Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add kalpmodi/akira --skill plan-engagementgit clone --depth 1 https://github.com/kalpmodi/akiraWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kalpmodi/akira/plan-engagement)<a href="https://agentmods.dev/skills/kalpmodi/akira/plan-engagement"><img src="https://agentmods.dev/badge/skills/kalpmodi/akira/plan-engagement/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kalpmodi/akira/plan-engagement"><img src="https://agentmods.dev/badge/skills/kalpmodi/akira/plan-engagement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.04947 |
| Opus 5 | $0.00037 | $0.02474 |
| Sonnet 5 | $0.00015 | $0.00989 |
| Haiku 4.5 | $0.00007 | $0.00495 |
Grade B, and why
plan-engagement scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Cloud metadata endpointmediumServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
→ add 169.254.169.254 to SSRF target list Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 505 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plan Engagement — Attack Graph Engine
Philosophy
You are not filling out a form. You are an attack planner initializing a live intelligence system.
Extract everything from the user's message. Ask at most one question — only if the target itself is completely missing. Everything else has smart defaults. Your job is to generate a dynamic attack graph, not a static checklist, and hand the user a running start.
Step 0: Bootstrap from Cross-Engagement Memory
Before anything else, read ~/.akira/memory.json to seed this engagement with prior intelligence.
MEMORY=~/.akira/memory.json
# Read global SCL counter for this session
SCL_COUNTER=$(jq -r '.scl_id_counter // 1' $MEMORY 2>/dev/null || echo 1)
# Read ATW flagged techniques (hallucination guard)
FLAGGED=$(jq -r '.hallucination_guard | to_entries[] | select(.value.flagged==true) | .key' $MEMORY 2>/dev/null)
# Read DNA registry for pattern matching
DNA_REGISTRY=$(jq -r '.dna_registry' $MEMORY 2>/dev/null)
echo "Memory loaded. SCL counter: $SCL_COUNTER | Flagged techniques: $(echo $FLAGGED | wc -w)"
If ~/.akira/memory.json does not exist:
mkdir -p ~/.akira
# Write initial memory.json with defaults - see references/memory-schema.md
# Set scl_id_counter to 1
Use memory to calibrate hypothesis priors in Step 2:
- Match extracted tech hints against
tech_vuln_priorskeys - For each match, use stored
prior_probabilityas the hypothesis starting point - Do NOT exceed 0.85 even if memory says higher (anti-overfitting cap)
- Write
flagged_techniquesfromhallucination_guardintosession.json scalpel.doom_loop
Step 1: Infer From the Message
Extract without asking:
| Extract | Source | Default if missing |
|---|---|---|
| Target | domain, IP, URL, app name in message | ask — only this |
| Program type | "bug bounty", "pentest", "CTF", "my lab" | bug_bounty |
| Platform | "HackerOne", "Bugcrowd", "Intigriti" | unknown |
| Tech hints | AWS, OAuth, GraphQL, checkout, LDAP | none |
| Constraint hints | "no active scan", "stealth", "out of scope" | none |
| Attack surface hints | "403s", "login page", "API", "internal" | none |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 505 lines · 74 tokens per session scan B 18bf90fbcfad
plan-engagement is a skill published in the GitHub repository kalpmodi/akira (21 stars, last pushed 1mo ago), licensed MIT. It adds 74 tokens to every session and 4,947 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (cloud metadata endpoint). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
hunt-llm-ai
Hunt LLM/AI feature bugs — prompt injection, indirect injection, exfiltration viatool-use/markdown, ASCII smuggling, agentic AI security (OWASP Agentic Apps 2026, ASI01-ASI10). Patterns: direct injection ('ignore previous instructions'), indirect injection via documents/web pages/email the model reads, ASCII smuggling…
performing-aws-privilege-escalation-assessment
Performing authorized privilege escalation assessments in AWS environments to identify IAM misconfigurations that allow users or roles to elevate their permissions using Pacu, CloudFox, Principal Mapper, and manual IAM policy analysis techniques.
performing-cloud-penetration-testing-with-pacu
Performing authorized AWS penetration testing using Pacu, the open-source AWS exploitation framework, to enumerate IAM configurations, discover privilege escalation paths, test credential harvesting, and validate security controls through systematic attack simulation.
conducting-cloud-penetration-testing
This skill outlines methodologies for performing authorized penetration testing against AWS, Azure, and GCP cloud environments. It covers understanding the shared responsibility model for testing scope, leveraging cloud-specific attack tools like Pacu and ScoutSuite, exploiting IAM misconfigurations, testing for SSRF…
performing-kubernetes-penetration-testing
Kubernetes penetration testing systematically evaluates cluster security by simulating attacker techniques against the API server, kubelet, etcd, pods, RBAC, network policies, and secrets. Using tools.
conducting-full-scope-red-team-engagement
Plan and execute a comprehensive red team engagement covering reconnaissance through post-exploitation using MITRE ATT&CK-aligned TTPs to evaluate an organization's detection and response capabilities.