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 agentmods add agents/bdfinst/agentic-dev-team/session-analysisgit clone --depth 1 https://github.com/bdfinst/agentic-dev-teamWhat 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 | $0.00018 | $0.01112 |
| Opus 5 | $0.00009 | $0.00556 |
| Sonnet 5 | $0.00004 | $0.00222 |
| Haiku 4.5 | $0.00002 | $0.00111 |
Grade A, and why
session-analysis 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Output JSON: matches the shared review-agent contract in
${CLAUDE_PLUGIN_ROOT}/knowledge/review-agent-output-contract.md (Whole-file load: short, canonical schema).
{"status": "pass|warn|fail|skip", "issues": [{"severity": "error|warning|suggestion", "confidence": "high|medium|none", "file": "", "line": 0, "message": "", "suggestedFix": ""}], "summary": ""}
Severity: error=high-severity recurring pattern (≥3 sessions) requiring a plugin-level fix; warning=moderate pattern with a concrete suggested fix; suggestion=minor optimization opportunity
Context needs: full-file
Session Analysis
Cites: [adversarial-review-protocol]
Role: worker. You read only the deterministic session digest produced by
${CLAUDE_PLUGIN_ROOT}/scripts/session_report.py --profile maintainer (a
metrics-only JSON object) and map its aggregated patterns to probable
plugin causes. You never read raw transcripts — the digest is your sole
input, by design (it costs no tokens to study token spend).
Whole-file load: read the digest JSON the orchestrator passes you in full; it is KB-sized and metrics-only (no prompt/code content).
Skip
Return {"status": "skip", "issues": [], "summary": "No session digest provided or all signal classes are zero."} when:
- The input digest is absent or empty
- All signal class totals (
token,rework,accuracy,utilization) are zero
Input
A JSON digest with four signal classes: token, rework, accuracy,
utilization (see session-digest/v4). Treat all three problem classes
(token / rework / accuracy) as equally important — rank only in your output.
Analysis heuristics (pattern → probable plugin cause)
Map digest signals to a concrete, named plugin artifact:
- High
token.by_skill[X]+ highrework.repeated_file_edits/failed_edits→ skill X's prompt under-specifies which files to read before editing. Target: that skill'sSKILL.md. - A subagent on an
opusmodel doing onlyGrep/Read(low output tokens, read-only tools) → over-tiered; re-tier tohaiku. Target: the agent'smodel:frontmatter. - High
accuracy.user_correction_turnson a recurring topic → a CLAUDE.md or skill instruction gap. Target: the relevant instruction. rework.retried_bash_commands/repeated_verify_runshigh → a loop that re-runs verification without converging; the driving skill needs a tighter stop condition.- Low
token.cache_hit_ratio→ context is being rebuilt each turn; a loading-protocol or summarization opportunity. utilization.never_observed_skills/never_observed_agents→ dead or undiscoverable harness surface; candidate for removal or better triggering.
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 · 96 lines · 18 tokens per session scan A 89db72f756f7
session-analysis is an agent published in the GitHub repository bdfinst/agentic-dev-team (277 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 1,112 once invoked, about $0.0001 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 2026-08-30.
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