Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add fledgeling-co/fledgeling-plugins/plugin install warrantWrote 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/fledgeling-co/fledgeling-plugins/panel)<a href="https://agentmods.dev/skills/fledgeling-co/fledgeling-plugins/panel"><img src="https://agentmods.dev/badge/skills/fledgeling-co/fledgeling-plugins/panel/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/fledgeling-co/fledgeling-plugins/panel"><img src="https://agentmods.dev/badge/skills/fledgeling-co/fledgeling-plugins/panel.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 | $0.00101 | $0.01788 |
| Opus 5.5 | $0.00040 | $0.00715 |
| Sonnet 5.5 | $0.00020 | $0.00358 |
| Haiku 4.5 | $0.00010 | $0.00179 |
Grade A, and why
panel 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.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Panel — the verdict, and the evidence channel
The design problem here is not accuracy. It is that every artefact the verdict rests on is
reachable by the thing being judged. Frontier coding agents modify tests, overwrite timers and
monkey-patch evaluators to return success. On RE-Bench, a research-engineering benchmark, 30.4%
of runs exhibited reward hacking, and on some tasks every successful run did (C14). An audit of
a benchmark built specifically to be
trustworthy found 59.4% of the audited subset materially flawed and retired it (C15).
So the sequence below snapshots the evidence before anything judges it, and the verdict is void if its digest does not match.
Running as a Gemini model? Read gemini.md in this directory first, then follow this file with the overrides it names. It puts denominators under the lane set, the captures and the verdict's five ledger fields, keeps relative qualifiers out of both the find and filter prompts, and reads model ids out of lanes.toml rather than from memory. Other models skip it.
Procedure
-
Snapshot first, judge second.
# Resolve ONCE in the conductor and pass the absolute path down to every agent. # A spawned agent does not reliably inherit CLAUDE_PLUGIN_ROOT, so a relative # `scripts/...` path resolves against the agent's own cwd and dies. W=<abs path to warrant>/scripts python3 "$W/snapshot_evidence.py" --root <repo> --diff <patch> --tests <dir> --captures <dir>Prints a content-addressed digest over sorted paths and contents, writes the snapshot read-only, and that digest travels with every verdict. A verdict whose digest does not match its snapshot is void rather than suspect.
This is the most-failed invocation in the toolkit, and the flags above are not the problem. Measured 2026-08-26: 19 of 46 real
snapshot_evidence.pycalls produced no snapshot, across 30 different agents, on two mistakes — an argparseusage:banner from an invented flag, and a script path that does not exist from the agent's working directory. Thirty agents meeting the same CLI for the first time made the same error, which is a documentation defect rather than thirty careless agents. The only flags that exist are--root --diff --tests --captures --include --json --now --selftest; there are no others, and--out,--dirand--evidenceare the three most commonly invented.
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.
- 1mo ago First seen · 134 lines · 101 tokens per session scan A f1f29586e294
panel is a skill published in the GitHub repository fledgeling-co/fledgeling-plugins (2 stars, last pushed 4d ago), licensed MIT. It adds 101 tokens to every session and 1,788 once invoked, about $0.0004 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 2026-09-04.
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