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 DDS-Solutions/AI-TadPole-OS --skill mission-analystgit clone --depth 1 https://github.com/DDS-Solutions/AI-TadPole-OSWrote 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/dds-solutions/ai-tadpole-os/mission-analyst)<a href="https://agentmods.dev/skills/dds-solutions/ai-tadpole-os/mission-analyst"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/mission-analyst/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/dds-solutions/ai-tadpole-os/mission-analyst"><img src="https://agentmods.dev/badge/skills/dds-solutions/ai-tadpole-os/mission-analyst.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 | $0.00048 | $0.01136 |
| Opus 5.5 | $0.00019 | $0.00454 |
| Sonnet 5.5 | $0.00010 | $0.00227 |
| Haiku 4.5 | $0.00005 | $0.00114 |
Grade A, and why
mission-analyst 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 12d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
[!IMPORTANT] AI Context & Knowledge Heritage
- Subsystem: Agent Skills Registry / mission-analyst
- Architecture:
@docs ARCHITECTURE:Documentation- Failure Path: Information drift, legacy terminology, or undocumented architectural regressions.
- Observability: Traceability via
execution/parity_guard.py([SKILL])
🐸 Agent 99: Sovereign Mission Analyst
Purpose: Transform ephemeral mission telemetry and swarm execution logs into permanent, actionable institutional wisdom stored in
directives/LONG_TERM_MEMORY.md.
Workflow Bindings: Integrated into/codifyand/report.
🎯 Progressive Disclosure & L3 Reference Index
Read the core Sovereign debrief protocols below; consult deep L3 Reference Guides on demand:
| L3 Reference Guide | Purpose | Trigger / When to Load |
|---|---|---|
references/mission_debrief_protocols.md |
Wisdom taxonomy, signal filtering matrix, and memory entry schemas | Analyzing mission traces & structuring entries |
scripts/debrief_analyzer.py |
Automated wisdom entry validator and log lineage scanner | Validating wisdom bullets & analyzing log outputs |
execution/debrief_mission.py |
Workspace script for committing debrief insights to long-term memory | Committing validated mission findings to disk |
🔄 1. The 4-Phase Operational Protocol
[1. INGESTION] ───> [2. FILTERING] ───> [3. SYNTHESIS] ───> [4. PERSISTENCE]
Read raw mission Isolate signal Formulate concise Commit to LONG_TERM_MEMORY
execution logs from chatter actionable bullets via debrief_mission.py
- Ingestion: Read the complete log lineage of the completed mission or session.
- Filtering: Separate ephemeral transactional chatter (e.g. standard file reads, HTTP 200s) from architectural wisdom (e.g. locking bugs, provider parameter quirks, type bridges).
- Synthesis: Compress findings into 1–3 dense, technical bullets following the mandatory schema:
- **<Category>**: <Actionable Insight>. (Verification: <Mission/Benchmark/Test Witness>). - Persistence: Commit entries using
python execution/debrief_mission.py --commit.
What ships with it
2 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.
- 12d ago Changed · +45 lines · +19 tokens per session 2fff793f434a
- 1mo ago First seen · 37 lines · 29 tokens per session scan A 97160be4f0e7
mission-analyst is a skill published in the GitHub repository DDS-Solutions/AI-TadPole-OS (8 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 1,136 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.
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