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 skills/cass-2003/local-workflow-skill/process-mappernpx skills add cass-2003/local-workflow-skill --skill process-mappergit clone --depth 1 https://github.com/cass-2003/local-workflow-skillWhat 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 | $0.00176 | $0.02071 |
| Opus 5 | $0.00088 | $0.01035 |
| Sonnet 5 | $0.00035 | $0.00414 |
| Haiku 4.5 | $0.00018 | $0.00207 |
Grade A, and why
process-mapper 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
process-mapper
BPMN-style business process documentation, bottleneck detection, and cycle-time analysis for internal-operations leaders.
Purpose
Internal-operations work suffers from three recurring failure modes:
- Implicit process — the steps exist only in tribal knowledge, so handoffs drop and onboarding takes weeks.
- Invisible waiting — most of the elapsed time on any business process is queue / wait / approval time, not actual work; teams optimize the wrong stage.
- Local optimization — Goldratt's Theory of Constraints is ignored; resources are added to non-constraint stages, gaining nothing.
This skill produces a documented process map, identifies where work waits, and points the constraint out by name with deterministic logic — not LLM intuition.
When to use
- Documenting a new business process (procurement intake, vendor onboarding, employee onboarding, incident handoff, expense reimbursement, customer onboarding, claims adjudication).
- An existing process is "too slow" but nobody can name the bottleneck.
- Cycle time is being measured but value-add ratio is not — so the team can't tell whether the process is healthy or waste-heavy.
- Cross-functional handoffs are dropping work and root cause is unclear.
Workflow
Five-step deterministic flow:
- Intake. Capture the process as a JSON file with one entry per stage:
name,owner,type(value-add|wait|rework),duration_minutes_p50,duration_minutes_p90. Useassets/process_template.mdand its JSON skeleton. - Map stages. Run
process_documenter.pyto produce an ASCII swim-lane diagram + a normalized JSON artifact. The swim-lane separates lanes by owner so cross-functional handoffs become visible. - Measure cycle time. Run
cycle_time_analyzer.pyto compute total P50, total P90, value-add ratio (VA%), and a Little's-Law throughput estimate. Verdict: VA% > 25% = HEALTHY, 10–25% = TYPICAL, < 10% = WASTE-HEAVY. - Detect bottlenecks. Run
bottleneck_detector.pywith the appropriate--profile(saas / services / manufacturing / healthcare). Output is a ranked list with severity (CRITICAL / HIGH / MEDIUM), root-cause hypothesis, and one recommended action per finding. - Recommend. Pair the bottleneck list with the cycle-time verdict; recommend a single constraint-focused intervention per Goldratt's "subordinate everything to the constraint" rule. Don't recommend optimization of a non-constraint stage.
What ships with it
7 files 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.
- assets/process_template.md 4.2 KB
- references/bottleneck_anti_patterns.md 7.6 KB
- references/bpmn_essentials.md 6.3 KB
- references/lean_six_sigma_canon.md 6.4 KB
- scripts/bottleneck_detector.py 11 KB runs code
- scripts/cycle_time_analyzer.py 8.4 KB runs code
- scripts/process_documenter.py 8.9 KB runs code
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 · 107 lines · 176 tokens per session scan A 49b397394fcd
process-mapper is a skill published in the GitHub repository cass-2003/local-workflow-skill (12 stars, last pushed 1mo ago), licensed MIT. It adds 176 tokens to every session and 2,071 once invoked, about $0.0009 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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