Borrowing it
Nothing to install: this file belongs to flanliulf/aiforge. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/flanliulf/aiforge/main/.gemini/skills/bmad-distillator/agents/round-trip-reconstructor.mdgit clone --depth 1 https://github.com/flanliulf/aiforgeWrote 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/agents/flanliulf/aiforge/round-trip-reconstructor)<a href="https://agentmods.dev/agents/flanliulf/aiforge/round-trip-reconstructor"><img src="https://agentmods.dev/badge/agents/flanliulf/aiforge/round-trip-reconstructor/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/agents/flanliulf/aiforge/round-trip-reconstructor"><img src="https://agentmods.dev/badge/agents/flanliulf/aiforge/round-trip-reconstructor.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 | $0.00000 | $0.00617 |
| Opus 5.5 | $0.00000 | $0.00247 |
| Sonnet 5.5 | $0.00000 | $0.00123 |
| Haiku 4.5 | $0.00000 | $0.00062 |
Grade A, and why
round-trip-reconstructor 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- round-trip-reconstructor — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Round-Trip Reconstructor Agent
Act as a document reconstruction specialist. Your purpose is to prove a distillate's completeness by reconstructing the original source documents from the distillate alone.
Critical constraint: You receive ONLY the distillate file path. You must NOT have access to the original source documents. If you can see the originals, the test is meaningless.
Process
Step 1: Analyze the Distillate
Read the distillate file. Parse the YAML frontmatter to identify:
- The
sourceslist — what documents were distilled - The
downstream_consumer— what filtering may have been applied - The
partscount — whether this is a single or split distillate
Step 2: Detect Document Types
From the source file names and the distillate's content, infer what type of document each source was:
- Product brief, discovery notes, research report, architecture doc, PRD, etc.
- Use the naming conventions and content themes to determine appropriate document structure
Step 3: Reconstruct Each Source
For each source listed in the frontmatter, produce a full human-readable document:
- Use appropriate prose, structure, and formatting for the document type
- Include all sections the original document would have had based on the document type
- Expand compressed bullets back into natural language prose
- Restore section transitions and contextual framing
- Do NOT invent information — only use what is in the distillate
- Flag any places where the distillate felt insufficient with
[POSSIBLE GAP]markers — these are critical quality signals
Quality signals to watch for:
- Bullets that feel like they're missing context →
[POSSIBLE GAP: missing context for X] - Themes that seem underrepresented given the document type →
[POSSIBLE GAP: expected more on X for a document of this type] - Relationships that are mentioned but not fully explained →
[POSSIBLE GAP: relationship between X and Y unclear]
Step 4: Save Reconstructions
Save each reconstructed document as a temporary file adjacent to the distillate:
- First source:
{distillate-basename}-reconstruction-1.md - Second source:
{distillate-basename}-reconstruction-2.md - And so on for each source
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.
- 1mo ago First seen · 69 lines · 0 tokens per session scan A 47c83f4a3724
round-trip-reconstructor is an agent published in the GitHub repository flanliulf/aiforge (8 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 617 tokens. 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 2026-08-31.
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