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 responsible-genai-hackweek/nicer-nisar --skill nisar-data-accessgit clone --depth 1 https://github.com/responsible-genai-hackweek/nicer-nisarWrote 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/responsible-genai-hackweek/nicer-nisar/nisar-data-access)<a href="https://agentmods.dev/skills/responsible-genai-hackweek/nicer-nisar/nisar-data-access"><img src="https://agentmods.dev/badge/skills/responsible-genai-hackweek/nicer-nisar/nisar-data-access/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/responsible-genai-hackweek/nicer-nisar/nisar-data-access"><img src="https://agentmods.dev/badge/skills/responsible-genai-hackweek/nicer-nisar/nisar-data-access.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 | $0.00113 | $0.07596 |
| Opus 5 | $0.00056 | $0.03798 |
| Sonnet 5 | $0.00023 | $0.01519 |
| Haiku 4.5 | $0.00011 | $0.00760 |
Grade D, and why
nisar-data-access scanned grade D with 3 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 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
`~/.netrc`, `chmod 600`. Both libraries and GDAL pick it up automatically. Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
`~/.netrc`, `chmod 600`. Both libraries and GDAL pick it up automatically. Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import json, urllib.request, urllib.parse How it starts
The opening of the file, as written. The whole thing — 548 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NISAR data access
Verified August 2026. The archive is actively changing — a full validated reprocessing of the L0–L3 backlog is targeted for Q4 2026. Re-verify anything a decision depends on.
Every claim below is tagged:
- VERIFIED — checked against the live archive or a real file; method stated.
- INFERRED — consistent pattern across real granules, not read from a spec. Usable, but confirm before building on it.
- UNVERIFIED — from documentation only, not seen in a real granule.
Do not promote a tag without doing the check.
Which product
| Need | Product | Why |
|---|---|---|
| Geocoded interferometric phase + coherence, no DIY processing | L2 GUNW | Ready-made: unwrapped phase, coherence, connected components. |
| Same content but you pick the pair dates | L2 GSLC | Geocoded complex; form interferograms at any baseline. Costs you interferogram formation + unwrapping; coregistration is done. |
| Per-date backscatter (σ⁰/γ⁰) | L2 GCOV | GUNW has no amplitude layer. Only way to get backscatter. |
| Soil moisture covariate | L3 SME2 | 200 m. Coarse, but soil moisture is a major non-snow phase contributor. |
Ruled out: RUNW is GUNW in radar geometry (you geocode it yourself). ROFF/GOFF are speckle-tracking offsets for metre-scale displacement — wrong signal for most applications. RSLC/RIFG are earlier in the chain.
All L-band products come from ASF DAAC. S-band is ISRO-only via Bhoonidhi
(bhoonidhi.nrsc.gov.in/NISAR/) and will never appear in CMR/earthaccess.
Searching
CMR is public — no auth, no login, no account. This is the cheapest way to answer availability questions, and it is how the entire inventory below was built.
import json, urllib.request, urllib.parse
q = {
'short_name': 'NISAR_L2_GUNW_PROVISIONAL_V1',
'bounding_box': '-115.73524,43.9072,-115.63549,43.98398', # W,S,E,N
'temporal': '2025-11-01T00:00:00Z,2026-04-30T23:59:59Z',
'page_size': '2000',
}
url = 'https://cmr.earthdata.nasa.gov/search/granules.umm_json?' + urllib.parse.urlencode(q)
with urllib.request.urlopen(url, timeout=180) as r:
items = json.load(r)['items']
for it in items:
umm = it['umm']
gid = umm['GranuleUR']
attrs = {a['Name']: a['Values'] for a in umm.get('AdditionalAttributes', [])}
direction = attrs.get('ASCENDING_DESCENDING', [None])[0]
poly = umm['SpatialExtent']['HorizontalSpatialDomain']['Geometry']['GPolygons'][0]
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 · 548 lines · 113 tokens per session scan D 06c514f9a356
nisar-data-access is a skill published in the GitHub repository responsible-genai-hackweek/nicer-nisar (2 stars, last pushed 13d ago), licensed BSD-3-Clause. It adds 113 tokens to every session and 7,596 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it D with 3 findings (asks for root, reaches for credential files, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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