Best fit
- Scanning a repository for exploitable vulnerabilities
- Preparing a Huntr, HackerOne, or similar bounty submission
- Triage where the question is "does this actually pay?" rather than "is this theoretically unsafe?"
affaan-m/ECC
Hunt for exploitable, bounty-worthy security issues in repositories. Focuses on remotely reachable vulnerabilities that qualify for real reports instead of noisy local-only findings.
npx skills add https://github.com/affaan-m/ECC --skill "skills/security-bounty-hunter"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
According to the pinned SKILL.md from affaan-m/ECC: Use this when the goal is practical vulnerability discovery for responsible disclosure or bounty submission, not a broad best-practices review.
npx skills add https://github.com/affaan-m/ECC --skill "skills/security-bounty-hunter"Best fit
Bring this context
Expected outputs
Key source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
1. Check scope first: program rules, SECURITY.md, disclosure channel, and exclusions. 2. Find real entrypoints: HTTP handlers, uploads, background jobs, webhooks, parsers, and integration endpoints. 3. Run static tooling where it helps, but treat it as triage input only. 4. Read…
Scanning a repository for exploitable vulnerabilities
Bias toward remotely reachable, user-controlled attack paths and throw away patterns that platforms routinely reject as informative or out of scope.
These are the kinds of issues that consistently matter:
These are usually low-signal or out of bounty scope unless the program says otherwise:
SkillSignal prompt templates
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Task-start prompt
Confirm source fit, inputs, and outputs before acting.
Use security-bounty-hunter to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.
Source-guided execution
Make the Agent explicitly follow the key extracted sections.
Apply the pinned security-bounty-hunter source to [task]. Pay particular attention to these source sections: “Workflow”, “When to Use”, “How It Works”, “In-Scope Patterns”, “Skip These”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].
Result-review prompt
Check omissions, permissions, and source drift before delivery.
Review the current security-bounty-hunter result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.
Output checklist
The task matches the purpose documented in the SKILL.md.
The source section “Workflow” has been checked.
The source section “When to Use” has been checked.
The source section “How It Works” has been checked.
The source section “In-Scope Patterns” has been checked.
Inputs, constraints, and acceptance criteria are explicit.
Unverified facts, compatibility, and outcome claims are clearly marked.
Any file, command, network, or data action has been reviewed.
Choose a different workflow
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
A separate implementation from event4u-app/agent-config; compare its source, maintenance signals, and permission requirements.
Open source detailDistributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailUse NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
A separate implementation from K-Dense-AI/scientific-agent-skills; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
Use this when the goal is practical vulnerability discovery for responsible disclosure or bounty submission, not a broad best-practices review.
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "skills/security-bounty-hunter". Inspect the command and pinned source before running it.
No dedicated Agent platform is declared in the pinned source record.
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
Use when the user says "review the design", "check the UI", or wants a comprehensive UI/UX review. Uses a 7-phase methodology covering interaction, responsiveness, accessibility, and more.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Use NeuroKit2 to build or audit reproducible research workflows for physiological time-series preprocessing, event/interval analysis, multimodal alignment, variability, and complexity. Trigger when code imports neurokit2 or needs its current APIs, schemas, and method-aware validation—not for diagnosis or device validation.
Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite this prompt", or explicitly asks to enhance prompt quality. Also triggers on Chinese equivalents: "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令". DO NOT TRIGGER when: user wants th
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, visualization, and converting R-friendly single-cell formats such as Seurat or SingleCellExperiment RDS files into h5ad for Scanpy. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.
Use this when the goal is practical vulnerability discovery for responsible disclosure or bounty submission, not a broad best-practices review.
Bias toward remotely reachable, user-controlled attack paths and throw away patterns that platforms routinely reject as informative or out of scope.
These are the kinds of issues that consistently matter:
| Pattern | CWE | Typical impact |
|---|---|---|
| SSRF through user-controlled URLs | CWE-918 | internal network access, cloud metadata theft |
| Auth bypass in middleware or API guards | CWE-287 | unauthorized account or data access |
| Remote deserialization or upload-to-RCE paths | CWE-502 | code execution |
| SQL injection in reachable endpoints | CWE-89 | data exfiltration, auth bypass, data destruction |
| Command injection in request handlers | CWE-78 | code execution |
| Path traversal in file-serving paths | CWE-22 | arbitrary file read or write |
| Auto-triggered XSS | CWE-79 | session theft, admin compromise |
These are usually low-signal or out of bounty scope unless the program says otherwise:
pickle.loads, torch.load, or equivalent with no remote patheval() or exec() in CLI-only toolingshell=True on fully hardcoded commandssemgrep --config=auto --severity=ERROR --severity=WARNING --json
Then manually filter:
## Description
[What the vulnerability is and why it matters]
## Vulnerable Code
[File path, line range, and a small snippet]
## Proof of Concept
[Minimal working request or script]
## Impact
[What the attacker can achieve]
## Affected Version
[Version, commit, or deployment target tested]
Before submitting: