suspected query shape: boolean, error, union, stacked, time.
  4. Increase level only for additional parameter locations/headers that matter.
  5. Increase risk only when the generated predicates are appropriate for the operation.
  6. Use explicit prefix/suffix or boundary tuning when source/query evidence shows the syntactic context.
  7. Inspect payload and comparison logs before adding tampers.

Do not stack multiple tampers speculatively. Each tamper should correspond to an observed canonicalization, filter, parser, or intermediary transformation.

Preserve request fidelity

Control redirects, cookies, CSRF refresh, randomization parameters, null connection, compression, keep-alive, proxying, and safe-frequency checks explicitly. A working browser request can fail in sqlmap because:

  • a token must be refreshed;
  • duplicated keys have precedence;
  • content type selects a different binder;
  • the application signs body bytes;
  • a WAF/session cookie changes the path;
  • redirects cross host or method boundaries;
  • the vulnerable value is consumed only after a prior state transition.

Use traffic capture and verbosity to compare the generated request with the canonical request byte-for-byte where necessary.

Show full SKILL.md (159 more words)Show less

Establish the minimum proof

Detection requires a repeatable true/false, error, union, stacked, or time discriminator tied to the parameter. Characterization may include DBMS family, current database/user, and effective privilege only when needed for impact. Broad enumeration is not a prerequisite for confirmation.

For time-based results, collect multiple controls, randomized order, and latency distribution. One slow response is not confirmation.

For stored/second-order candidates, separate write request, persistence key, trigger request, observation point, and cleanup. Ensure sqlmap is comparing the trigger response, not the storage response.

Reproduce independently

Extract the decisive request pair or minimal sequence from sqlmap logs and replay it manually. A confirmed result links:

controlled input -> query-context discriminator -> repeatable application/DB effect

Report sqlmap's result as supporting evidence, not the entire causal explanation.

Handoff

Preserve raw request template, sqlmap version, exact argv, output/session directory, parameter marker, techniques, payload pair, timing samples, DBMS evidence, application state, rejected hypotheses, and independent replay. Separate confirmed injection from privilege/impact that was not tested.

© cyberful, AGPL-3.0. 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 2 other files (references) in cyberful/builtin/skills/operate-sqlmap of cyberful/cyberful.

  • SKILL.md
  • agents/openai.yaml
  • references/sqlmap-fieldbook.md

Open the folder on GitHubat commit ec598a6

Compare with similar skills

Operate Sqlmap 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.

Operate Sqlmap compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Operate Sqlmap this skillcyberful/cyberful134—~1.1kAutomated safety check: PassAGPL-3.0
Datajunction QueryDataJunction/dj161—~2kAutomated safety check: PassMIT
Pp Jobbermvanhorn/printing-press-library2.1k—~3kAutomated safety check: NotesApache-2.0
Hunt IdorEncod3d-Sec/TORCH3291 repos~2.6kAutomated safety check: PassMIT
Hunt InjectionEncod3d-Sec/TORCH329—~2kAutomated safety check: PassMIT
Go ReviewSpecterOps/skills702—~2.1kAutomated safety check: PassApache-2.0

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Works with

Questions about Operate Sqlmap

What does Operate Sqlmap do?

Use sqlmap to confirm and characterize suspected SQL injection with faithful requests and bounded evidence. Operate Sqlmap is an agent skill from cyberful/cyberful. Use sqlmap to confirm and characterize suspected SQL injection with faithful requests and bounded evidence.

When should I use Operate Sqlmap?

Operate Sqlmap fits situations like: graphQL-variable; stored/second-order; custom request-file injection hypotheses after manual differential evidence; code tracing identifies a plausible SQL dataflow.

How do I install Operate Sqlmap in Claude Code?

Run `npx skills add cyberful/cyberful --skill operate-sqlmap -a claude-code`. Or copy the skill folder (cyberful/builtin/skills/operate-sqlmap in cyberful/cyberful) into .claude/skills/operate-sqlmap in your project. Claude Code loads it when a task matches its description.

How do I install Operate Sqlmap in Codex?

Run `npx skills add cyberful/cyberful --skill operate-sqlmap -a codex`. Or copy the skill folder (cyberful/builtin/skills/operate-sqlmap in cyberful/cyberful) into .agents/skills/operate-sqlmap in your project. Codex loads it when a task matches its description.

Can I use Operate Sqlmap 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 cyberful/cyberful --skill operate-sqlmap -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/operate-sqlmap, .gemini/skills/operate-sqlmap, .github/skills/operate-sqlmap and .opencode/skills/operate-sqlmap in your project.

What does Operate Sqlmap need to run?

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

Does Operate Sqlmap 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 Operate Sqlmap 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 Operate Sqlmap use?

Operate Sqlmap is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Operate Sqlmap use?

About 1.1k tokens (SKILL.md is roughly 4.4k 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 997 tokens, read only when the agent opens those files.

What are the alternatives to Operate Sqlmap?

Skills that share tags, products or a category with Operate Sqlmap: Datajunction Query (DataJunction/dj, 161 stars), Pp Jobber (mvanhorn/printing-press-library, 2.1k stars), Hunt Idor (Encod3d-Sec/TORCH, 329 stars) and Hunt Injection (Encod3d-Sec/TORCH, 329 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Operate Sqlmap?

cyberful (a GitHub organization) maintains it in cyberful/cyberful, which has 134 GitHub stars. The repository holds 85 skills in this directory. The repository was last updated on August 24, 2026.

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