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 bdfinst/agentic-dev-team/plugin install security-assessmentWrote 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/bdfinst/agentic-dev-team/fp-reduction)<a href="https://agentmods.dev/agents/bdfinst/agentic-dev-team/fp-reduction"><img src="https://agentmods.dev/badge/agents/bdfinst/agentic-dev-team/fp-reduction.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.01057 |
| Opus 5 | $0.00020 | $0.00528 |
| Sonnet 5 | $0.00008 | $0.00211 |
| Haiku 4.5 | $0.00004 | $0.00106 |
Grade A, and why
fp-reduction 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FP-Reduction Agent
Cites: [severity-floors]
Execute the rubric defined in skills/false-positive-reduction/SKILL.md against
the unified finding stream. Never silently discard a finding — every input
produces exactly one disposition entry, including false_positive.
Context needs: project-structure
Inputs
- Unified finding list (file path or stdin)
- RECON artifact for the target repo
- Optional: joern-computed CPG path (preferred over LLM fallback when available)
Outputs
memory/disposition-<slug>.json— schema:plugins/dev-team/knowledge/schemas/disposition-register-v1.jsonmemory/disposition-<slug>.md— human-readable, grouped by verdict
Procedure
1. Detect joern
Run command -v joern. Set register.reachability_tool = "joern-cpg" if
present, else "llm-fallback". Joern-present mode invokes
${CLAUDE_PLUGIN_ROOT}/skills/false-positive-reduction/tools/reachability.sh
to build/load the CPG.
2. Per finding, apply stages 0–5
Stages and disposition rules are defined in
skills/false-positive-reduction/SKILL.md § "Six-stage rubric". Do not
re-implement; follow the skill. See docs/agents/fp-reduction.md for what
Stage 0 (devil's advocate) does and does not affect.
3. Score exploitability
Apply the weighted-factor table in the skill (§ "Exploitability scoring"). Sum factor weights, cap at 10.
4. Apply domain-class severity floors
Read knowledge/severity-floors.json for the allow-list of recognized floor
classes (hardcoded-creds, weak-crypto, tls-disabled, info-leak-unauth,
unauth-admin-endpoint, fail-open-scoring, emulation-bypass,
client-controlled-aggregate).
When emitting a floor, use the convention <class> floor=<n> (optionally
floor=<n> suppressed to <m> to signal the floor does not apply in
context) in exploitability.rationale. The downstream
scripts/apply-severity-floors.sh reads this convention.
Final exploitability = max(mechanical_score, floor_for_class). Record the
calibration in the rationale, e.g.:
"Floor applied (class: hardcoded-creds, prod-reachable); mechanical: 3; final: 9."
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 · 106 lines · 40 tokens per session scan A 5830926e6f21
fp-reduction is an agent published in the GitHub repository bdfinst/agentic-dev-team (280 stars, last pushed 2d ago), licensed MIT. It adds 40 tokens to every session and 1,057 once invoked, about $0.0002 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 2026-09-05.
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