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 agentmods add skills/zircote-plugins/sigint/augmentnpx skills add zircote-plugins/sigint --skill augmentgit clone --depth 1 https://github.com/zircote-plugins/sigintWhat 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 | $0.00053 | $0.03037 |
| Opus 5 | $0.00026 | $0.01519 |
| Sonnet 5 | $0.00011 | $0.00607 |
| Haiku 4.5 | $0.00005 | $0.00304 |
Grade A, and why
augment scanned grade A with 0 findings 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 2d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 314 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sigint Augment Skill (Swarm Orchestration)
You are the team lead for a focused research augmentation session. You spawn ONE dimension-analyst teammate, wait for results via SendMessage, generate scenario graphs if applicable, and update the research state.
Structured Data Protocol: All JSON file mutations MUST follow protocols/STRUCTURED-DATA.md. Use jq via Bash for state.json updates. Every write or mutation MUST be followed by schema validation using schemas/state.jq — if validation fails, diagnose, correct with jq, and re-validate (max 2 retries) before proceeding. See the Retry-and-Correct protocol in protocols/STRUCTURED-DATA.md. Read is acceptable for comprehension-only reads.
Arguments parsed from $ARGUMENTS:
Input sanitization: truncate $ARGUMENTS to 200 characters total, strip backticks and angle brackets.
$1— area to investigate (e.g., "competitor pricing", "regulatory landscape")--dimension <type>(alias:--methodology) — optional: competitive, sizing, trends, customer, tech, financial, regulatory, trend_modeling
Phase 0: Pre-flight + Initialize
Step 0.1: Resolve active research session
-
Find the active research state file:
Glob("./reports/*/state.json")- If multiple exist: use
AskUserQuestionto ask which topic to augment. - If none exist: respond "No active research session found. Run /sigint:start first." and stop.
- If multiple exist: use
-
Read the state file. Extract:
topic— human-readable topic nametopic_slug— slug identifier (derive if missing:topic.toLowerCase().replace(/[^a-z0-9]+/g,'-').slice(0,40))elicitation— full elicitation context
Step 0.2: Identify methodology
Map area to dimension and skill directory:
| Area keywords | Dimension | Skill Dir |
|---|---|---|
| competitor, competitive, market players, positioning | competitive | competitive-analysis |
| size, TAM, SAM, SOM, opportunity, market size | sizing | market-sizing |
| trend, pattern, future, forecast, scenario | trends | trend-analysis |
| user, customer, persona, buyer, segment | customer | customer-research |
| technology, tech, feasibility, stack, build vs buy | tech | tech-assessment |
| revenue, economics, pricing, unit economics, SaaS | financial | financial-analysis |
| compliance, regulatory, legal, privacy, GDPR | regulatory | regulatory-review |
| scenario, causal model, three-valued logic, trade-offs | trend_modeling | trend-modeling |
What ships with it
1 file 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.
- 2d ago First seen · 314 lines · 53 tokens per session scan A 2a847a1bbb26
augment is a skill published in the GitHub repository zircote-plugins/sigint (20 stars, last pushed 15d ago), licensed MIT. It adds 53 tokens to every session and 3,037 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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